The Real Python Podcast

Overcoming Testing Obstacles With Python's Mock Object Library

Fri, 27 Feb 2026 12:00:00 +0000

Do you have complex logic and unpredictable dependencies that make it hard to write reliable tests? How can you use Python’s mock object library to improve your tests? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher shares details about his recent Real Python video course, “Improving Your Tests With the Python Mock Object Library.” He describes how mocking in Python with unittest.mock allows you to simulate complex logic or unpredictable dependencies, such as responses from external services. He covers how the Mock class can imitate real objects, and the patch() function lets you temporarily substitute mocks for real objects in your tests.

We also share other articles and projects from the Python community, including a collection of recent releases, using open source AI at Wagtail, updates from the inaugural PyPI Support Specialist, a lightweight OS for microcontrollers in MicroPythonOS, why match-case is not necessarily switch-case for Python, thinking about time in programming, a TUI-based presentation tool for the terminal, and a tool to check Django projects for dead code.

This episode is sponsored by AgentField.

Course Spotlight: Improving Your Tests With the Python Mock Object Library

Master Python testing with unittest.mock. Create mock objects to tame complex logic and unpredictable dependencies.

Topics:

00:00:00 – Introduction
00:02:53 – Python 3.14.3 and 3.13.12 Released
00:03:15 – Django Security Releases Issued: 6.0.2, 5.2.11, and 4.2.28
00:03:36 – Open Source AI We Use to Work on Wagtail
00:07:27 – Dispatch From the Inaugural PyPI Support Specialist
00:09:01 – MicroPythonOS Graphical Operating System
00:11:27 – Sponsor: AgentField
00:12:21 – Improving Your Tests With the Python Mock Object Library
00:17:30 – Need Switch-Case in Python? It’s Not Match-Case!
00:26:03 – Video Course Spotlight
00:27:38 – How to Think About Time in Programming
00:31:58 – Prezo: A TUI-based Presentation Tool for the Terminal
00:34:52 – django-deadcode: Tracks URLs, Templates, and Django Views
00:38:09 – Thanks and goodbye

News:

Show Links:

Open Source AI We Use to Work on Wagtail – One of the core maintainers at Wagtail CMS shares what open source models have been working best for the project so far.
Dispatch From the Inaugural PyPI Support Specialist – Maria Ashna (Thespi-Brain on GitHub) is the inaugural PyPI Support Specialist and she’s written up how the first year went.
MicroPythonOS Graphical Operating System – MicroPythonOS lightweight OS for microcontrollers targets applications with graphical user interfaces with a look similar to Android/iOS.
Improving Your Tests With the Python Mock Object Library – Master Python testing with unittest.mock. Create mock objects to tame complex logic and unpredictable dependencies.
Need Switch-Case in Python? It’s Not Match-Case! – Python’s match-case is not a switch-case statement. If you need switch-case, you can often use a dictionary instead.
How to Think About Time in Programming – Time is a complex thing to code. This article is a very deep dive, covering absolute measurement, civil time, modern time keeping, the mess that are timezones, and much more.

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Exploring MCP Apps & Adding Interactive UIs to Clients

Fri, 20 Feb 2026 12:00:00 +0000

How can you move your MCP tools beyond plain text? How do you add interactive UI components directly inside chat conversations? This week on the show, Den Delimarsky from Anthropic joins us to discuss MCP Apps and interactive UIs in MCP.

Den is a member of the MCP Steering Committee and a Core Maintainer focusing on auth and security. He explains how MCP acts as a universal bridge, providing AI models with the real-time context they need. He shares insights on working with MCP Apps and moving beyond simple text to render web-based user interfaces directly in your chat window.

Den previously worked on GitHub Spec Kit, and we discuss taking advantage of spec-driven development and how to use “Skills” to remove the toil from your workflow. We also talk briefly about his podcast and his conversations about navigating careers in technology.

This episode is sponsored by AgentField.

Course Spotlight: Getting Started With Google Gemini CLI

Learn how to use Gemini CLI to bring Google’s AI-powered coding assistance into your terminal for faster code analysis, debugging, and fixes.

Topics:

00:00:00 – Introduction
00:02:17 – Den’s Background
00:02:53 – The Work Item Podcast
00:06:55 – Career Move to Anthropic
00:10:24 – What is Model Context Protocol (MCP)?
00:11:39 – MCP UI and MCP Apps
00:16:17 – Sponsor: AgentField
00:17:13 – Structuring MCP Tools
00:24:52 – Cloudflared & tunneling
00:29:59 – Reverse engineering with Ghidra MCP
00:35:43 – Connecting MCP tools
00:43:10 – Video Course Spotlight
00:44:23 – Spec-Driven Development & Context Management
00:57:23 – Leveraging Skills for Model Guidance
01:02:51 – What are you excited about in the world of Python?
01:03:42 – What do you want to learn next?
01:07:14 – How can people follow your work online?
01:07:48 – Thanks and goodbye

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Running Local LLMs With Ollama and Connecting With Python

Fri, 13 Feb 2026 12:00:00 +0000

Would you like to learn how to work with LLMs locally on your own computer? How do you integrate your Python projects with a local model? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We cover a recent Real Python step-by-step tutorial on installing local LLMs with Ollama and connecting them to Python. It begins by outlining the advantages this strategy offers, including reducing costs, improving privacy, and enabling offline-capable AI-powered apps. We talk through the steps of setting things up, generating text and code, and calling tools.

We also share other articles and projects from the Python community, including the 2026 Python Developers Survey, creating callable instances with Python’s .__call__(), creating maps and projections with GeoPandas, ending 15 years of subprocess polling, discussing backseat software, a retry library that classifies errors, and a peer-to-peer encrypted CLI chat project.

This episode is sponsored by Honeybadger.

Course Spotlight: Tips for Using the AI Coding Editor Cursor

Learn Cursor fast: Use AI-powered coding with agents, project-aware chat, and inline edits to supercharge your VS Code workflow.

Topics:

00:00:00 – Introduction
00:02:37 – Take the Python Developers Survey 2026
00:03:07 – How to Integrate Local LLMs With Ollama and Python
00:08:15 – Sponsor: Honeybadger
00:09:01 – Create Callable Instances With Python’s .__call__()
00:12:13 – GeoPandas Basics: Maps, Projections, and Spatial Joins
00:16:03 – Ending 15 Years of subprocess Polling
00:18:57 – Video Course Spotlight
00:20:23 – Backseat Software – Mike Swanson
00:39:06 – cmd-chat: Peer-to-Peer Encrypted CLI Chat
00:41:58 – redress: A Retry Library That Classifies Errors
00:43:56 – Thanks and goodbye

News:

Topics:

How to Integrate Local LLMs With Ollama and Python – Learn how to integrate your Python projects with local models (LLMs) using Ollama for enhanced privacy and cost efficiency.
Create Callable Instances With Python’s .__call__() – Learn about Python callables, including what “callable” means, how to use dunder call, and how to build callable objects with step-by-step examples.
GeoPandas Basics: Maps, Projections, and Spatial Joins – Dive into GeoPandas with this tutorial covering data loading, mapping, CRS concepts, projections, and spatial joins for intuitive analysis.
Ending 15 Years of subprocess Polling – Python’s standard library subprocess module relies on busy-loop polling to determine whether a process has completed yet. Modern operating systems have callback mechanisms to do this, and Python 3.15 will now take advantage of these.

Discussion:

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Improving Your GitHub Developer Experience

Fri, 06 Feb 2026 12:00:00 +0000

What are ways to improve how you’re using GitHub? How can you collaborate more effectively and improve your technical writing? This week on the show, Adam Johnson is back to talk about his new book, “Boost Your GitHub DX: Tame the Octocat and Elevate Your Productivity”.

Adam has written a series of books about improving developer experience (DX). In this episode, we dig into his newest book, which focuses on GitHub and how to get the most out of its features—from settings and keyboard shortcuts to hidden tools, CLI commands, and the command palette.

Adam also shares insights on the best ways to communicate on the platform. We discuss the nuances of GitHub-Flavored Markdown (GFM), best practices for effective communication in open source, the importance of maintaining civility in issue reports, and why he included a glossary of acronyms to help developers decode common shorthand like LGTM and FTFY.

This episode is sponsored by Honeybadger.

Course Spotlight: Introduction to Git and GitHub for Python Developers

What is Git, what is GitHub, and what’s the difference? Learn the basics of Git and GitHub from the perspective of a Pythonista in this step-by-step video course.

Topics:

00:00:00 – Introduction
00:02:20 – Why the focus on developer experience?
00:03:41 – Process of writing the book
00:06:26 – Filling in the gaps of knowledge
00:11:52 – GitHub-Flavored Markdown
00:16:00 – Sponsor: Honeybadger
00:16:47 – Acronym glossary
00:25:18 – GitHub command palette
00:28:35 – What questions did you want to answer?
00:29:42 – Whether to cover Copilot or not
00:36:14 – Video Course Spotlight
00:37:50 – Advice on working with coding agents
00:40:46 – Defining the scope
00:48:07 – GitHub pages and codespaces
00:50:46 – Finding the hidden features
00:51:53 – Data-oriented Django series
00:53:59 – How to find the book
00:54:51 – What are you excited about in the world of Python?
00:57:27 – What do you want to learn next?
00:58:00 – How can people follow your work online?
00:58:22 – Thanks and goodbye

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Testing Python Code for Scalability & What's New in pandas 3.0

Fri, 30 Jan 2026 12:00:00 +0000

How do you create automated tests to check your code for degraded performance as data sizes increase? What are the new features in pandas 3.0? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher digs into an article about building tests to make sure your software is fast, or at least doesn’t get slower as it scales. The piece focuses on testing Big-O scaling and its implications for algorithms.

We also discuss another article covering the top features in pandas 3.0, including the new dedicated string dtype, a cleaner way to perform column-based operations, and more predictable default copying behavior with Copy-on-Write.

We share several other articles and projects from the Python community, including a collection of recent releases and PEPs, a profiler for targeting individual functions, a quiz to test your Django knowledge, when to use each of the eight versions of UUID, the hard-to-swallow truths about being a software engineer, an offline reverse geocoding library, and a library for auto-generating CLIs from any Python object.

Our live Python cohorts start February 2, and we’re down to the last few seats. There are two tracks: Python for Beginners or Intermediate Deep Dive. Eight weeks of live instruction, small groups, and real accountability. Grab your seat at realpython.com/live.

This episode is sponsored by Honeybadger.

Course Spotlight: Intro to Object-Oriented Programming (OOP) in Python

Learn Python OOP fundamentals fast: master classes, objects, and constructors with hands-on lessons in this beginner-friendly video course.

Topics:

00:00:00 – Introduction
00:03:28 – Python 3.15.0 Alpha 4 Released
00:03:50 – Django Bugfix Release: 5.2.10, 6.0.1
00:04:22 – PEP 819: JSON Package Metadata
00:04:41 – PEP 820: PySlot: Unified Slot System for the C API
00:04:59 – PEP 822: Dedented Multiline String (d-String)
00:06:04 – What’s New in pandas 3.0
00:13:11 – pandas 3.0.0 documentation
00:13:44 – Sponsor: Honeybadger
00:14:30 – Unit Testing Your Code’s Performance
00:17:51 – Introducing tprof, a Targeting Profiler
00:23:03 – Video Course Spotlight
00:24:31 – Django Quiz 2025
00:24:56 – 8 Versions of UUID and When to Use Them
00:29:17 – 10 hard-to-swallow truths they won’t tell you about software engineer job
00:44:02 – gazetteer: Offline Reverse Geocoding Library
00:46:13 – python-fire: A library for automatically generating command-line interfaces
00:47:40 – Thanks and goodbye

News:

Show Links:

What’s New in pandas 3.0 – Learn what’s new in pandas 3.0: pd.col expressions for cleaner code, Copy-on-Write for predictable behavior, and PyArrow-backed strings for 5-10x faster operations.
What’s new in 3.0.0 (January 21, 2026) — pandas 3.0.0 documentation
Unit Testing Your Code’s Performance – Testing your code is important, not just for correctness but also for performance. One approach is to check performance degradation as data sizes go up, also known as Big-O scaling.
Introducing tprof, a Targeting Profiler – Adam has written tprof, a targeting profiler for Python 3.12+. This article introduces you to the tool and why he wrote it.
Django Quiz 2025 – Last month, Adam held another quiz at the December edition of Django London. This is an annual tradition at the meetup, and now you can take it yourself or just skim the answers.
8 Versions of UUID and When to Use Them – RFC 9562 outlines the structure of Universally Unique Identifiers (UUIDs) and includes eight different versions. In this post, Nicole gives a quick intro to each kind so you don’t have to read the docs, and explains why you might choose each.
uuid — UUID objects according to RFC 9562 — Python 3.14.2 documentation

Discussion:

10 hard-to-swallow truths they won’t tell you about software engineer job

Projects:

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Continuing to Improve the Learning Experience at Real Python

Fri, 23 Jan 2026 12:00:00 +0000

If you haven’t visited the Real Python website lately, then it’s time to check out a great batch of updates on realpython.com! Dan Bader returns to the show this week to discuss improvements to the site and more ways to learn Python.

Dan details changes to the website, including our Python reference area. This tool provides a quick reference for both beginners and experienced developers looking for concise definitions and refreshers on Python’s features.

We discuss the expansion of our live courses to include a beginner course and an intermediate deep dive. Dan also shares how we’re growing the team and highlights the ongoing commitment to our editorial standards.

Spotlight: Python for Beginners: Code With Confidence

Learn the fundamentals of Python step-by-step in a friendly, interactive cohort. Build confidence writing code and understand the “why” behind Python’s core concepts.

Topics:

00:00:00 – Introduction
00:02:54 – Real Python Reference
00:08:31 – Integration with search
00:13:51 – Sponsor: Honeybadger
00:14:38 – Live courses
00:23:20 – Podcast visibility and interconnection
00:32:42 – Spotlight
00:33:35 – Editorial standards and goals for 2026
00:36:28 – Building the team
00:37:52 – Real Python for teams
00:40:56 – Feedback and listening to users
00:43:23 – Thanks and goodbye

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Considering Fast and Slow in Python Programming

Fri, 16 Jan 2026 12:00:00 +0000

How often have you heard about the speed of Python? What’s actually being measured, where are the bottlenecks—development time or run time—and which matters more for productivity? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article titled “The Uselessness of ‘Fast’ and ‘Slow’ in Programming.” It digs into how the different aspects of software performance span a wide range of orders of magnitude, and how developers can obsess over irrelevant performance details, often losing more time working in suboptimal environments than building what they need with tools they already know.

We also discuss an article about why uv is fast, which explains how most of its speed comes from engineering decisions rather than just being written in Rust.

We then share several other articles and projects from the Python community, including a roundup of 2025 year-end lists, an explanation of why Python’s deepcopy can be so slow, serving a website with FastAPI using Jinja2, Python numbers every programmer should know, a discussion of spec-driven development and whether waterfall is back, a tool to detect whether a PDF has a bad redaction, and a CLI for measuring HTTP request phases.

Course Spotlight: Jinja Templating

With Jinja, you can build rich templates that power the front end of your web applications. But you can also use Jinja without a web framework running in the background. Whenever you need to generate text files with dynamic content, Jinja provides a flexible and powerful solution.

Topics:

00:00:00 – Introduction
00:02:53 – Top Python Libraries of 2025
00:04:43 – 2025 Python Year in Review
00:04:58 – PyPI in 2025: A Year in Review
00:05:36 – Join in the PSF Year-End Fundraiser & Membership Drive!
00:06:10 – How uv Got So Fast
00:12:29 – Why Python’s deepcopy Can Be So Slow
00:17:03 – Serve a Website With FastAPI Using HTML and Jinja2
00:23:19 – The Uselessness of “Fast” and “Slow” in Programming
00:27:06 – Python Numbers Every Programmer Should Know
00:28:17 – Video Course Spotlight
00:29:43 – Spec-Driven Development: The Waterfall Strikes Back
00:49:21 – x-ray: A Tool to Detect Whether a PDF Has a Bad Redaction
00:52:20 – httptap: CLI Measuring HTTP Request Phases
00:54:19 – Thanks and goodbye

2025 Top List Roundup

Top Python Libraries of 2025 – Explore Tryolabs’ 11th annual Top Python Libraries roundup, featuring two curated Top 10 lists: one for General Use and one for AI/ML/Data tools.
2025 Python Year in Review – Talk Python interviews Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters, and the panel discusses what mattered for Python in 2025.
PyPI in 2025: A Year in Review – Dustin summarizes all the happenings with the Python Packaging Index in 2025, including 130,000 new projects and over 2.5 trillion requests served.
Join in the PSF Year-End Fundraiser & Membership Drive!

Topics:

How uv Got So Fast – uv’s speed comes from engineering decisions, not just Rust. Static metadata, dropping legacy formats, and standards that didn’t exist five years ago.
Why Python’s deepcopy Can Be So Slow – “Python’s copy.deepcopy() creates a fully independent clone of an object, traversing every nested element of the object graph.” That can be expensive. Learn what it’s doing and how you can sometimes avoid the cost.
Serve a Website With FastAPI Using HTML and Jinja2 – Use FastAPI to render Jinja2 templates and serve dynamic sites with HTML, CSS, and JavaScript, then add a color picker that copies hex codes.
The Uselessness of “Fast” and “Slow” in Programming – “One of the unique aspects of software is how it spans such a large number of orders of magnitude.” The huge difference makes the terms “fast” and “slow” arbitrary. Read on to discover how this affects our thinking as programmers and what mistakes it can cause.
Python Numbers Every Programmer Should Know – Ever wonder how much memory an empty list takes? How about how long it takes to add two integers in Python? This post contains loads of performance data for common Python operations.

Discussion:

Projects:

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Coding Python With Confidence: Beginners Live Course Participants

Fri, 09 Jan 2026 12:00:00 +0000

Are you looking for that solid foundation to begin your Python journey? Would the accountability of scheduled group classes help you get through the basics and start building something? This week, two members of the Python for Beginners live course discuss their experiences.

We speak with course instructor Stephen Gruppetta about building a course where the participants start using their knowledge as soon as possible. He describes how he’s evolved his teaching techniques over years of working with beginners. We explore the advantages of having a curated collection of written tutorials, video courses, and a forum for asking those nagging questions.

We also speak with students Louis and Andrew about their experiences learning Python through the course. They discuss learning how to apply their new skills, employing them in their careers, and building confidence to continue their Python learning journey.

Spotlight: Python for Beginners: Code With Confidence

Learn the fundamentals of Python step-by-step in a friendly, interactive cohort. Build confidence writing code and understand the “why” behind Python’s core concepts.

Topics:

00:00:00 – Introduction
00:01:40 – Instructor Stephen Gruppetta
00:02:42 – Designing the course
00:09:01 – Introducing mini-projects early
00:13:22 – How have the questions changed for Python beginners?
00:20:23 – Taking advantage of Real Python resources
00:24:07 – More courses for 2026
00:25:40 – Spotlight: Python for Beginners
00:26:39 – Python for Beginners participants
00:27:50 – Louis’ background in programming
00:30:46 – Andrew’s background in programming
00:37:43 – Starting to use the knowledge with mini-projects
00:42:52 – What were challenges with the language?
00:54:15 – Working on the larger final project
00:59:45 – What advantages did the cohort-style course provide?
01:03:56 – Are you ready for that blank page?
01:12:00 – How do you see yourself using these new skills?
01:17:39 – Thanks and goodbye

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PyCoder's Weekly 2025 Top Articles & Hidden Gems

Fri, 02 Jan 2026 12:00:00 +0000

PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2025. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and uncovering a few hidden gems from the pile.

We share the top links explored by PyCoder’s readers. We also dig into trends across all the articles and stories this year. We highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year.

We hope you enjoy this review! We look forward to bringing you an upcoming year full of great Python news, articles, topics, and projects.

Course Spotlight: Using Functional Programming in Python

Boost your Python skills with a quick dive into functional programming: what it is, how Python supports it, and why it matters.

Topics:

00:00:00 – Introduction
00:01:52 – Django 6.0 released
00:02:58 – The Inner Workings of Python Dataclasses Explained
00:03:40 – Going Beyond requirements.txt With pylock.toml and PEP 751
00:04:58 – Django vs. FastAPI, An Honest Comparison
00:05:46 – How to Use Loguru for Simpler Python Logging
00:06:47 – Narwhals: Unified DataFrame Functions
00:08:32 – Observations and statistics for the year of articles
00:13:23 – Data Validation Libraries for Polars (2025 Edition)
00:18:53 – Video Course Spotlight
00:20:25 – Create Temporary Files and Directories in Unittest
00:22:27 – Capture Stdout and Stderr in Unittest
00:24:59 – I don’t like NumPy
00:26:34 – Python performance myths and fairy tales
00:31:05 – Congratulations on making through 2025!

News:

Django 6.0 released - Django Weblog

Moving Towards Spec-Driven Development

Fri, 19 Dec 2025 12:00:00 +0000

What are the advantages of spec-driven development compared to vibe coding with an LLM? Are these recent trends a move toward declarative programming? This week on the show, Marc Brooker, VP and Distinguished Engineer at AWS, joins us to discuss specification-driven development and Kiro.

Marc describes the process of developing an application by writing specifications, which outline what a program should do and what needs it should meet. We dig into a bit of computer science history to explore the differences between declarative and imperative programming.

We also discuss Kiro, a new integrated development environment from Amazon, built around turning prompts into structured requirements. We examine the various types of documents used to specify tasks, requirements, design, and steering.

Real Python Resource Spotlight: Python Coding With AI - Learning Path

Explore tools and workflows for AI in Python: coding partners, prompt engineering, RAG, ChromaDB, FastAPI chatbots, and MCP integrations. Stay current and start today.

Topics:

00:00:00 – Introduction
00:02:41 – How did you get involved in open source?
00:07:23 – How would you describe spec-driven development?
00:10:49 – Balancing the desire to start coding with defining the project
00:13:06 – What does this documentation look like?
00:18:27 – Declarative vs imperative programming
00:24:13 – Infrastructure as part of the design
00:27:03 – Getting started with a small project
00:29:05 – Committing the spec files along with the code
00:31:08 – What is steering?
00:34:17 – How to get better at distilling specifications?
00:38:59 – What are anti-patterns in spec-driven development?
00:41:08 – Should you avoid third-party libraries?
00:43:16 – Real Python Resource Spotlight
00:44:39 – Getting started with Kiro
00:51:00 – Neuro-symbolic AI
00:55:41 – What are you excited about in the world of Python?
00:58:36 – What do you want to learn next?
01:00:18 – How can people follow your work online?
01:00:57 – Thanks and goodbye

Show Links:

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Exploring Quantum Computing & Python Frameworks

Fri, 05 Dec 2025 12:00:00 +0000

What are the recent advances in the field of quantum computing and high-performance computing? And what Python tools can you use to develop programs that run on quantum computers? This week on the show, Real Python author Negar Vahid discusses her tutorial, “Quantum Computing Basics With Qiskit.”

Negar digs into the fundamentals of quantum computers, describing qubits, superposition, entanglement, and interference. We discuss the concept of quantum advantage and the fields of exploration where quantum computing promises speed-ups over classical computers. She also shares tools for designing quantum circuits with Python.

Course Spotlight: Profiling Performance in Python

Learn to profile Python programs with built-in and popular third-party tools, and turn performance insights into faster code.

Topics:

00:00:00 – Introduction
00:01:31 – Writing for Real Python
00:02:37 – What drew you to quantum computing?
00:04:27 – What is quantum advantage?
00:07:10 – Quantum computing basics article
00:09:32 – Linear algebra
00:10:32 – What is a quantum computer?
00:14:52 – Superconducting devices
00:17:29 – Looking for ways to advance computing
00:19:17 – Superposition of qubits and entanglement
00:22:43 – Video Course Spotlight
00:24:27 – Potential areas of research
00:26:45 – IBM quantum computing & Qiskit
00:29:43 – Describing superposition as a spinning coin
00:30:41 – Other types of quantum computers
00:32:08 – Qiskit Global Summer School 2025
00:32:48 – Qiskit Advocate
00:33:49 – What do you see in the near future?
00:37:42 – What are the type of HPCs?
00:40:05 – Additional resources to learn more
00:43:32 – Answering to skeptics
00:47:51 – What are you excited about in the world of Python?
00:48:24 – What do you want to learn next?
00:49:04 – What’s the best way to follow your work online?
00:49:25 – Thanks and goodbye

Show Links:

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Building a FastAPI Application & Exploring Python Concurrency

Fri, 21 Nov 2025 12:00:00 +0000

What are the steps to get started building a FastAPI application? What are the different types of concurrency available in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent Real Python step-by-step tutorial about programming a FastAPI example application. You practice installing FastAPI, building your first endpoints, adding path and query parameters, and validating endpoints using Pydantic.

Christopher covers updates to his Real Python video course about concurrency in Python. The course digs into what concurrency means in Python and why you might want to incorporate it in your code. He describes the different methods and demonstrates how to approach coding using threading, asyncio, and multiprocessing.

We also share several other articles and projects from the Python community, including a news roundup, the PSF fundraiser campaign for 2025, where Python stores attributes, performance hacks for faster Python code, a project to transform functions into a web interface, and a Python disk-backed cache.

Course Spotlight: Python Descriptors

Learn what Python descriptors are, how the descriptor protocol works, and when descriptors are useful, with practical, hands-on examples.

Topics:

00:00:00 – Introduction
00:02:18 – Django Security Release
00:02:46 – Django Is Now a CVE Numbering Authority (CNA)
00:03:53 – An Annual Release Cycle for Django
00:04:12 – PEP 810: Explicit Lazy Imports (Accepted)
00:04:27 – PSF Board Office Hour Sessions for 2026
00:05:42 – PyCon US 2026: Call for Proposals Open
00:06:15 – PSF Fundraiser campaign for 2025
00:10:12 – A Close Look at a FastAPI Example Application
00:16:36 – Speed Up Python With Concurrency
00:21:08 – __dict__: Where Python Stores Attributes
00:25:59 – Video Course Spotlight
00:27:17 – 10 Smart Performance Hacks for Faster Python Code
00:29:56 – FuncToWeb: Transform Python Functions Into a Web Interface
00:32:48 – python-diskcache: Python Disk-Backed Cache
00:34:07 – Thanks and goodbye

News:

Show Links:

A Close Look at a FastAPI Example Application – Set up a FastAPI example app, add path and query parameters, and handle CRUD operations with Pydantic for clean, validated endpoints.
Speed Up Python With Concurrency – Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.
__dict__: Where Python Stores Attributes – Most Python objects store their attributes in a __dict__ dictionary. Modules and classes always use __dict__, but not everything does.
10 Smart Performance Hacks for Faster Python Code – Some practical optimization hacks, from data structures to built-in modules, that boost speed, reduce overhead, and keep your Python code clean.

Projects:

Additional Links:

Become a Member of the PSF - Python Software Foundation
FastAPI
Welcome to Pydantic
Quiz: A Close Look at a FastAPI Example Application – Practice FastAPI basics with path parameters, request bodies, async endpoints, and CORS. Build confidence to design and test simple Python web APIs.
Diskcache, more than caching - Bite code!

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Preparing Data Science Projects for Production

Fri, 14 Nov 2025 12:00:00 +0000

How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, “Production Ready Data Science.”

Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production.

Course Spotlight: Python Project Management With uv

Create and manage Python projects with uv, a blazing-fast package and project manager built in Rust. Learn setup, workflow, and best practices.

Topics:

00:00:00 – Introduction
00:01:27 – Recent article about top six visualization libraries
00:02:19 – How long have you been blogging?
00:03:55 – What do you cover in your book?
00:07:07 – Potential issues with notebooks
00:11:40 – Structuring data science projects
00:15:12 – Reproducibility and sharing notebooks
00:20:33 – Using Polars
00:26:03 – Advantages of marimo notebooks
00:34:21 – Video Course Spotlight
00:35:44 – Shipping a project in data science
00:42:10 – Advice on testing
00:49:50 – Creating importable parameter values
00:53:55 – Seeing the commit diff of a notebook
00:55:12 – What are you excited about in the world of Python?
00:56:04 – What do you want to learn next?
00:56:52 – What’s the best way to follow your work online?
00:58:28 – Thanks and goodbye

Show Links:

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Advice for Writing Maintainable Python Code

Fri, 07 Nov 2025 12:00:00 +0000

What are techniques for writing maintainable Python code? How do you make your Python more readable and easier to refactor? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article about writing code that is easy to maintain. We cover writing comments, creating meaningful names, avoiding magic numbers, and preparing code for your future self.

We also share several other articles and projects from the Python community, including release news, modifying the REPL, differences between Polars and pandas, generating realistic test data in Python, investigating quasars with Polars and marimo, creating simple meta tags for Django objects, and a GUI toolkit for grids of buttons.

Course Spotlight: Modern Python Linting With Ruff

Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular.

Topics:

00:00:00 – Introduction
00:01:53 – PyTorch 2.9 Release
00:02:38 – Django 6.0 Beta 1
00:03:05 – Handy Python REPL Modifications
00:11:06 – Polars vs pandas: What’s the Difference?
00:17:55 – Faker: Generate Realistic Test Data in Python
00:22:06 – Video Course Spotlight
00:23:35 – Investigating Quasars With Polars and marimo
00:27:37 – Writing Maintainable Code
00:49:48 – buttonpad: GUI Toolkit for Grids of Buttons
00:52:10 – django-snakeoil: Simple Meta Tags for Django Objects
00:54:07 – Thanks and goodbye

News:

Show Links:

Handy Python REPL Modifications – Trey uses the the Python REPL a lot. In this post he shows you his favorite customizations to make the REPL even better.
Polars vs pandas: What’s the Difference? – Discover the key differences in Polars vs pandas to help you choose the right Python library for faster, more efficient data analysis.
Faker: Generate Realistic Test Data in Python – If you want to generate test data with specific types (bool, float, text, integers) and realistic characteristics (names, addresses, colors, emails, phone numbers, locations), Faker can help you do that.
Investigating Quasars With Polars and marimo – Learn to visualize quasar redshift data by building an interactive marimo dashboard using Polars, pandas, and Matplotlib. You’ll practice retrieving, cleaning, and displaying data in your notebook. You’ll also build interactive UI components that live-update visualizations in the notebook.

Discussion:

Writing Maintainable Code – “Maintainable code can easily be the difference between long-lived, profitable software, and short-lived money pits.” Read on to see just what maintainable code is and how to achieve it.

Projects:

Additional Links:

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Michael Kennedy: Managing Your Own Python Infrastructure

Fri, 31 Oct 2025 12:00:00 +0000

How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, “Talk Python in Production.”

Michael runs multiple Python applications online, including a training site, blog, and two podcasts. While searching for the best solution for hosting his business, he documented his findings in a book. We talk about containerizing Python applications, generating static sites, preparing for traffic spikes, and avoiding cloud service lock-in.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

Topics:

00:00:00 – Introduction
00:01:28 – Welcome back!
00:03:05 – Is this your first book?
00:04:13 – A book that reads like a blog
00:06:15 – Incentives to keep you locked in
00:09:20 – Following the journey of the Talk Python sites
00:11:47 – Audio reader briefs
00:15:19 – Discussing Dev Ops as a topic
00:18:31 – Background of developing for the web
00:20:14 – Stack-Native vs Cloud-Native
00:24:40 – Using Quart web framework
00:25:50 – Embracing Docker
00:32:39 – Sharing a single powerful machine allows for individual peaks
00:37:04 – Video Course Spotlight
00:38:30 – Minimal cloud lock-in
00:40:04 – Using OrbStack for local builds and testing
00:42:07 – Coolify as a Docker host
00:47:14 – Moving away from Google analytics and a GDPR rant
00:50:43 – Diving deep into web tech of ngnix, SSL, and CDNs
00:54:33 – Talking about the prices for hosting
00:59:09 – Creating static sites
01:06:22 – Invitation to come back to discuss AI and agents
01:10:06 – What are you excited about in the world of Python?
01:16:19 – What do you want to learn next?
01:17:34 – What’s the best way to follow your work online?
01:19:40 – Thanks and goodbye

Links:

Michael’s Recommended Hosting Locations:

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Benchmarking Python 3.14 & Enabling Asyncio to Scale

Fri, 24 Oct 2025 12:00:00 +0000

How does Python 3.14 perform under a few hand-crafted benchmarks? Does the performance of asyncio scale on the free-threaded build? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

At the top of the show, we have a mountain of release news and Python Enhancement Proposals to cover. Then, we dig into a couple of articles covering the performance of Python 3.14. The first is a benchmarking comparison of the last several Python versions and their variations, including JIT and free-threaded mode. The second explores the changes in 3.14 that enable asyncio to scale on CPython’s free-threaded build.

We also share several other articles and projects from the Python community, including an introduction to NiceGUI, a free-threaded Python library compatibility checker, an exploration of what is “good taste” in software engineering, HTML templating with t‑strings, and a tool for testing Sphinx docs instantly in the browser.

Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs

In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read the Docs to automatically build and publish your code documentation.

Topics:

00:00:00 – Introduction
00:02:28 – Python 3.12.12, 3.11.14, 3.10.19 and 3.9.24 now available
00:03:08 – Python 3.13.9 is now available
00:03:26 – Python 3.15.0 alpha 1
00:04:02 – PEP 804: An External Dependency Registry and Name Mapping Mechanism
00:04:56 – PEP 806: Mixed Sync/Async Context Managers With Precise Async Marking
00:06:34 – PEP 807: Index Support for Trusted Publishing
00:07:21 – PEP 809: Stable ABI for the Future
00:08:10 – PEP 810: Explicit Lazy Imports
00:10:31 – Python lazy imports you can use today
00:10:48 – Lazy Imports Using wrapt
00:11:18 – Python 3.14 Is Here. How Fast Is It?
00:17:45 – Free-Threaded Python Library Compatibility Checker
00:19:54 – Scaling Asyncio on Free-Threaded Python
00:24:06 – Real Python 3.14 Resources
00:25:18 – Video Course Spotlight
00:26:31 – Intro to NiceGUI: Build Interactive Python Web Apps
00:30:22 – What Is “Good Taste” in Software Engineering?
00:40:52 – Try Sphinx Docs Instantly in Your Browser
00:43:11 – Introducing tdom: HTML Templating With t‑strings
00:46:21 – Thanks and goodbye

News:

Python Insider: Python 3.12.12, 3.11.14, 3.10.19 and 3.9.24 are now available!
Python Insider: Python 3.13.9 is now available!
Python Insider: Python 3.15.0 alpha 1
PEP 804: An External Dependency Registry and Name Mapping Mechanism (Added)
PEP 806: Mixed Sync/Async Context Managers With Precise Async Marking (Added)
PEP 807: Index Support for Trusted Publishing (Added)
PEP 809: Stable ABI for the Future (Added)
PEP 810: Explicit Lazy Imports (Added)
Python lazy imports you can use today - PythonTest
Lazy Imports Using wrapt – PEP 810 proposes adding explicit lazy imports to Python, but you can already achieve this with third-party libraries. This post shows you how using wrapt.

Show Links:

Python 3.14 Is Here. How Fast Is It? – A comprehensive deep-dive comparison of performance figures between Python versions and variations, including the JIT and free-threaded mode.
Free-Threaded Python Library Compatibility Checker – A heat map and table summarizing Python free-threaded compatibility in a variety of common Python packages.
Python 3.14: 3 asyncio Changes – asyncio changes are often overlooked. In the latest 3.14 release, there are three new asyncio features and changes.
Scaling Asyncio on Free-Threaded Python – A recap of the work done in Python 3.14 to enable asyncio to scale on the free-threaded build of CPython.
Intro to NiceGUI: Build Interactive Python Web Apps – Use NiceGUI to turn Python scripts into interactive web apps without touching HTML, CSS, or JavaScript.

Discussion:

What Is “Good Taste” in Software Engineering? – This opinion piece discusses the difference between skill and taste when writing software. What counts as “clean code” for one person may not be the same for another.

Projects:

Try Sphinx Docs Instantly in Your Browser
Introducing tdom: HTML Templating With t‑strings – Python 3.14 introduces t-strings, and this article showcases tdom, a new HTML DOM toolkit that takes advantage of them to produce safer output.

Additional Links:

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Evolving Teaching Python in the Classroom

Fri, 17 Oct 2025 12:00:00 +0000

How is teaching young students Python changing with the advent of LLMs? This week on the show, Kelly Schuster-Paredes from the Teaching Python podcast joins us to discuss coding and AI in the classroom.

Kelly shares her current thoughts on teaching Python to young students. She stresses that the earliest classes still need to cover the fundamentals. We also discuss how coding instruction is evolving toward reading and reviewing code more than writing it.

Course Spotlight: Building a Python GUI Application With Tkinter

In this video course, you’ll learn the basics of GUI programming with Tkinter, the de facto Python GUI framework. Master GUI programming concepts such as widgets, geometry managers, and event handlers. Then, put it all together by building two applications: a temperature converter and a text editor.

Topics:

00:00:00 – Introduction
00:01:28 – Starting to use AI in the classroom
00:05:17 – How did students react to using NLP and NLTK?
00:06:40 – Teaching Python beginners now
00:12:31 – Code editor changes with LLM features
00:15:25 – Less in-classroom teaching
00:16:22 – What sparks interest in coding for students?
00:21:14 – Video Course Spotlight
00:22:44 – Exploratory robotics
00:25:56 – Do you need to struggle to learn?
00:32:52 – Working through frustration and the tendency not to read
00:36:37 – Mixed feelings on the changes to teaching
00:41:19 – Teaching through reading and code reviewing
00:45:33 – Lower-level classes are still about fundamentals
00:47:47 – Other areas of teaching using AI tools
00:51:29 – Improving prompting skills
00:54:22 – Using these tools to organize creativity
00:54:53 – What AI tools are working for you?
00:57:13 – Sharing knowledge and techniques with other teachers
01:00:02 – General advice for teachers
01:02:03 – What are you excited about in the world of Python?
01:02:52 – What do you want to learn next?
01:03:46 – How can people follow your work online?
01:04:10 – Thanks and goodbye

Show Links:

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Python 3.14: Exploring the New Features

Fri, 10 Oct 2025 12:00:00 +0000

Python 3.14 is here! Christopher Trudeau returns to discuss the new version with Real Python team member Bartosz Zaczyński. This year, Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.14: Cool New Features for You to Try.” Christopher’s video course, “What’s New in Python 3.14”, covers the topics from the article and shows the new features in action.

Christopher and Bartosz dug into the new release to create code examples showcasing the new features for the tutorial and course. We look at the enhanced and more colorful REPL experience, better error messages, safer hooks for live debugging, and deferred annotation evaluation. We also discuss template strings, Zstandard compression, and multiple performance improvements.

We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.14.

Course Spotlight: What’s New in Python 3.14

Covers Python 3.14’s key changes: free-threading, subinterpreters, t-strings, lazy annotations, new REPL features, and improved error messages.

Topics:

00:00:00 – Introduction
00:02:03 – Cool New Features for You to Try - Tutorial
00:02:20 – What’s New in Python 3.14 - Video Course
00:02:54 – Even Friendlier Python REPL
00:05:05 – Allow “json” instead of “json.tool”
00:05:49 – Zstandard compression
00:06:37 – Working With Numbers
00:11:09 – Deferred Evaluation of Annotations
00:17:35 – More Helpful Error Messages
00:20:41 – Warnings in try…finally Blocks
00:22:08 – Safer Live Process Debugging
00:25:22 – Pathlib Improvements
00:26:50 – Additional assert methods for unittest
00:27:19 – Template Strings (T-strings)
00:31:01 – Free-threaded Build Updates
00:35:30 – Incremental Garbage Collector
00:37:59 – functools partial & placeholder improvement
00:40:13 – Video Course Spotlight
00:41:12 – Experimental JIT Builds
00:46:52 – Parallel Subinterpreters
00:48:59 – Unicode Database Update
00:49:47 – 𝜋thon - Easter Egg
00:51:26 – Starting to use the new version
00:55:26 – Thanks and goodbye

Show Links:

Additional Links:

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Advice on Beginning to Learn Python

Fri, 03 Oct 2025 12:00:00 +0000

What’s changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python.

We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions.

Stephen shares details about our upcoming eight-week live course, Python for Beginners: Code With Confidence. Check out realpython.com/live to learn more and reserve your spot.

Course Spotlight: 11 Beginner Tips for Learning Python

In this course, you’ll see several learning strategies and tips that will help you jumpstart your journey towards becoming a successful Python programmer.

Topics:

00:00:00 – Introduction
00:02:36 – Martin’s teaching background
00:02:57 – Stephen’s teaching background
00:04:07 – Having a vested interest in learning
00:08:35 – No shortcut to learning the fundamentals
00:09:21 – Parallels to learning a foreign language
00:12:43 – What’s different about starting to learn Python now?
00:15:03 – Stephen’s journey to coaching and using LLMs
00:16:20 – Are LLMs helpful for learning?
00:18:50 – Teaching what you’ve learned to someone else
00:19:38 – Learning how to ask good questions
00:22:11 – Improved error messages
00:24:35 – REPL: Read Evaluate Print Loop
00:26:33 – Video Course Spotlight
00:27:48 – Installing Python and choosing an editor
00:35:16 – Considering the scale of beginner projects
00:37:39 – Should a beginner be concerned with making Pythonic code?
00:40:55 – Using LLM tools and defining your level of skill
00:42:39 – Python for Beginners: Code With Confidence - live course
00:47:32 – Looking at projects - Awesome Python
00:48:02 – Asking an LLM to explain the code generated
00:50:46 – Debuggers and seeing code run
00:51:23 – Thanks and goodbye

Show Links:

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Managing Feature Flags & Comparing Python Visualization Libraries

Fri, 26 Sep 2025 12:00:00 +0000

What’s a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article exploring the use of feature flags. The piece advises targeting specific users, storing schemas, and relying on well-worn code paths. He also discusses the danger of the exponential growth of functionality tests as feature flags are added.

We dig into a comparison of six popular Python visualization libraries. The article provides code examples along with their respective visual output to highlight features and make selecting the optimal library for your work easier.

We also share several other articles and projects from the Python community, including a news roundup, benchmarking MicroPython, simplifying IPs and networks in Python, a “scream” cipher, and a browser-based graphical viewer for the output of Python’s cProfile module.

This episode is sponsored by InfluxData.

Course Spotlight: Exploring Astrophysics in Python With pandas and Matplotlib

This course uses three problems often covered in introductory astrophysics courses to explore in Python. Along the way, you’ll learn some astronomy and how to use a variety of data science libraries like NumPy, Matplotlib, pandas, and pint.

Topics:

00:00:00 – Introduction
00:03:04 – Python 3.14.0rc3 is go!
00:03:14 – Django 6.0 alpha 1 released
00:04:27 – PEP 782: Add PyBytesWriter C API
00:05:06 – PEP 794: Import Name Metadata
00:05:30 – PEP 803: Stable ABI for Free-Threaded Builds
00:05:55 – Announcing the 2025 PSF Board Election Results!
00:06:18 – Top 6 Python Libraries for Visualization: Which One to Use?
00:18:34 – Sponsor: InfluxData
00:19:24 – Feature Flags in Depth
00:22:55 – Benchmarking MicroPython
00:30:02 – Video Course Spotlight
00:31:16 – Simplify IPs, Networks, and Subnets With the ipaddress
00:34:52 – SCREAM CIPHER (“ǠĂȦẶAẦ ĂǍÄẴẶȦ”
00:36:20 – SnakeViz: browser based graphical viewer for the output of Python’s cProfile module
00:40:58 – Thanks and goodbye

News:

Show Links:

Top 6 Python Libraries for Visualization: Which One to Use? – The vast number of Python visualization libraries can be overwhelming. This article shows you the pros and cons of some popular libraries, including Matplotlib, seaborn, Plotly, Bokeh, Altair, and Pygal.
Feature Flags in Depth – Feature flags are a way to enable or disable blocks of code without needing to redeploy your software. This post shows you several different approaches to feature flags.
Benchmarking MicroPython – This post compares the performance of running Python on several microcontroller boards.
Simplify IPs, Networks, and Subnets With the ipaddress – Python’s built-in ipaddress module makes handling IP addresses and networks clean and reliable. This article shows how to validate, iterate, and manage addresses and subnets while avoiding common pitfalls of string-based handling.

Projects:

SCREAM CIPHER (“ǠĂȦẶAẦ ĂǍÄẴẶȦ”) – Seth discovered that Unicode has more accented “Latin capital letter A” characters than the 26 letters in the English alphabet.
SnakeViz: browser based graphical viewer for the output of Python’s cProfile module

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Dangers of Automatically Converting a REST API to MCP

Fri, 19 Sep 2025 12:00:00 +0000

When converting an existing REST API to the Model Context Protocol, what should you consider? What anti-patterns should you avoid to keep an AI agent’s context clean? This week on the show, Kyle Stratis returns to discuss his upcoming book, “AI Agents with MCP”.

Kyle has been busy since he last appeared on the show in 2020. He’s taken his experience working in machine learning startups and started his own consultancy, Stratis Data Labs. He’s been documenting his explorations working with LLMs and MCP on his blog, The Signal Path.

Kyle is also writing a book about building MCP clients, services, and end-to-end agents. We discuss a recent article he wrote about the hazards of using an automated tool to convert a REST API into an MCP server. He shares his personal experiences with building MCP tools and provides additional resources for you to learn more about the topic.

This episode is sponsored by InfluxData.

Spotlight: Python for Beginners: Code With Confidence – Real Python

Learn Programming Fundamentals and Pythonic Coding in Eight Weeks—With a Structured Course

Topics:

00:00:00 – Introduction
00:02:41 – Updates on career
00:04:36 – The Signal Path - newsletter
00:07:15 – Moving into consulting
00:12:35 – Recent projects
00:14:51 – Need for data skills with MCP
00:16:49 – Describing the differences between REST APIs and MCP
00:19:59 – Interaction model differences
00:27:29 – Sponsor: InfluxData
00:28:21 – Agent stories
00:32:58 – Going through a simple example of MCP server
00:37:50 – Defining client and server
00:40:19 – Examples of servers currently
00:51:44 – Announcement: Python for Beginners: Code with Confidence
01:02:07 – Resources for further study
01:05:07 – Breaking down advice on moving an API to MCP
01:08:04 – What are you excited about in the world of Python?
01:18:20 – What do you want to learn next?
01:21:35 – How can people follow your work online?
01:22:46 – Thanks and goodbye

Show Links:

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Python App Hosting Choices & Documenting Python's History

Fri, 12 Sep 2025 12:00:00 +0000

What are your options for hosting your Python application or scripts? What are the advantages of a platform as a service, container-based hosts, or setting up a virtual machine? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article about hosting Python applications. The piece digs into the differences between using containers or platform-provided tools to automatically configure your project. We also cover an older article listing several free hosting options for smaller projects or Python scripts.

The Python documentary premiered this past week, and we provide a synopsis and our thoughts. Following a similar theme, we cover our recent code conversation video course, which explores the history of Python versions and the features added across the releases.

We also share several other articles and projects from the Python community, including a news roundup, regex affordances, four different ways to speed up your code, a Python-based music library manager, a tool to visualize the structure of your data in Python, and a project for exploring color science in Python.

This episode is sponsored by InfluxData.

Course Spotlight: A History of Python Versions and Features

Explore Python’s evolution from the 1990s to today with a brief history and demos of key features added throughout its lifetime.

Topics:

00:00:00 – Introduction
00:03:02 – PEP 728: TypedDict With Typed Extra Items
00:04:09 – Django security releases issued: 5.2.6, 5.1.12, and 4.2.24
00:04:37 – Python Type System and Tooling Survey 2025
00:04:56 – Python: The Documentary - An Origin Story
00:14:11 – A History of Python Versions and Features
00:16:58 – Sponsor: InfluxData
00:17:48 – Regex Affordances
00:21:34 – Where to Host Your Python App
00:26:48 – Best hosting platforms for Python applications and Python scripts
00:30:18 – Video Course Spotlight
00:31:59 – 330× Faster: Four Different Ways to Speed Up Your Code
00:37:18 – beets: Music Library Manager
00:39:12 – Memory Graph - GitHub
00:40:49 – colour: Colour Science for Python
00:42:35 – Thanks and goodbye

News:

Show Links:

Python: The Documentary - An Origin Story - YouTube – “This is the story of the world’s most beloved programming language: Python. What began as a side project in Amsterdam during the 1990s became the software powering artificial intelligence, data science and some of the world’s biggest companies.”
A History of Python Versions and Features – Video Course
Regex Affordances – A tour of some real code showing little-used power features of the Python regular expression module, including verbose regex syntax, calling re.sub() with a function reference, and more.
Where to Host Your Python App – Whether it’s Django, Flask, FastAPI, or some other Python web framework, your hosting options are plenty. This guide will show you how to choose.
Best hosting platforms for Python applications and Python scripts
330× Faster: Four Different Ways to Speed Up Your Code – There are many approaches to speeding up Python code; applying multiple approaches can make your code even faster. This post talks about four different ways you can achieve speed-up.

Projects:

Additional Links:

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Large Language Models on the Edge of the Scaling Laws

Fri, 05 Sep 2025 12:00:00 +0000

What’s happening with the latest releases of large language models? Is the industry hitting the edge of the scaling laws, and do the current benchmarks provide reliable performance assessments? This week on the show, Jodie Burchell returns to discuss the current state of LLM releases.

The most recent release of GPT-5 has been a wake-up call for the LLM industry. We discuss how the current scaling of these systems is reaching a diminishing edge. Jodie also shares how many AI model assessments and benchmarks are flawed. We also take a sober look at the productivity gains from using these tools for software development within companies.

We discuss how newer developers should consider additional factors when looking at the current job market. Jodie digs into how economic changes and rising interest rates are influencing layoffs and hiring freezes. Then we share a wide collection of resources for you to continue exploring these topics.

This episode is sponsored by InfluxData.

Course Spotlight: Exploring Python Closures: Examples and Use Cases

Learn about Python closures: function-like objects with extended scope used for decorators, factories, and stateful functions.

Topics:

00:00:00 – Introduction
00:03:00 – Recent conferences and talks
00:04:18 – What’s going on with LLMs?
00:06:06 – What happened with the GPT-5 release?
00:08:14 – Simon Willison - 2025 in LLMs so far
00:09:00 – How did we get here?
00:10:37 – OpenAI’s and scaling laws
00:12:25 – Pivoting to post-training
00:16:01 – Some history of AI eras
00:17:54 – Issues with measuring performance and benchmarks
00:22:19 – Chatbot Arena
00:24:06 – Languages are finite
00:26:22 – LLMs and the illusion of humanity
00:30:41 – Sponsor: InfluxData
00:31:34 – Types of solutions to move past these limits
00:36:57 – Does AI actually boost developer productivity?
00:44:19 – Agentic Al Programming with Python
00:48:02 – Results of non-programmers vibe coding
00:50:18 – Back to the concept of overfitting
00:52:52 – The money involved in training
00:56:50 – Video Course Spotlight
00:58:21 – Deepseek and new methods of training
01:01:02 – Quantizing and fitting on a local machine
01:04:48 – The layoffs and the economic changes
01:10:32 – AI implementation failures
01:21:01 – Don’t doubt yourself as a developer
01:24:06 – What are you excited about in the world of Python?
01:25:39 – What do you want to learn next?
01:26:42 – What’s the best way to follow your work online?
01:27:04 – Thanks and goodbye

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Exploring Mixin Classes in Python

Fri, 29 Aug 2025 12:00:00 +0000

What is a good way to add isolated, reusable functionality to Python classes? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher covers a recent Real Python tutorial about developing mixin classes to reuse code across multiple Python classes. He describes how mixins rely on multiple inheritance to combine features from different classes, enhancing flexibility and code reuse.

We also share several other articles and projects from the Python community, including a news roundup, highlights from the 2024 Python Developers Survey, reasons you might not need a Python class, an exploration of asyncio’s sharp corners, an explanation of how JIT builds of CPython work, a web-based GUI library, and a project for quickly querying Python lists.

This episode is sponsored by InfluxData.

Course Spotlight: Design and Guidance: Object-Oriented Programming in Python

In this video course, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that’s more maintainable, extensible, scalable, and testable.

Topics:

00:00:00 – Introduction
00:03:14 – Python 3.13.7 Released
00:03:38 – Python 3.14.0rc2 Released
00:04:10 – PEP 802: Display Syntax for the Empty Set
00:04:59 – Announcing the PSF Board Candidates for 2025
00:05:26 – Rodrigo - PSF - Community Service Award for Q2 2025
00:06:06 – Python Developers Survey 2024 Results
00:13:27 – pyx: A Python-Native Package Registry
00:15:12 – Test & Code Final Episode
00:15:48 – You Might Not Need a Python Class
00:20:52 – Sponsor: InfluxData
00:21:44 – asyncio: A Library With Too Many Sharp Corners
00:25:43 – How JIT Builds of CPython Actually Work
00:35:21 – Video Course Spotlight
00:37:12 – What Are Mixin Classes in Python?
00:44:05 – nicegui: Create Web-Based UI With Python
00:46:51 – leopards: Quickly query your Python lists
00:49:00 – Thanks and goodbye

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News:

Python 3.13.7 Released
Python 3.14.0rc2 Released
PEP 802: Display Syntax for the Empty Set (Added)
Announcing the PSF Board Candidates for 2025 – The Python Software Foundation elections are upon us, and this post announces this year’s candidates. Voting is September 2nd to 16th. To vote, you must be registered by August 26th.
Rodrigo - PSF - Community Service Award for Q2 2025
Python Developers Survey 2024 Results
The State of Python 2025 - The PyCharm Blog
pyx: A Python-Native Package Registry, Now in Beta – The folks at Astral, who brought you uv and more, have created a new commercial Python-native package registry called “pyx”.
Test & Code Final Episode – After 10 years and 237 episodes, Brian Okken has decided to stop recording Test & Code. He’ll still be contributing to Python Bytes. Here’s to all his work on a great podcast over the last decade.

Show Links:

You Might Not Need a Python Class – If you’re coming from other languages, you might think a class is the easiest way to do something, but Python has other options. This post shows you some alternatives and why you might choose them.
asyncio: A Library With Too Many Sharp Corners – asyncio has a few gotchas and this post describes five different problems, including: cancellation, disappearing tasks, and more.
How JIT Builds of CPython Actually Work – You don’t have to be a compiler engineer to understand how your code runs in a JIT build of CPython. This article runs you through just what happens under the covers.
What Are Mixin Classes in Python? – Learn how to use Python mixin classes to write modular, reusable, and flexible code with practical examples and design tips.

Projects:

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Travis Oliphant: SciPy, NumPy, and Fostering Scientific Python

Fri, 22 Aug 2025 12:00:00 +0000

What went into developing the open-source Python tools data scientists use every day? This week on the show, we talk with Travis Oliphant about his work on SciPy, NumPy, Numba, and many other contributions to the Python scientific community.

Travis discusses his initial involvement in the open-source community and how he discovered Python while working in biomedical imaging. He was trying to find ways to manage large sets of numerical data, which led to his initial contributions and collaborations in building scientific libraries.

His appearance on the show coincides with the release of the Python documentary, in which he’s featured. We discuss the myriad organizations Travis founded, including Quansight, OpenTeams, and Anaconda. We dig into his underlying mission to continue fostering the growth of the open-source scientific computing community.

This episode is sponsored by InfluxData.

Course Spotlight: NumPy Techniques and Practical Examples

In this video course, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.

Topics:

00:00:00 – Introduction
00:02:41 – Python documentary
00:07:44 – Getting involved in open source
00:12:04 – Numeric Python
00:15:36 – SciPy and the SciPy community
00:17:35 – Starting to think about entrepreneurship
00:18:16 – NumPy evolving from the work of Numeric
00:22:01 – Sponsor: InfluxData
00:22:53 – Python as controlling code for lower-level libraries
00:23:37 – Numba open-source JIT compiler
00:30:09 – Starting to build in Python before learning it all
00:34:45 – Python as the language AI generates
00:36:31 – Guilds and sharing knowledge
00:40:15 – More NumPy backstory
00:46:36 – Contributing to Python
00:48:24 – Video Course Spotlight
00:49:41 – The investment of companies in Python
00:51:22 – Quansight and businesses in open source
00:53:09 – Open Teams and Quansight details
00:57:14 – NumFOCUS and Anaconda
00:58:51 – FairOSS
01:02:36 – Documenting these efforts
01:05:37 – What are you excited about in the world of Python?
01:07:12 – What do you want to learn next?
01:08:10 – How can people follow your work online?
01:10:03 – Thanks and goodbye

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Selecting Inheritance or Composition in Python

Fri, 15 Aug 2025 12:00:00 +0000

When considering an object-oriented programming problem, should you prefer inheritance or composition? Why wouldn’t it just be simpler to use functions? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article about structuring code using inheritance, composition, or only functions. We discuss how the piece is a good exploration of the trade-offs of each solution. Unlike the tutorials beginners typically encounter while learning the fundamentals, the article goes much deeper into the “why” of object-oriented programming.

We also share several other articles and projects from the Python community, including a news roundup, processing audio in Python, reasons why you shouldn’t call dunder methods, smuggling arbitrary data through an emoji, an HTML to markdown converter, and a library to convert Python requests into curl commands.

Course Spotlight: Single and Double Underscore Naming Conventions in Python

In this video course, you’ll learn a few Python naming conventions involving single and double underscores (_). You’ll learn how to use this character to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.

Topics:

00:00:00 – Introduction
00:02:23 – Python 3.13.6 Released
00:02:47 – Django 5.2.5 Released
00:02:55 – Django REST Framework Release v3.16.1
00:03:09 – Narwhals v2.0.0 Released
00:03:22 – mypy 1.17 Released
00:03:42 – PEP 798: Unpacking in Comprehensions
00:04:11 – PEP 799: A Dedicated Profilers Package for Organizing Python Profiling Tools
00:06:12 – PyPI Users Email Phishing Attack
00:07:33 – Django in Action
00:08:00 – Call for proposals deadline - PyCon NL
00:08:59 – Python Audio Processing With pedalboard
00:18:49 – Smuggling Arbitrary Data Through an Emoji
00:21:53 – Don’t Call Dunder Methods
00:28:51 – Video Course Spotlight
00:30:27 – Inheritance Over Composition, Sometimes
00:40:03 – html-to-markdown: HTML to Markdown Converter
00:42:20 – curlify: A library to convert Python requests request object into curl commands
00:44:18 – transfunctions: Support Both Sync and Async
00:45:18 – Thanks and goodbye

Survey:

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News:

Python 3.13.6 Released
Django 5.2.5 Released
Django REST Framework Release v3.16.1
Narwhals v2.0.0 Released
Mypy 1.17 Released
PEP 798: Unpacking in Comprehensions (Added)
PEP 799: A Dedicated Profilers Package for Organizing Python Profiling Tools (Added)
PyPI Users Email Phishing Attack – PyPI users are being targeted by an email phishing attack attempting to trick them into logging into a fake PyPI site. This post from the Security Engineer at PyPI discusses what’s happening and what you should do about it. There’s also a follow-up post.
PyCon NL 2025 - Call for Papers
Django in Action - Christopher Trudeau - Code ladjango40

Topics:

Python Audio Processing With pedalboard – The pedalboard library for Python is aimed at audio processing of various sorts, from converting between formats to adding audio effects. This post summarizes a PyCon US talk on pedalboard and its uses.
Smuggling Arbitrary Data Through an Emoji – Unicode includes flexibility through the use of variation selectors. These include the ability to change characters through a consecutive series of coding points. But, when used with code points that don’t need them, they’re ignored, so you can hide data in them.
Don’t Call Dunder Methods – It’s best to avoid calling dunder methods. It’s common to define dunder methods, but uncommon to call them directly.
Inheritance Over Composition, Sometimes – In an older post, Adrian wrote some code using inheritance. He got questions from his readers asking why it wouldn’t just be simpler to use functions. This post re-implements the code with inheritance, composition, and plain old functions, then compares the approaches.

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Harnessing the Power of Python Polars

Fri, 08 Aug 2025 12:00:00 +0000

What are the advantages of using Polars for your Python data projects? When should you use the lazy or eager APIs, and what are the benefits of each? This week on the show, we speak with Jeroen Janssens and Thijs Nieuwdorp about their new book, Python Polars: The Definitive Guide.

Jeroen and Thijs describe how they were introduced to Polars while working at Xomnia. They were converting a large data project to Python and saw surprising speed increases using the new library.

We discuss converting projects from pandas to Polars, getting away from indexes, consistent syntax, and using lazy vs eager APIs. Along the way, Jeroen and Thijs offer tips for getting the most out of Polars in your code.

We dig into the process of writing a definitive guide and the advantages of working collaboratively on a book project. They also share resources for practicing data wrangling and building visualizations with Pydy Tuesday.

Course Spotlight: Working With Python Polars

Welcome to the world of Polars, a powerful DataFrame library for Python. In this video course, you’ll get a hands-on introduction to Polars’ core features and see why this library is catching so much buzz.

Topics:

00:00:00 – Introduction
00:02:47 – Polars start at Xomnia
00:04:08 – Putting Polars into production
00:07:18 – Realizing the speed differences
00:08:49 – Converting the project from R to Python
00:14:34 – How did Polars improve the project?
00:16:34 – Making the code more ergonomic and readable
00:19:21 – Only grabbing the data that is needed
00:20:37 – Titling and deciding to write the book
00:24:40 – Advantages to collaboration
00:29:34 – What were you excited to include in the book?
00:31:55 – Working with different engines and Nvidia’s Cuda
00:35:05 – Defining a Polars expression
00:36:11 – Transitioning from pandas to Polars
00:37:34 – Not needing an index
00:39:56 – What inspired the syntax?
00:45:01 – Defining lazy vs eager workflows
00:49:16 – Examples covered in first chapter preview
00:51:51 – Video Course Spotlight
00:53:14 – Data formats and Arrow
00:55:41 – Working with NaN, null, or None
00:58:11 – Measuring performance through a benchmark
00:59:12 – Advantages to working with the Discord community
01:02:32 – Code examples and applying the techniques
01:03:34 – Pydy Tuesday
01:05:47 – What are you excited about in the world of Python?
01:09:21 – What do you want to learn next?
01:13:26 – What’s the best way to follow your work online?
01:14:14 – Thanks and goodbye

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Design Patterns That Don't Translate to Python

Fri, 01 Aug 2025 12:00:00 +0000

Do the design patterns learned in other programming languages translate to coding in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher dives into an article that tackles popular object-oriented design patterns from the famous Gang of Four book. These patterns offer solutions to common coding problems, but as Christopher explores, Python often doesn’t even have the problems these solutions try to fix. He discusses several common design patterns and the simpler, more Pythonic ways to achieve the same goals.

We also share several other articles and projects from the Python community, including an exceptionally robust news roundup, running coverage on tests, an exploration of expert generalists, a preview of template strings from Python 3.14, a quiz on f-strings, and a project that calculates the complexity of your Python code.

Course Spotlight: Working With Python’s Built-in Exceptions

Learn the most common built-in Python exceptions, when they occur, how to handle them, and how to raise them properly in your code.

Topics:

00:00:00 – Introduction
00:02:03 – Python 3.14.0b4 Released
00:02:11 – Python 3.14 release candidate 1 is go!
00:02:48 – PyPy v7.3.20 Release
00:03:00 – Textual 4.0.0 Released
00:03:23 – Announcing Toad - a universal UI for agentic coding in the terminal
00:03:42 – uv 0.8.0 Released
00:03:56 – Django Bugfix Release 5.2.4
00:04:14 – Django Community Ecosystem
00:04:52 – Happy 20th Birthday Django!
00:05:31 – PyData London 2025 Videos
00:05:48 – PEP 792: Project Status Markers in the Simple Index
00:06:09 – PEP 800 – Solid bases in the type system
00:07:06 – Run Coverage on Tests
00:14:32 – Design Patterns You Should Unlearn in Python
00:18:13 – Video Course Spotlight
00:19:24 – Expert Generalists
00:34:42 – Python 3.14 Preview: Template Strings (T-Strings)
00:41:00 – fstrings.wtf - Python F-String Quiz
00:43:09 – complexipy: Calculate Complexity of Your Python
00:48:18 – Thanks and goodbye

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News:

Show Topics:

Run Coverage on Tests – Code coverage tools tell you which parts of your programs got executed during test runs. They’re an important part of your test suite, and without them, you may miss errors in your tests themselves. This post has two quick examples of just why you should use a coverage tool.
Design Patterns You Should Unlearn in Python – The Gang of Four design patterns specify object-oriented solutions to common issues in code, but Python doesn’t have many of the problems the solutions are aiming to solve. This article talks about some of the common patterns and the easier ways to solve the problems they intend to address in Python. See also Part 2.
Expert Generalists – MartinFowler.com – “As computer systems get more sophisticated we’ve seen a growing trend to value deep specialists. But we’ve found that our most effective colleagues have a skill in spanning many specialties.”
Python 3.14 Preview: Template Strings (T-Strings) – Python 3.14 introduces t-strings: a safer, more flexible alternative to f-strings. Learn how to process templates securely and customize string workflows.

Projects:

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Supporting the Python Package Index

Fri, 25 Jul 2025 12:00:00 +0000

What goes into supporting more than 650,000 projects and nearly a million users of the Python Package Index? This week on the show, we speak with Maria Ashna about her first year as the inaugural PyPI Support Specialist.

Maria has a varied background in creative arts and neuroscience. She decided to apply for the PyPI support position, defying common misconceptions about who can take on roles inside the Python Software Foundation, and challenging imposter syndrome along the way.

Her recent talks at PyCon US 2025 and EuroPython 2025 were about her experiences in the role. She describes tackling the backlogs of account recovery and PEP 541 requests, and we also discuss PyPI community and company organizations.

Course Spotlight: Publishing Python Packages to PyPI

In this video course, you’ll learn how to create a Python package for your project and how to publish it to PyPI, the Python Package Index. Quickly get up to speed on everything from naming your package to configuring it using setup.cfg.

Topics:

00:00:00 – Introduction
00:01:42 – What led you to learn Python?
00:08:09 – PyCon 2025 talk about the first year at PyPI
00:11:06 – Embracing asking questions
00:13:55 – Being willing to say “I don’t know, let’s find out”
00:15:06 – What is PEP 541 and resolving name retention issues
00:23:22 – Video Course Spotlight
00:24:40 – Addressing the account recovery backlog
00:26:43 – PyPI Organizations
00:30:54 – Moving beyond the hesitancy to submit a package to PyPI
00:40:43 – Getting past imposter syndrome and applying
00:45:07 – What are you excited about in the world of Python?
00:46:10 – What do you want to learn next?
00:47:52 – How can people follow your work online?
00:49:03 – Thanks and goodbye

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Comparing Real-World Python Performance Against Big O

Fri, 11 Jul 2025 12:00:00 +0000

How does the performance of an algorithm hold up when you put it into a realistic context? Where might Python code defy Big O notation expectations when using a profiler? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article about why real-world performance often defies Big O expectations. The piece starts with a task coded in Go and then optimized from O(n²) to O(n). Can an interpreted language like Python compete with a compiled language? Profiling the performance of both versions provides some interesting results.

We also share several other articles and projects from the Python community, including a news roundup, the fastest way to detect a vowel in a string, whether Python dictionaries are ordered data structures, an overview of Python’s enum module, a Python client library for Google Data Commons, and a project to convert plain ASCII to “smart” punctuation.

Course Spotlight: Building Enumerations With Python’s enum

In this video course, you’ll discover the art of creating and using enumerations of logically connected constants in Python. To accomplish this, you’ll explore the Enum class and other associated tools and types from the enum module in the Python standard library.

Topics:

00:00:00 – Introduction
00:02:12 – ruff Release 0.12.0
00:02:38 – streamlit Release 1.46.0
00:02:48 – lxml 6.0.0 Released
00:03:00 – PSF Board Election Schedule
00:03:26 – Are Python Dictionaries Ordered Data Structures?
00:08:59 – The Fastest Way to Detect a Vowel in a String
00:16:37 – Module enum Overview
00:24:46 – Video Course Spotlight
00:26:24 – O(no) You Didn’t
00:38:34 – New Python Client Library for Google Data Commons
00:41:55 – smartypants.py: Plain ASCII to “Smart” Punctuation
00:44:07 – Thanks and goodbye

News:

ruff Release 0.12.0
streamlit Release 1.46.0
lxml 6.0.0 Released
PSF Board Election Schedule – It is time for the Python Software Foundation Board elections. Nominations are due by July 29th. See the article for the full election schedule and deadlines.

Show Links:

Are Python Dictionaries Ordered Data Structures? – Although dictionaries have maintained insertion order since Python 3.6, they aren’t strictly speaking ordered data structures. Read on to find out why and how the edge cases can be important depending on your use case.
The Fastest Way to Detect a Vowel in a String – If you need to find the vowels in a string there are several different approaches you could take. This article covers 11 different ways and how each performs.
Module enum Overview – This article gives an overview of the tools available in the module enum and how to use them, including Enum, auto, StrEnum, Flag, and more.
O(no) You Didn’t – A deep dive into why real-world performance often defies Big-O expectations, and why context and profiling matter more than theoretical complexity.

Projects:

New Python Client Library for Google Data Commons – Google Data Commons announced the general availability of its new Python client library for the Data Commons. The goal of the library is to enhance how students, researchers, analysts, and data scientists access and leverage Data Commons.
smartypants.py: Plain ASCII to “Smart” Punctuation

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Solving Problems and Saving Time in Chemistry With Python

Fri, 04 Jul 2025 12:00:00 +0000

What motivates someone to learn how to code as a scientist? How do you harness the excitement of solving problems quickly and make the connection to the benefits of coding in your scientific work? This week on the show, we speak with Ben Lear and Christopher Johnson about their book “Coding For Chemists.”

Christopher is an associate professor of chemistry at Stony Brook University. Ben is a professor of chemistry at Penn State’s Eberly College of Science. They’re long-time friends who decided to collaborate on a book after discussing the challenges of teaching coding to chemistry students.

The book targets chemists and other researchers who want to streamline common workflows with Python. It covers core Python concepts, data visualization, and data analysis topics by sharing common problems encountered in chemical research and presenting a complete Python-based solution for each problem.

We discuss how they collaborated on the book and decided what libraries and tools to include. We cover how LLM tools have affected classroom teaching and require new techniques to reinforce learning. We also dig into what motivates students to learn how to code.

Course Spotlight: Defining Python Functions With Optional Arguments

In this video course, you’ll learn about Python optional arguments and how to define functions with default values. You’ll also learn how to create functions that accept any number of arguments using args and kwargs.

Topics:

00:00:00 – Introduction
00:02:06 – Ben’s background and starting with Python
00:04:34 – Chris’ background and starting with Python
00:07:16 – What has sped up Python for your use?
00:08:22 – How did idea for the book start?
00:11:30 – Shifting publisher and new release time frame
00:12:24 – Three potential audiences
00:13:20 – The ubiquitous need for programming skills in science
00:15:05 – Difficult workflows with chemistry equipment
00:16:06 – What is a chart recorder?
00:16:34 – Working with proprietary equipment and exporting data
00:23:37 – Explaining how programming will help chemists
00:27:31 – Finding the problems to solve
00:29:09 – The classic common chemistry workflow
00:30:48 – Teaching Python in a classroom and starting with functions
00:35:05 – Helping students cultivate inspiration
00:37:06 – LLM and AI use by students
00:41:19 – Video Course Spotlight
00:42:36 – Using Spyder IDE and Positron
00:45:29 – How does the book cover notebooks and managing packages?
00:48:08 – Using marimo for archiving and sharing projects
00:50:25 – What was difficult to put into the book?
00:54:04 – What were you eager to share in the book?
00:55:54 – Teaching students about file management
00:58:13 – Sharing tools to plot data
01:01:45 – Choosing not to teach pandas and using NumPy arrays instead
01:04:03 – How can people learn more about the book?
01:05:20 – What are you excited about in the world of Python?
01:07:58 – What do you want to learn next?
01:10:50 – How can people follow your work?
01:12:15 – Thanks and goodbye

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Structuring Python Scripts & Exciting Non-LLM Software Trends

Fri, 27 Jun 2025 12:00:00 +0000

What goes into crafting an effective Python script? How do you organize your code, manage dependencies with PEP 723, and handle command-line arguments for the best results? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We dig into a recent Real Python article about how to structure your Python scripts. It includes advice for adding inline script metadata as defined in PEP 723, which helps tools automatically create an environment and install dependencies when the script is run. The piece also covers choosing appropriate data structures, improving runtime feedback, and making your code more maintainable with constants and entry points.

We discuss a collection of software trends happening behind the scenes of the constant LLM news. The piece starts with local-first software, prioritizing processing and storing private data on personal devices rather than relying on the cloud. The other trends include common themes and tools we’ve shared over the past few years, including WebAssembly, SQLite’s renaissance, and improvements to cross-platform mobile development.

We also share several other articles and projects from the Python community, including a news roundup, the state of free-threaded Python, tips for improving Django management commands, advice for time management as a manager, a data science-focused IDE, and a project to check for multiple patterns in a single string.

Course Spotlight: SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files

In this video course, you’ll learn how to store and retrieve data using Python, SQLite, SQLAlchemy, and flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Topics:

00:00:00 – Introduction
00:02:26 – Followup - marimo and LaTeX
00:03:26 – PEP 734: Multiple Interpreters in the Stdlib (Accepted)
00:03:52 – Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 Security Releases
00:04:21 – Python Insider: Python 3.14.0 beta 3 is here!
00:04:30 – Django Bugfix Releases: 5.2.3, 5.1.11, and 4.2.23
00:04:52 – NumPy v2.3.0 Released
00:05:02 – scikit-learn 1.7 Released
00:05:12 – PyData Virginia 2025 Talks
00:05:52 – How Can You Structure Your Python Script?
00:12:08 – State of Free-Threaded Python
00:18:23 – 5 Non-LLM Software Trends to Be Excited About
00:29:50 – Video Course Spotlight
00:31:23 – Better Django Management Commands
00:33:56 – Advice for time management as a manager
00:46:49 – positron: Data Science IDE
00:50:05 – ahocorasick_rs: Check for Multiple Patterns in a Single String
00:52:41 – 10 Polars Tools and Techniques To Level Up Your Data Science - Podcast Episode
00:53:22 – Thanks and goodbye

News:

PEP 734: Multiple Interpreters in the Stdlib (Accepted)
Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 Security Releases
Python 3.13.5 Released
Python Insider: Python 3.14.0 beta 3 is here!
Django Bugfix Releases: 5.2.3, 5.1.11, and 4.2.23
NumPy v2.3.0 Released
scikit-learn 1.7 Released
PyData Virginia 2025 Talks – A list of the recorded talks from PyData Virginia 2025.

Show Links:

How Can You Structure Your Python Script? – Structure your Python script like a pro. This guide shows you how to organize your code, manage dependencies with PEP 723, and handle command-line arguments.
State of Free-Threaded Python – This is a blog post from the Python Language Summit 2025 giving an update on the progress of free-threaded Python. You may also be interested in the complete list of Language Summit Blogs.
PEP 779 – Criteria for supported status for free-threaded Python
5 Non-LLM Software Trends to Be Excited About – Tired of reading about AI and LLMs? This post talks about other tech that is rapidly changing in the software world, including local-first applications, web assembly, the improvement of cross-platform tools, and more.
Better Django Management Commands – Writing Django management commands can involve a ton of boilerplate code. This article shows you how to use two libraries that could cut your management command code in half: django-click and django-typer.

Discussion:

Advice for time management as a manager - benkuhn.net

Projects:

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Scaling Python Web Applications With Kubernetes and Karpenter

Fri, 20 Jun 2025 12:00:00 +0000

What goes into scaling a web application today? What are resources for learning and practicing DevOps skills? This week on the show, Calvin Hendryx-Parker is back to discuss the tools and infrastructure for autoscaling web applications with Kubernetes and Karpenter.

Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares how they recently helped a client scale a web application that employs video, audio, and chat sessions. We dig deep into the tooling behind modern Kubernetes systems management and performance monitoring.

Calvin shares a project bootstrap tool for streamlining the development and deployment of a web application. The tool includes a complete blueprint for the infrastructure needed to get started.

We also dig into a collection of coding tools Calvin has been experimenting with. We discuss his recent IndyPy presentation, “Battle of the Bots,” which put several AI code assistants through their paces.

This episode is sponsored by AMD.

Course Spotlight: First Steps With LangChain

Large language models (LLMs) have taken the world by storm. In this step-by-step video course, you’ll learn to use the LangChain library to build LLM-assisted applications.

Topics:

00:00:00 – Introduction
00:02:23 – Scaling a Django project using Kubernetes
00:05:35 – Elastic Kubernets Service (EKS)
00:09:10 – Kubernetes terminology and improvements in tooling
00:11:29 – The Control Plane and the API
00:14:06 – Video Course Spotlight
00:15:11 – scaf: providing DevOps engineers a blueprint for new projects
00:17:21 – What have been the benefits of scaf for internal teams?
00:20:18 – How do you identify and reproduce scaling issues?
00:22:44 – Dealing with IP address scaling
00:26:03 – Why use other observability tools beyond AWS internal ones?
00:29:22 – Other lessons learned and moving toward refactoring code
00:33:53 – Scaling a voice-based LLM application
00:35:35 – Sponsor: AMD
00:36:11 – Looking at limitations and bottlenecks
00:38:08 – Configuring a Kubernetes operator to act on itself
00:39:47 – What project components are within a pod of containers?
00:42:31 – Budgeting for scale using Karpenter
00:43:58 – Tools for running containers locally
00:46:01 – Are containers still a primary development tool for you?
00:50:58 – Resources for learning DevOps and Kubernetes
00:52:54 – Conferences and talks
00:53:56 – Battle of the Bots: comparing coding agents
00:55:15 – What are you excited about in the world of Python?
00:56:20 – What do you want to learn next?
01:02:42 – What’s the best way for people to follow your work online?
01:03:33 – Thanks and goodbye

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Starting With marimo Notebooks & Python App Config Management

Fri, 13 Jun 2025 12:00:00 +0000

Looking for a guide on getting started with marimo notebooks? How do you build a reproducible notebook for sharing or create a dashboard with interactive UI elements? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We cover a recent Real Python article by Ian Eyre about using narimo notebooks. The tutorial covers installing Marimo, taking advantage of reactivity, building interactive dashboards, and managing a notebook’s environment through sandboxing. The piece ends by examining the limitations of traditional linear notebooks and how Marimo addresses them.

Christopher discusses an article about how to store the configurations for your Python scripts and projects. Whether you’re managing resource handles to a database, deployment variables, or credentials to external services, you’ll need a way to save and load the details into your Python project. The piece compares saving configurations in several common file formats or through environment variables.

We also share several other articles and projects from the Python community, including a news roundup, the discourse between generative AI coding proponents and detractors, catching memory leaks with your test suite, epigrams on programming, a command line tool to check packages on PyPI, and a collection of string, file, and object utilities.

This episode is sponsored by Six Feet Up.

Course Spotlight: Python Continuous Integration and Deployment Using GitHub Actions

With most software following agile methodologies, it’s essential to have robust DevOps systems in place to manage, maintain, and automate common tasks with a continually changing codebase. By using GitHub Actions, you can automate your workflows efficiently, especially for Python projects.

Topics:

00:00:00 – Introduction
00:02:44 – Python Release 3.14.0b2
00:03:09 – Django security releases issued: 5.2.2, 5.1.10, and 4.2.22
00:03:27 – PyBay 2025
00:03:43 – PyCon NL 2025 - Call for Proposals
00:04:05 – Django Forum: Supporting t-strings
00:04:44 – Ruff Users: What Rules Are You Using and What Are You Ignoring?
00:05:19 – My Shot at Real Python
00:06:03 – My AI Skeptic Friends Are All Nuts
00:10:06 – I Think I’m Done Thinking About genAI For Now
00:11:12 – AI Changes Everything
00:23:01 – Video Course Spotlight
00:24:14 – Configuration of Python Applications
00:29:15 – marimo: A Reactive, Reproducible Notebook
00:35:15 – Sponsor: Six Feet Up
00:36:02 – Catching memory leaks with your test suite
00:41:45 – Epigrams on Programming
00:46:54 – whatsonpypi: Check PyPI From the Command Line
00:48:18 – strif: String, File, and Object Utilities
00:49:44 – PyCoder’s Weekly - Submit a Link
00:50:13 – Thanks and goodbye

News:

Python Release 3.14.0b2
Django security releases issued: 5.2.2, 5.1.10, and 4.2.22
PyBay 2025
PyCon NL 2025 - Call for Proposals
Django Forum: Supporting t-strings
Ruff Users: What Rules Are You Using and What Are You Ignoring?
My Shot at Real Python – Amanda has recently written her first article for Real Python and this post talks about her experience doing so. If you want to check out the article, it’s on Nested Loops.

Show Links:

My AI Skeptic Friends Are All Nuts
I Think I’m Done Thinking About genAI For Now
AI Changes Everything
AI Blog Comparison - Armin Ronacher
Configuration of Python Applications – This post talks about how to store configuration for your script and how and when to load the information into your program.
marimo: A Reactive, Reproducible Notebook – Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks.
Catching memory leaks with your test suite – “If you have a good test suite, you may be able to use pytest fixtures to identify memory and other resource leaks.”

Discussion:

Projects:

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Rodrigo Girão Serrão: Python Training, itertools, and Idioms

Fri, 06 Jun 2025 12:00:00 +0000

Once you’ve learned the vocabulary and syntax of the Python language, how do you progress into learning the right combinations to put into your code? How can Python’s built-in itertools library enhance your skills? This week on the show, we speak with Rodrigo Girão Serrão about teaching Python through his blog and his passion for the itertools library.

We discuss Rodrigo’s different approaches to writing on his blog. He likes to document smaller concepts about Python and building code in his “Today I Learned” series. He’s also been collecting advice about the best way to use core Python features in another series called “Pydon’ts.”

We cover his recent PyCon US tutorial about the built-in itertools module. The functions contained in the module create iterators for efficient looping. We discuss the categories of tools inside the collection and ways to simplify your code.

We also explore the concept of vocabulary versus idioms in writing. Idioms are a group of words that hold a symbolic meaning that goes beyond the literal meaning of the individual words. We dig into how that applies to learning Python and building a personal collection of programming idioms.

This episode is sponsored by AMD.

Course Spotlight: Working With Missing Data in Polars

In this video course, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them.

Topics:

00:00:00 – Introduction
00:02:34 – Creating Polars video course
00:03:27 – How did you start programming and teaching Python?
00:04:59 – Where did mathspp come from?
00:05:38 – Exploring math and programming in university
00:07:48 – Learning APL
00:09:24 – What goes into building the blog?
00:15:05 – The Pydon’ts and writing books
00:18:37 – PyCon US 2025
00:20:46 – Sponsor: AMD
00:21:23 – Teaching a tutorial about itertools
00:28:58 – Categorizing itertools
00:40:39 – Video Course Spotlight
00:41:55 – The difference between me and Shakespeare
00:46:51 – Learning and practicing with idioms
00:51:01 – TIL and asking questions
00:53:54 – What are you excited about in the world of Python?
00:55:40 – What do you want to learn next?
00:57:35 – How can people follow your work online?
01:01:19 – Thanks and goodbye

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Python Thread Safety & Managing Projects With uv

Fri, 30 May 2025 12:00:00 +0000

What are the ways you can manage multithreaded code in Python? What synchronization techniques are available within Python’s threading module? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his recent Real Python video course about thread safety. The course provides a quick overview of race conditions and how to use locks in your code. It then goes on to share a collection of additional synchronization primitives to make your code thread-safe.

We dig into a tutorial by Leodanis Pozo Ramos about managing Python projects with uv. The tutorial shows you how to quickly initialize a project, build the directory structure, add dependencies, and publish your package while practicing the commands inside uv.

We also share several other articles and projects from the Python community, including a news roundup, unraveling t-strings, what’s new in pip 25.1, an SVG-first plotting library, and a data modeling tool built on top of Polars and Pydantic.

Course Spotlight: Thread Safety in Python: Locks and Other Techniques

In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe.

Topics:

00:00:00 – Introduction
00:02:23 – PEP 773: A Python Installation Manager for Windows
00:03:09 – PEP 784: Adding Zstandard to the Standard Library
00:03:28 – Python Insider: Python 3.14.0 Beta 1 Is Here!
00:03:48 – Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.2
00:04:09 – ty: New Type Checker and Language Server by Astral
00:05:01 – pyrefly: A Fast Type Checker and IDE for Python
00:06:03 – The Future of Textualize
00:07:08 – Managing Python Projects With uv
00:12:20 – pre-commit: Install With uv
00:13:03 – Python’s New t-strings
00:16:38 – Unraveling t-strings
00:18:33 – Video Course Spotlight
00:19:50 – What’s New in Pip 25.1
00:24:30 – Thread Safety in Python: Locks and Other Techniques
00:28:40 – glyphx: SVG-first Plotting Library
00:31:20 – patito: A data modeling layer built on top of Polars and Pydantic
00:34:02 – Thanks and goodbye

News:

PEP 773: A Python Installation Manager for Windows (Accepted)
PEP 784: Adding Zstandard to the Standard Library (Accepted)
Python Insider: Python 3.14.0 Beta 1 Is Here!
Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.21
ty: New Type Checker and Language Server by Astral
pyrefly: A Fast Type Checker and IDE for Python
The Future of Textualize – Will McGugan, founder of Textualize the company has announced that they will be closing their doors. Textualize the open source project will remain.

Show Links:

Managing Python Projects With uv – In this tutorial, you’ll learn how to create and manage your Python projects using uv, an extremely fast Python package and project manager written in Rust.
pre-commit: Install With uv – pre-commit is Adam’s favorite Git-integrated “run things on commit” tool. It acts as a kind of package manager, installing tools as necessary from their Git repositories. This post explains how to use it with uv.
Python’s New t-strings – Using f-strings is a readable way of building output, but there are situations where they can’t be used because the contents need to be verified before being string-ified. The new t-strings, coming in 3.14, are a solution to this problem.
Unraveling t-strings – PEP 750 introduced t-strings for Python 3.14. These are a template string mechanism similar to f-strings. Although they are in 3.14.0b1, there isn’t any documentation yet, so this post explains what they are how they can be used.
What’s New in Pip 25.1 – pip 25.1 introduces support for Dependency Groups (PEP 735), resumable downloads, and an installation progress bar. Dependency resolution has also received a raft of bugfixes and improvements.
Thread Safety in Python: Locks and Other Techniques – In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe.

Projects:

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DjangoCon Europe 2025: Live Recording From Dublin

Fri, 23 May 2025 12:00:00 +0000

What goes into making video courses at Real Python? How should you build an installable Django application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

This week, the Real Python Podcast is experiencing several firsts. We recorded a show in front of a live audience for the very first time, and it happened in Dublin, Ireland—a place neither of us had visited before. I also got to meet Christopher Trudeau in person for the first time.

We’re sharing that live recording from the conference in this episode. We discuss how we create video courses at Real Python, and Christopher talks about his tutorial on how to write an installable Django application.

We also share a few summaries of the talks from the conference and projects from the Django community, including a news roundup, how decisions are made inside the Django Foundation, ways you can help with reviews, using the Django ORM with Marimo notebooks, maintaining a data-oriented project, how to get foreign keys horribly wrong, a project for simple deployment, and a project for adding extra buttons inside the Django Admin.

We would like to thank the audience members from Ireland who heard about DjangoCon by listening to the podcast. Thank you for attending the conference and for taking the time to say hello. We also appreciate those who asked us insightful questions at the end of the show.

We enjoyed exploring Dublin and recording the show in front of such a welcoming audience. We learned a ton from all the great talks given at the conference and made some new connections for future interviews.

This episode is sponsored by AMD.

Course Spotlight: How to Set Up a Django Project

In this course, you’ll learn the necessary steps that you’ll need to take to set up a new Django project. You’ll learn the basic setup for any new Django project, which needs to happen before programming the specific functionality of your project.

Topics:

00:00:00 – Introduction
00:03:59 – PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials
00:04:22 – PEP 736 – Shorthand syntax for keyword arguments at invocation
00:04:46 – PEP 661 – Sentinel Values
00:05:21 – Pydantic v2.11 Released
00:05:41 – How We Build Video Courses at Real Python
00:17:17 – Sponsor: AMD
00:17:56 – How to Write an Installable Django App
00:22:21 – Attendees from Ireland who heard about the conference from us
00:23:09 – Django needs you! (to do code review)
00:24:12 – How we make decisions in Django
00:26:07 – Marimo: Sharing the joys of the Django ORM with Python Notebooks
00:27:30 – Steering Council Introduction
00:28:17 – Video Course Spotlight
00:29:45 – Data-Oriented Django Drei
00:31:04 – How to get Foreign Keys horribly wrong in Django
00:32:17 – Converting integer fields to bigint using Django migrations at scale
00:33:17 – django-simple-deploy
00:34:57 – django-admin-extra-buttons
00:37:51 – What goes into creating the podcast?
00:44:04 – How does RP decide what Learning Paths to create?
00:48:30 – Python background when starting with a framework
00:54:46 – Django getting started resources at Real Python
00:55:34 – Thanks and goodbye

News:

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Going Beyond requirements.txt With pylock.toml and PEP 751

Fri, 16 May 2025 12:00:00 +0000

What is the best way to record the Python dependencies for the reproducibility of your projects? What advantages will lock files provide for those projects? This week on the show, we welcome back Python Core Developer Brett Cannon to discuss his journey to bring PEP 751 and the pylock.toml file format to the community.

Brett has been working on a way to move beyond the requirements.txt file for over six years. He was on the show previously to discuss his work on PEP 665, which was rejected. He decided to continue to push forward, authoring PEP 751 last year, which was accepted at the end of March this year.

The PEP calls for a new file format to record your project’s dependencies. The goal was to have a standardized immutable record for what should be installed to reproduce your project in a virtual environment. He discusses working with other packaging projects and the compromises involved in creating a standard.

Course Spotlight: Using the Python subprocess Module

In this video course, you’ll learn how to use Python’s subprocess module to run and control external programs from your scripts. You’ll start with launching basic processes and progress to interacting with them as they execute.

Topics:

00:00:00 – Introduction
00:02:38 – Brett’s roles within the Python community
00:05:41 – How to move beyond requirement.txt?
00:10:58 – What does the community use as project artifacts?
00:15:28 – Building on the success of pyproject.toml
00:17:44 – Introducing PEP 665
00:19:49 – Software Bills of Materials and security
00:25:20 – Back to lock files and security
00:31:08 – Video Course Spotlight
00:32:27 – Not giving up on the idea
00:34:01 – Leading into PEP 751
00:38:54 – Working toward a single multi-platform file
00:43:02 – The final push
00:48:54 – Leaving room for flexibility
00:53:50 – And it’s done, PEP 751 accepted unconditionally
00:58:06 – Keynote speaker at EuroPython 2025
00:58:45 – What are uv workspaces?
01:01:02 – Considering the use of lock files in data science
01:05:23 – Updates about Python for WASI and Emscripten
01:13:51 – Clarification on WASI
01:20:28 – Future conversation about Python launcher
01:23:04 – What are you excited about in the world of Python?
01:24:25 – What do you want to learn next?
01:28:41 – What’s the best way to follow your work online?
01:31:00 – Thanks and goodbye

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Experiments With Gen AI, Knowledge Graphs, Workflows, and Python

Fri, 09 May 2025 12:00:00 +0000

Are you looking for some projects where you can practice your Python skills? Would you like to experiment with building a generative AI app or an automated knowledge graph sentiment analysis tool? This week on the show, we speak with Raymond Camden about his journey into Python, his work in developer relations, and the Python projects featured on his blog.

Raymond is a developer evangelist and advocate who works with APIs, AI, and the web. He’s been expanding his developer knowledge by learning Python and documenting his journey through his blog and with the live-streaming show Code Break.

We discuss a couple of his recent Python projects. The first is building a resume review and revision system with generative AI and Flask. The other project uses Diffbot’s knowledge graph and Pipedream’s workflow tools to create an automated sentiment analysis tool.

This episode is sponsored by AMD.

Course Spotlight: What Can You Do With Python?

In this video course, you’ll find a set of guidelines that will help you start applying your Python skills to solve real-world problems. By the end, you’ll be able to answer the question, “What can you do with Python?”

Topics:

00:00:00 – Introduction
00:03:15 – Programming background and learning Python
00:07:59 – What’s been hard about learning a new language?
00:09:26 – Learning pip, managing packages, and suggesting uv
00:12:26 – Developer relations and sharing knowledge
00:14:40 – Sponsor: AMD - AIatAMD
00:15:17 – Moving things from Code Break to the blog
00:17:27 – Building a resume review and revise system with Gen AI
00:31:58 – Video Course Spotlight
00:33:16 – Adding the revision step
00:35:59 – Exploring code assistance
00:38:52 – Changing into the developer relations role
00:41:40 – Using Diffbot and Pipedream for sentiment analysis project
00:48:06 – Pipedream workflow with Python scripts
00:53:28 – What are you excited about in the world of Python?
00:55:45 – What do you want to learn next?
00:57:45 – How can people follow your work online?
00:58:03 – Thanks and goodbye

Show Links:

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Exploring DuckDB & Comparing Python Expressions vs Statements

Fri, 18 Apr 2025 12:00:00 +0000

Are you looking for a fast database that can handle large datasets in Python? What’s the difference between a Python expression and a statement? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We cover a Real Python article that explores DuckDB and discuss creating a database by reading data from multiple file formats. When building queries, DuckDB uses standard SQL syntax, or for an object-oriented approach, you can chain methods together using the Python API. We also explore the advantages of lazy evaluation using DuckDB relations.

Christopher digs into another Real Python tutorial that covers the differences between expressions and statements in Python. The piece goes beyond definitions to answer questions about where and when to use them in your code.

We also share several other articles and projects from the Python community, including a news roundup, an investigation into the lack of security in MCP, a discussion on the differences between staff engineer and engineering manager roles, guidance on creating and modifying Word documents with Python, and a project to go beyond print for debugging your code.

Check out realpython.com/workshops to join the upcoming cohort of the Intermediate Python Deep Dive course.

Course Spotlight: Creating a Python Dice Roll Application

In this step-by-step video course, you’ll build a dice-rolling simulator app with a minimal text-based user interface using Python. The app will simulate the rolling of up to six dice. Each individual die will have six sides.

Topics:

00:00:00 – Introduction
00:02:24 – Python 3.14.0a7, 3.13.3, 3.12.10, 3.11.12, 3.10.17 and 3.9.22 are now available
00:02:47 – PEP 768: Safe External Debugger Interface for CPython (Accepted)
00:03:16 – PEP 781: Make TYPE_CHECKING a Built-in Constant
00:03:43 – PEP 750: Template Strings (Accepted)
00:04:15 – PEP 751: A file format to record Python dependencies for installation reproducibility (Accepted)
00:05:20 – EuroPython July 14th-20th Prague, Tickets Available
00:05:42 – Django 5.2 Released
00:05:59 – Django security releases issued: 5.1.8 and 5.0.14
00:06:19 – Introducing DuckDB
00:12:19 – Expression vs Statement in Python: What’s the Difference?
00:17:11 – Video Course Spotlight
00:18:33 – The “S” in MCP Stands for Security
00:28:08 – Real Python Workshops
00:30:26 – Staff Engineer vs Engineering Manager
00:44:48 – python-docx: Create and modify Word documents with Python
00:47:28 – peek: like print, but easy
00:50:32 – Thanks and goodbye

News:

Python 3.14.0a7, 3.13.3, 3.12.10, 3.11.12, 3.10.17 and 3.9.22 are now available
PEP 768: Safe External Debugger Interface for CPython (Accepted)
PEP 781: Make TYPE_CHECKING a Built-in Constant – This PEP proposes adding a new built-in variable, TYPE_CHECKING, which is True when the code is being analyzed by a static type checker, and False during normal runtime.
PEP 750: Template Strings (Accepted) – This PEP introduces template strings for custom string processing.
PEP 751: A file format to record Python dependencies for installation reproducibility (Accepted)
EuroPython July 14th-20th Prague, Tickets Available
Django 5.2 Released
Django security releases issued: 5.1.8 and 5.0.14

Topics:

Introducing DuckDB – In this showcase tutorial, you’ll be introduced to a library that allows you to use a database in your code. DuckDB provides an efficient relational database that supports many features you may already be familiar with from more traditional relational database systems.
Expression vs Statement in Python: What’s the Difference? – In this tutorial, you’ll explore the differences between an expression and a statement in Python. You’ll learn how expressions evaluate to values, while statements can cause side effects. You’ll also explore the gray areas between them, which will enhance your Python programming skills.
The “S” in MCP Stands for Security - Elena Cross – Model Context Protocol is a new standard behind how Large Language Models integrate with tools and data. Unfortunately, MCP is not secure by default.
Staff Engineer vs Engineering Manager - Alex Ewerlöf Notes – When do you need a Staff Engineers? What’s the difference between Staff Engineer and Engineering Manager? This article covers these questions and more.

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Learning Intermediate Python With a Deep Dive Course

Fri, 11 Apr 2025 12:00:00 +0000

Do you want to learn deeper concepts in Python? Would the accountability of scheduled group classes help you get past the basics? This week, five Real Python Intermediate Deep Dive workshop members discuss their experiences.

We discuss the struggles of learning Python independently and the barriers to moving beyond the basics. We also explore the advantages of having a curated collection of both written tutorials and video courses. The cohort members also talk about filling in the gaps in their knowledge, using their new skills at work, and building confidence in their Python journey.

Check out realpython.com/live to join the upcoming cohort of the Intermediate Python Deep Dive course.

Course Spotlight: Efficient Iterations With Python Iterators and Iterables

In this video course, you’ll learn what iterators and iterables are in Python. You’ll learn how they differ and when to use them in your code. You’ll also learn how to create your own iterators and iterables to make data processing more efficient.

Topics:

00:00:00 – Introduction
00:02:04 – Matt’s background
00:03:17 – Chris’ background
00:05:55 – Jerry’s background
00:07:40 – Akhil’s background
00:09:25 – Rich’s background
00:10:35 – What skills didn’t translate from the previous language?
00:11:54 – Learning deeper concepts about OOP in Python
00:15:42 – Moving beyond scripts and ability to read code
00:19:41 – How accountability helps with learning
00:23:41 – Challenges with self-paced learning
00:28:11 – Having a curated collection of written and video materials
00:33:28 – Video Course Spotlight
00:34:56 – What were surprising discoveries?
00:36:32 – Working on a project
00:37:27 – Using these new skills at work
00:45:01 – Refining existing skills
00:46:41 – Do you feel more confident to learn even further?
00:49:26 – What are other Python projects you work on?
00:55:17 – Thanks and goodbyes

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GUIs & TUIs: Choosing a User Interface for Your Python Project

Fri, 04 Apr 2025 12:00:00 +0000

What are the current Python graphical user interface libraries? Should you build everything in the terminal and create a text-based user interface instead? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We cover a Real Python article that explores the Textual library. Textual is a Python toolkit and framework for creating attractive and functional text-based user interface (TUI) applications that run in the user’s terminal. The tutorial covers organizing layouts of widgets, styling components, and handling events and user actions within an application.

We continue our exploration of user interface options for your projects by discussing a recent article about Python GUI libraries. The piece compares the frameworks, showing a quick preview of how they look and sample code for a simple application. We share our thoughts and experiences with several of the libraries as we go through the collection.

We also share several other articles and projects from the Python community, including a news roundup, handling binary data in Python, exploring the rules terminal programs follow, using Microsoft Edge’s online text-to-speech service from Python, and a project for reading and writing compressed JSON.

Course Spotlight: Building a Code Image Generator With Python

In this step-by-step video course, you’ll build a code image generator that creates nice-looking images of your code snippets to share on social media. Your code image generator will be powered by the Flask web framework and include exciting packages like Pygments and Playwright.

Topics:

00:00:00 – Introduction
00:02:16 – PyCon US: Travel Grants & Refund Policy
00:02:57 – PyCon US 2025 travel guidance?
00:03:32 – Faster Branch Coverage Measurement
00:04:11 – Python Release Python 3.14.0a6
00:04:21 – Django 5.2 Release Candidate 1 Released
00:04:30 – PyOhio July 26-27, Call for Papers
00:04:59 – PEP 779: Criteria for Supported Status for Free-Threaded Python
00:05:45 – Python Textual: Build Beautiful UIs in the Terminal
00:11:32 – Bytes Objects: Handling Binary Data in Python
00:16:41 – Video Course Spotlight
00:18:01 – Which Python GUI Library Should You Use in 2025?
00:32:08 – Real Python Workshops
00:34:23 – “Rules” That Terminal Programs Follow
00:40:29 – edge-tts: Use Microsoft Edge’s online text-to-speech service from Python
00:44:07 – compress_json: Read and Write Compressed JSON
00:45:34 – Thanks and goodbye

News:

PyCon US: Travel Grants & Refund Policy – PyCon US offers travel grants to visitors. This post explains how they’re decided. Also, with changing border requirements in the US, you may also be interested in the Refund Policy for International Attendees.
PyCon US 2025 travel guidance? - PSF / Ask the staff! - Discussions on Python.org
Faster Branch Coverage Measurement – After nearly two years, Ned thinks this is finally ready: coverage.py can use sys.monitoring to more efficiently measure branch coverage.
Python Release Python 3.14.0a6
Django 5.2 Release Candidate 1 Released
PyOhio July 26-27, Call for Papers
PEP 779: Criteria for Supported Status for Free-Threaded Python – PEP 703 (Making the Global Interpreter Lock Optional in CPython), described three phases of development. This PEP outlines the criteria to move between phases.

Show Links:

Python Textual: Build Beautiful UIs in the Terminal – Textual is a Python library for building text-based user interfaces (TUIs) that support rich text, advanced layouts, and event-driven interactivity in the terminal. This tutorial showcases some of the ways you can design an appealing and engaging UI using Textual.
Bytes Objects: Handling Binary Data in Python – In this tutorial, you’ll learn about Python’s bytes objects, which help you process low-level binary data. You’ll explore how to create and manipulate byte sequences in Python and how to convert between bytes and strings. Additionally, you’ll practice this knowledge by coding a few fun examples.
Which Python GUI Library Should You Use in 2025? – This post compares the Python GUI libraries available in 2025, including PyQT, PySide, TKinter, and Kivy.
“Rules” That Terminal Programs Follow – The conventions that most terminal programs follow mean that you can more easily know how to control them. Julia’s post talks about “rules” that terminal programs tend to follow, and so should yours.

Projects:

Additional Links:

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A Decade of Automating the Boring Stuff With Python

Fri, 21 Mar 2025 12:00:00 +0000

What goes into updating one of the most popular books about working with Python? After a decade of changes in the Python landscape, what projects, libraries, and skills are relevant to an office worker? This week on the show, we speak with previous guest Al Sweigart about the third edition of “Automate the Boring Stuff With Python.”

Al shares his thoughts on teaching Python and writing books over the past decade. In this third edition, he shares several new projects and updates to existing ones. We discuss Python tools for transcription, text-to-speech, notifications, and data storage.

We talk about the importance of debugging and improvements to Python error messages. He also shares a collection of resources, including conference talks, small projects, and Python libraries.

Course Spotlight: Exploring Scopes and Closures in Python

In this Code Conversation video course, you’ll take a deep dive into how scopes and closures work in Python. To do this, you’ll use a debugger to walk through some sample code, and then you’ll take a peek under the hood to see how Python holds variables internally.

Topics:

00:00:00 – Introduction
00:01:46 – The Recurse Center and scrollart.org
00:05:11 – Third Edition of Automate the Boring Stuff With Python
00:07:32 – The types of projects covered in the new edition
00:09:44 – What was the original page count?
00:11:00 – Learning Python and it being perceived as magic
00:12:00 – PyCon US 2025 - Make Python Talk and Listen
00:14:22 – Text-to-speech with pyttsx3
00:19:31 – Generating notifications and messages with ntfy.sh
00:22:09 – Exploring SQLite
00:28:26 – Teaching enough to start building
00:31:03 – The Recursive Book of Recursion
00:32:45 – Do you see a change in the audience of Python learners
00:35:36 – Expectations put upon a new Python learner
00:40:28 – What changes has 10 years inspired for the book?
00:43:40 – Teaching things in a new order and debugging
00:47:31 – Video Course Spotlight
00:48:56 – Including simple projects
00:54:12 – Book release timeframe and pre-orders
00:58:26 – In-line metadata for Python script sharing
00:59:33 – What are you excited about in the world of Python?
01:01:56 – What do you want to learn next?
01:04:34 – How can people follow your work online?
01:05:19 – Thanks and goodbye

Show Links:

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Manage Projects With pyproject.toml & Explore Polars LazyFrames

Fri, 14 Mar 2025 12:00:00 +0000

How can you simplify the management of your Python projects with one file? What are the advantages of using LazyFrames in Polars? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent Real Python tutorial by Ian Currie about managing projects with a pyproject.toml file. This file simplifies Python project configuration by unifying package setup, managing dependencies, and streamlining builds.

Christopher continues his exploration of the Polars library by covering another Real Python tutorial about working with LazyFrames. He describes how LazyFrames don’t contain data but instead store a set of instructions known as a query plan.

We also share several other articles and projects from the Python community, including a news roundup, building a to-do app with Python and Kivy, working with DuckDB directly instead of using a DataFrame library, a discussion on fiction and nonfiction books about computer science, a terminal visual effects engine, and a full-stack platform for interactive data apps.

Course Spotlight: Everyday Project Packaging With pyproject.toml

In this Code Conversation video course, you’ll learn how to package your everyday projects with pyproject.toml. Playing on the same team as the import system means you can call your project from anywhere, ensure consistent imports, and have one file that’ll work for many build systems.

Topics:

00:00:00 – Introduction
00:02:00 – Happy Pi Day!
00:02:15 – Follow-up: Is BDD Dying?
00:03:32 – Django security releases issued: 5.1.7, 5.0.13 and 4.2.20
00:04:01 – Django 5.2 Beta 1 Released
00:04:11 – DjangoCon Africa Aug 2025 CFP
00:04:29 – Launching the PyCon US 2025 Schedule
00:04:48 – PyPy v7.3.19 Release
00:05:06 – Poetry 2.0.0 Released
00:05:34 – How to Manage Python Projects With pyproject.toml
00:12:10 – Build a To-Do App With Python and Kivy
00:16:22 – Mastering DuckDB When You’re Used to pandas or Polars
00:21:08 – Video Course Spotlight
00:22:42 – How to Work With Polars LazyFrames
00:27:41 – Fiction/Non-Fiction Books on the Topic of CS?
00:42:28 – preswald: Full-Stack Platform for Interactive Data Apps
00:45:52 – terminaltexteffects: Terminal Visual Effects Engine
00:47:59 – Thanks and goodbye

Follow-up:

News:

Django security releases issued: 5.1.7, 5.0.13 and 4.2.20 | Weblog | Django
Django 5.2 Beta 1 Released
DjangoCon Africa Aug 2025, Arusha, Tanzania, (Call for Proposals)
Launching the PyCon US 2025 Schedule – This post summarizes the schedule for PyConUS, including a summary of the keynote speakers, and updates on conference swag.
PyPy v7.3.19 Release
Poetry 2.0.0 Released

Show Links:

How to Manage Python Projects With pyproject.toml – Learn how to manage Python projects with the pyproject.toml configuration file. In this tutorial, you’ll explore key use cases of the pyproject.toml file, including configuring your build, installing your package locally, managing dependencies, and publishing your package to PyPI.
Build a To-Do App With Python and Kivy – “In this tutorial, you’ll go through a series of steps to build a basic To-Do app with Python, SQLite, and Kivy.”
Mastering DuckDB When You’re Used to pandas or Polars – Why use DuckDB / SQL at all if you’re used to DataFrames? This article makes the case for some reasons why, and shows how to perform some operations which in DataFrames are basic but in SQL aren’t necessarily obvious.
How to Work With Polars LazyFrames – In this tutorial, you’ll gain an understanding of the principles behind Polars LazyFrames. You’ll also learn why using LazyFrames is often the preferred option over more traditional DataFrames.

Discussion:

Project:

Additional Links:

Books Mentioned by Mr. Trudeau:

“The Cuckoo’s Egg” by Clifford Stoll
“Mythical Man Month” by Frederick Brooks
“Phoenix Project” by Gene Kim
“Dreaming in Code” by Scott Rosenberg
“Digital Fortress” by Dan Brown
“Godel Escher, Bach” by Douglas Hofstadlter
“A Philosophy of Software Design” by John Ousterhout’s
“I Hate The Internet” by Jarret Kobek
“Snow Crash” by Neal Stephenson
“Automate the Boring Stuff with Python” by Al Sweigart
“Django In Action” by Christopher Trudeau
“Refactoring Databases” by Scott W Ambler and Pramod J Sadalage
“The C Programming Language” by Dennis M. Ritchie and Brian W. Kernighan
“Open Source Licensing” by Lawrence Rosen
“The Quick Python Book” by Naomi R. Ceder
“Learn to Code By Solving Problems: A Python Programming Primer” by Daniel Zingaro
“Python Automation Cookbook” by Jaime Buelta

Books Mentioned by Mr. Bailey:

“The Little Schemer” by Daniel P. Friedman
“Zen and the Art of Motorcycle Maintenance” by Robert M. Pirsig
“Shop Class as Soulcraft: An Inquiry into the Value of Work” by Matthew B. Crawford
“Django for Beginners, APIs, and Professionals” by William S. Vincent
“Python Crash Course” by Eric Matthes
“Automate the Boring Stuff With Python” by Al Sweigart
“Fluent Python” by Luciano Ramalho
“Practices of the Python Pro” by Dane Hillard
“Daemon and Freedom™” by Daniel Suarez

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Eric Matthes: Maybe Don't Start With Unit Tests

Fri, 07 Mar 2025 12:00:00 +0000

Should you always start testing your code with unit tests? When does it make sense to look at integration or end-to-end testing as a first step instead? This week on the show, we speak with previous guest Eric Matthes about where to begin testing your code.

Eric is the author of the popular book Python Crash Course. Early in the development of the book, he decided to introduce testing and added a chapter on testing code with pytest.

Over the past couple of years, Eric has continued to consider when and where to test a project’s code. He thinks there are hazards to always starting with unit tests. The type of project and its audience should determine what kind of testing to employ initially.

We discuss using pytest to develop integration tests on multiple types of projects. We also explore fixtures and what goes into building a test suite. Eric also shares criteria for when and where it makes sense to add unit tests to a project.

Course Spotlight: Using Python’s assert to Debug and Test Your Code

In this course, you’ll learn how to use Python’s assert statement to document, debug, and test code in development. You’ll learn how assertions might be disabled in production code, so you shouldn’t use them to validate data. You’ll also learn about a few common pitfalls of assertions in Python.

Topics:

00:00:00 – Introduction
00:01:47 – Submitting talks to conferences
00:04:10 – Don’t start with unit tests!
00:07:35 – How did you start with testing?
00:11:30 – Example of a project needing tests
00:14:54 – Defining types of tests
00:16:44 – Integration vs end-to-end tests
00:19:09 – When should you build tests?
00:22:13 – Trade offs of integration vs unit tests
00:24:05 – Why is there push back on this idea?
00:27:36 – Video Course Spotlight
00:29:09 – Using pytest
00:33:24 – Transcripts project example
00:37:03 – py-image-border project
00:40:29 – Criteria for when you should write unit tests
00:48:51 – How to practice writing tests
00:50:28 – Building an integration test and pytest fixtures
00:55:05 – What’s in the test folder?
00:56:31 – Idea for a PyCon tutorial on implementing tests
00:57:29 – Other pytest advice and parametrization
01:01:13 – Caveats to not starting with unit tests
01:02:30 – pytest documentation and other advice
01:05:23 – How to reach Eric online
01:06:47 – What are you excited about in the world of Python?
01:08:23 – What do you want to learn next?
01:09:48 – What conferences are you attending?
01:10:06 – Thanks and goodbye

Show Links:

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Deciphering Python Jargon & Compiling Python 1.0

Fri, 28 Feb 2025 12:00:00 +0000

How do you learn the terms commonly used when speaking about Python? How is the jargon similar to other programming languages? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a Python glossary recently created by Trey Hunner. Trey describes it as an unofficial glossary and Python jargon file. We dig into the terms and colloquial language often used when describing Python.

We cover a blog post celebrating 31 years of Python by compiling Python 1.0. The piece walks through the hoops of finding the source code and standing up an old version of Debian. Once compiled, they open the REPL and find it surprisingly capable.

We also share several other articles and projects from the Python community, including release news, a Python enhancement proposal roundup, managing Django’s queue, a course about NumPy techniques including practical examples, getting platform-specific directories, detecting which shell is in use, and a project for sorted container types.

This episode is sponsored by Postman.

Course Spotlight: NumPy Techniques and Practical Examples

Topics:

00:00:00 – Introduction
00:02:42 – Python Release 3.14.0a5
00:02:54 – PyPy v7.3.18 Released
00:03:32 – Beautifulsoup 4.13 Released
00:04:13 – PEP 759: External Wheel Hosting (Withdrawn)
00:04:54 – PEP 2026: Calendar Versioning for Python (Rejected)
00:06:48 – PEP 739: Static Description File for Build Details (Accepted)
00:07:51 – PEP 765: Disallow Return/Break/Continue That Exit a Finally Block (Accepted)
00:09:01 – Python Terminology: An Unofficial Glossary
00:19:32 – Sponsor: Postman
00:20:28 – NumPy Techniques and Practical Examples
00:24:12 – Let’s Compile Python 1.0
00:28:55 – Video Course Spotlight
00:30:14 – Managing Django’s Queue
00:36:41 – platformdirs: Get Platform-Specific Dirs
00:39:57 – shellingham: Tool to Detect Surrounding Shell
00:41:02 – python-sortedcontainers: Python Sorted Container Type
00:41:58 – Thanks and goodbye

News:

Topics:

Python Terminology: An Unofficial Glossary – “Definitions for colloquial Python terminology (effectively an unofficial version of the Python glossary).”
NumPy Techniques and Practical Examples – In this video course, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.
Let’s Compile Python 1.0 – As part of the celebration of 31 years of Python, Bite Code compiles the original Python 1.0 and plays around with it.
Managing Django’s Queue – Carlton is one of the core developers of Django. This post talks about staying on top of the incoming pull-requests, bug fixes, and everything else in the development queue.

Projects:

Additional Links:

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Telling Effective Stories With Your Python Visualizations

Fri, 21 Feb 2025 12:00:00 +0000

How do you make compelling visualizations that best convey the story of your data? What methods can you employ within popular Python tools to improve your plots and graphs? This week on the show, Matt Harrison returns to discuss his new book “Effective Visualization: Exploiting Matplotlib & Pandas.”

As a data scientist and instructor, Matt has been teaching the concepts of managing tabular data and making visualizations for over 20 years. Matt shares his methodology for taking a basic plot and then telling a compelling story with it. We discuss why you should limit your plot types to a few that your audience is familiar with.

We cover the resources built into pandas and Matplotlib and some of the libraries’ limitations. Matt talks about the professionally produced plots that inspired him and the process of recreating them. He also answers questions about finding data sources to practice these techniques with.

This episode is sponsored by Postman.

Course Spotlight: Using plt.scatter() to Visualize Data in Python

In this course, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.

Topics:

00:00:00 – Introduction
00:02:57 – XGBoost book and interview
00:04:00 – Effective Visualization – Exploiting Matplotlib & pandas
00:04:27 – Why focus on pandas?
00:06:01 – Plotting inside of pandas
00:08:41 – How did you get involved in visualizations?
00:13:54 – Why write this book?
00:16:17 – Sponsor: Postman
00:17:09 – What are the plots you appreciate?
00:22:41 – Creating a methodology for plotting
00:24:24 – Color to spell out the story
00:27:50 – Limited and simple types of visualizations
00:31:34 – Explaining the story
00:37:19 – highlight-text library for matplotlib
00:39:02 – Video Course Spotlight
00:40:11 – Who is the audience?
00:43:19 – Why not include interactivity?
00:45:38 – Listing the references for the data
00:49:12 – Deciding on the examples and recipes
00:54:45 – Using existing visualizations as inspiration
00:55:41 – Matplotlib style sheets
00:57:54 – Finding sources of data to work with
01:04:17 – How to purchase the book
01:05:07 – What are you excited about in the world of Python?
01:06:33 – What do you want to learn next?
01:07:36 – How can people follow your work online?
01:08:04 – Thanks and goodbye

Show Links:

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Behavior-Driven vs Test-Driven Development & Using Regex in Python

Fri, 14 Feb 2025 12:00:00 +0000

What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

In this episode, we expand on our software testing discussion from two weeks ago by adding behavior-driven development concepts. Christopher describes how BDD correlates with test-driven development and how it fosters collaboration within a team. We discuss building acceptance tests written in plain language and a handy tool for creating them.

We also share several other articles and projects from the Python community, including a news roundup, using regular expressions in Python, dealing with missing data in Polars, monkey patching in Django, first steps with Playwright, 3D printing giant things with a Python jigsaw generator, and a query language for JSON.

This episode is sponsored by Postman.

Course Spotlight: Regular Expressions and Building Regexes in Python

In this course, you’ll learn how to perform more complex string pattern matching using regular expressions, or regexes, in Python. You’ll also explore more advanced regex tools and techniques that are available in Python.

Topics:

00:00:00 – Introduction
00:02:21 – PyOhio 2025 July 26-27, 2025 Announced
00:02:38 – Python 3.13.2 and 3.12.9 now available!
00:02:52 – Django bugfix releases issued: 5.1.6, 5.0.12, and 4.2.19
00:03:04 – DjangoCon Europe 2025 - Real Python Podcast
00:05:24 – How to Deal With Missing Data in Polars
00:10:29 – Monkey Patching Django
00:15:50 – Sponsor: Postman
00:16:42 – My First Steps With Playwright
00:20:48 – How to Use Regular Expressions in Python
00:25:55 – Video Course Spotlight
00:27:25 – TDD vs. BDD: What’s the Difference?
00:50:13 – 3D Printing Giant Things With a Python Jigsaw Generator
00:53:58 – jmespath.py: Query Language for JSON
00:55:58 – Thanks and goodbye

News:

Topics:

How to Deal With Missing Data in Polars – In this tutorial, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them.
Monkey Patching Django – The nanodjango project is a modification to the Django framework that lets you get started with a single file instead of the usual cookie-cutter directory structure. This is a detailed post explaining how nanodjango monkey patches Django to achieve this result.
Fake Django Objects With Factory Boy – The
My First Steps With Playwright – Playwright is a browser-based automation tool that can be used for web scraping or testing. This intro article shows you how to use the Python interface to access a page including using cookies.
How to Use Regular Expressions in Python – This post explores the basics of regular expressions in Python, as well as more advanced techniques. It includes real-world use cases and performance optimization strategies.

Discussion:

TDD vs. BDD: What’s the Difference? – Discover the key differences between TDD vs BDD, their workflows, tools, and best practices for developers.
Cucumber

Projects:

3D Printing Giant Things With a Python Jigsaw Generator – This is a long, detailed article on 3D printing objects too large for the printer bed. The author has created dovetail joints to assemble pieces together. He wrote a Python program to automatically split up the larger model files into the jigsaw pieces needed to build a final result.
jmespath.py: Query Language for JSON

Additional Links:

Polars — DataFrames for the new era
nanodjango: Full Django in a single file - views, models, API ,with async support. Automatically convert it to a full project.
factory_boy library is a tool for managing fixtures for your tests. This article shows you how to use it with Django.
trimesh 4.6.2 documentation
Email::RFC822::Address - Regex Recipe

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Charlie Marsh: Accelerating Python Tooling With Ruff and uv

Fri, 07 Feb 2025 12:00:00 +0000

Are you looking for fast tools to lint your code and manage your projects? How is the Rust programming language being used to speed up Python tools? This week on the show, we speak with Charlie Marsh about his company, Astral, and their tools, uv and Ruff.

Charlie started working on Ruff as a proof of concept, stating that Python tooling could be much faster. He had seen similar gains in JavaScript tools written in Rust. The project started as a speedy linter with a small ruleset. It’s grown to include code formatting and over 800 built-in linting rules.

Last year, the team at Astral started working on a Python package and project manager written in Rust. As a single tool, uv can replace pip, pip-tools, pipx, poetry, pyenv, and more. We discuss how uv can install and manage versions of Python and run scripts without thinking about virtual environments or dependencies.

Charlie talks about growing the team at Astral over the past couple of years. We also discuss the funding model Astral has adopted and sustaining open-source software.

This episode is sponsored by Postman.

Course Spotlight: Python Basics: Installing Packages With pip

Python’s standard library includes a whole buffet of useful packages, but sometimes you need to reach for a third-party library. That’s where pip comes in handy. In this video course, you’ll learn how to pip install packages.

Topics:

00:00:00 – Introduction
00:03:37 – How did you get involved in open source?
00:07:01 – Fostering a community around a project
00:11:32 – Python tooling could be much, much faster
00:15:45 – Changing the ergonomics of tooling
00:19:59 – What is ruff and what jobs can it do?
00:22:23 – How do you configure ruff?
00:26:02 – Where do the linting rules come from?
00:29:29 – Can you build your own rules?
00:31:28 – Performance difference for ruff
00:36:25 – Installing ruff
00:37:34 – The rustification of Python
00:40:52 – The initial features and release of uv
00:45:07 – Installing Python
00:47:50 – Taking over the python-build-standalone project
00:53:02 – Installation methods and suggestions
00:55:37 – Video Course Spotlight
00:57:07 – The project API
01:01:57 – Inline script metadata and PEP 723
01:06:49 – Installing tools with uvx
01:09:37 – Project management
01:11:20 – Astral as company and VC funding
01:19:23 – New static type checker
01:26:15 – What are you excited about in the world of Python?
01:27:12 – What do you want to learn next?
01:28:52 – How can people follow your work online?
01:29:34 – Thanks and goodbye

Show Links:

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Testing Your Python Code Base: Unit vs. Integration

Fri, 31 Jan 2025 12:00:00 +0000

What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems.

We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing.

This episode is sponsored by Postman.

Course Spotlight: Testing Your Code With pytest

In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.

Topics:

00:00:00 – Introduction
00:02:28 – Python news and releases
00:04:02 – From Default Line Charts to Journal-Quality Infographics
00:07:25 – PyViz: Python Tools for Data Visualization
00:09:25 – Why Is hash(-1) == hash(-2) in Python?
00:12:40 – Sponsor: Postman
00:13:32 – Data Cleaning in Data Science
00:19:29 – 10 Ways to Work With Large Files in Python
00:23:40 – Unit Testing vs. Integration Testing
00:29:17 – Does university curriculum cover this?
00:31:22 – Building tests into smaller projects
00:36:04 – Video Course Spotlight
00:37:30 – How does the approach differ with clients or larger-scale projects?
00:40:45 – How do tests act as documentation?
00:42:02 – Difficulties in building integration tests
00:45:24 – How do you limit the results of tests?
00:47:52 – klp: Lightweight CLI Viewer for Log Files
00:50:54 – freezegun: Mocks the datetime Module for Testing
00:53:11 – Thanks and goodbye

News:

Topics:

From Default Line Charts to Journal-Quality Infographics – “Everyone who has used Matplotlib knows how ugly the default charts look like.” In this series of posts, Vladimir shares some tricks to make your visualizations stand out and reflect your individual style.
PyViz: Python Tools for Data Visualization – This site contains an overview of all the different visualization libraries in the Python ecosystem. If you’re trying to pick a tool, this is a great place to better understand the pros and cons of each.
Why Is hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in CPython. This post examines how and discovers why this is the case.
Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.”
10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge.

Discussion:

Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python.

Project:

Additional Links:

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Simon Willison: Using LLMs for Python Development

Fri, 24 Jan 2025 12:00:00 +0000

What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.

Simon has been researching LLMs over the past two and a half years and documenting the results on his blog. He shares which models work best for writing Python versus JavaScript and compares coding tools and environments.

We discuss prompt engineering techniques and the first steps to take. Simon shares his enthusiasm for the usefulness of LLMs but cautions about the potential pitfalls.

Simon also shares how he got involved in open-source development and Django. He’s a proponent of starting a blog and shares how it opened doors for his career.

This episode is sponsored by Postman.

Course Spotlight: Advanced Python import Techniques

The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code.

Topics:

00:00:00 – Introduction
00:02:38 – How did you get involved in open source?
00:04:04 – Writing an XML-RPC library
00:04:40 – Working on Django in Lawrence, Kansas
00:05:31 – Started building open-source collection
00:06:52 – shot-scraper: taking automated screenshots of websites
00:08:09 – First experiences with LLMs
00:10:08 – 22 years of simonwillison.net
00:18:22 – Navigating the hype and criticism of LLMs
00:22:14 – Where to start with Python code and LLMs?
00:26:22 – Sponsor: Postman
00:27:13 – ChatGPT Canvas vs Code Interpreter
00:28:23 – Asking nicely, tricking the system, and tipping?
00:30:35 – More Code Interpreter and building a C extension
00:32:05 – More details on Canvas
00:36:55 – What is a workflow for developing using LLMs?
00:39:43 – Creating pieces of code vs a system
00:42:00 – Workout program for prompting and pitfalls
00:53:54 – Video Course Spotlight
00:55:14 – Why an SVG of a pelican riding a bicycle?
00:57:48 – Repeating a query and refining
01:03:00 – Working in an IDE or text editor
01:05:45 – David Crawshaw on writing code with LLMs
01:08:33 – Running an LLM locally to write code
01:14:02 – Staying out of the AGI conversation
01:16:07 – What are you excited about in the world of Python?
01:18:34 – What do you want to learn next?
01:19:53 – How can people follow your work online?
01:20:51 – Thanks and goodbye

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Principles for Considering Your Python Tooling

Fri, 17 Jan 2025 12:00:00 +0000

What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article about effective Python developer tooling. Instead of digging into a list of current libraries, we talk about the principles you must consider before making decisions for your team. We cover common pitfalls teams get mired in and how to avoid them.

We also share several other articles and projects from the Python community, including a news roundup, a huge collection of the top Python libraries of 2024, programming sockets in Python, merging dictionaries, a Django quiz, mistakes to avoid in production, building a Portal sentry turret, a powerful TUI expense tracker, and a pure-Python async rendering engine.

Course Spotlight: Managing Dependencies With Python Poetry

Learn how Python Poetry can help you start new projects, maintain existing ones, and master dependency management.

Topics:

00:00:00 – Introduction
00:01:53 – DjangoCon US 2025 (Chicago, Sept 8-12) Announced
00:02:38 – Textualize 1.0 Released
00:03:15 – Top Python Libraries of 2024
00:07:07 – Programming Sockets in Python
00:11:56 – Merging Dictionaries in Python
00:17:03 – Django Quiz 2024
00:17:55 – Confessions of a Django Dev: Mistakes To Avoid in Production
00:18:40 – Sentry Turret Straight Out of the ‘Portal’ Franchise
00:20:00 – Video Course Spotlight
00:21:26 – Effective Python Developer Tooling in December 2024
00:41:13 – Bagels: Powerful TUI Expense Tracker
00:43:42 – htmy: Async, Pure-Python Rendering Engine
00:45:41 – Thanks and goodbye

News:

Show Links:

Top Python Libraries of 2024 – For the past ten years, Tyrolabs has put together a list of their favorite Python libraries of the year. This list includes ten general purpose libraries and ten more specific to AI/ML and Data.
Programming Sockets in Python – In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.
Merging Dictionaries in Python – There are multiple ways of merging two or more dictionaries in Python. This post teaches you how to do it and how to deal with corner cases like duplicate keys.
Django Quiz 2024 – Adam runs a quiz on Django at his Django London meetup. He’s shared it so you can try it yourself. Test how much you know about your favorite web framework.
Confessions of a Django Dev: Mistakes To Avoid in Production – This post covers some of the common mistakes you might make when taking a Django project into production.
Sentry Turret Straight Out of the ‘Portal’ Franchise – “Reckless_commenter has created a Raspberry Pi-powered sentry turret that looks and sounds just like the creepy machines found in the ‘Portal’ franchise.” Logic and sound effects managed through the PyGame library.

Discussion:

Effective Python Developer Tooling in December 2024 – This post talks about how tooling doesn’t solve all your problems when you code, especially with a team. It outlines some principles to implement, and bad practices to avoid when writing Python.
Mistakes engineers make in large established codebases - Sean Goedecke

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Building New Structures for Learning Python

Fri, 10 Jan 2025 12:00:00 +0000

What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn Python.

Stephen has recently joined the team, bringing years of online training expertise. He discusses our new offering of cohort-based courses, which combine live expert instruction, hands-on exercises, and a supportive community.

Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python.

Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months.

This episode is sponsored by Sentry.

Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP

In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one.

Topics:

00:00:00 – Introduction
00:02:29 – What Stephen has been up to
00:03:31 – What’s new for Martin
00:04:07 – Bringing on new team members
00:06:09 – Cohort-based courses
00:19:25 – Sponsor: Sentry
00:20:27 – Restructured and new learning paths
00:30:50 – Video Course Spotlight
00:32:19 – New Real Python Books
00:38:57 – A destination for learning
00:40:46 – Quizzes for tutorials and courses
00:44:58 – Video courses and updating content
00:47:52 – Code Mentor
00:49:45 – Code challenges
00:51:06 – Thanks and goodbye

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PyCoder's Weekly 2024 Top Articles & Missing Gems

Fri, 03 Jan 2025 12:00:00 +0000

PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2024. Christopher Trudeau is back on the show this week to help wrap it all up by sharing some highlights and uncovering a few missing gems from the pile.

We share the top links that PyCoder’s readers explored this year and uncover trends across all the articles and stories. We also highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year.

We hope you enjoy this review! We look forward to bringing you another year filled with great Python news, articles, topics, and projects.

Course Spotlight: Programming Sockets in Python

In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.

Topics:

00:00:00 – Introduction
00:01:47 – New releases and updates
00:03:07 – PyCon US 2025 Registration Open
00:03:18 – PyCon Austria 2025 Call for Papers
00:03:36 – PSF Year End Fundraiser - Membership Drive
00:04:31 – Mr. Trudeau on Flying High with Flutter
00:05:29 – We’re on Bluesky - follow us!
00:07:44 – Build Captivating Display Tables in Python With Great Tables
00:08:45 – Overview of the Module itertools
00:09:23 – Customize VS Code Settings
00:10:34 – Modern Good Practices for Python Development
00:11:55 – Asyncio Event Loop in Separate Thread
00:12:38 – Python Protocols: Leveraging Structural Subtyping
00:13:06 – Thoughts on the top links
00:22:29 – Video Course Spotlight
00:23:40 – Why I’m Switching From pandas to Polars
00:29:29 – Lessons Learned Reinventing the Python Notebook
00:32:47 – What’s a Python Hashable Object?
00:36:10 – uv: Python Packaging in Rust
00:38:26 – CI/CD for Python With GitHub Actions
00:40:07 – Thanks and goodbye

News:

Exploring Modern Sentiment Analysis Approaches in Python

Fri, 20 Dec 2024 12:00:00 +0000

What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.

Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.

In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.

This week’s episode is brought to you by Sentry.

Course Spotlight: Learn Text Classification With Python and Keras

In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.

Topics:

00:00:00 – Introduction
00:02:31 – Conference talks in 2024
00:04:23 – Background on sentiment analysis and studying feelings
00:07:09 – What led you to study emotions?
00:08:57 – Dimensional emotion classification
00:10:42 – Different types of sentiment analysis
00:14:28 – Lexicon-based approaches
00:17:50 – VADER - Valence Aware Dictionary and sEntiment Reasoner
00:19:41 – TextBlob and subjectivity scoring
00:21:48 – Sponsor: Sentry
00:22:52 – Measuring sentiment of New Year’s resolutions
00:27:28 – Lexicon-based approaches links for experimenting
00:28:35 – Multiple language support in lexicon-based packages
00:35:23 – Machine learning techniques
00:39:20 – Tools for this approach
00:42:54 – Video Course Spotlight
00:44:15 – Advantages to the machine learning models approach
00:45:55 – Large language model approach
00:48:44 – Encoder vs decoder models
00:52:09 – Comparing the concept of fine-tuning
00:56:49 – Is this a recent development?
00:58:08 – Ways to practice with these techniques
01:00:10 – Do you find this to be a promising approach?
01:07:45 – Resources to practice with all the techniques
01:11:06 – Upcoming conference talks
01:11:56 – Thanks and goodbye

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Good Python Programming Practices When New to the Language

Fri, 06 Dec 2024 12:00:00 +0000

What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an older forum post from a new Python user who came from Perl. We suggest checking out PEP 8, or as it’s commonly known, “The Style Guide for Python Code.” We provide advice about installing Python, avoiding common pitfalls, learning how scope is managed, and taking advantage of a collection of Real Python resources.

We share several other articles and projects from the Python community, including a new Python release, practical NumPy examples and exercises, considering targets of for loops, exploring Python dependency management, checking package compatibility with free-threading and subinterpreters, an experimental filesystem navigator in Textual, and a background workers reference implementation in Django.

This episode is sponsored by AssemblyAI.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

Learn how to write high-quality, readable code by using the Python style guidelines laid out in PEP 8. Following these guidelines helps you make a great impression when sharing your work with potential employers and collaborators. This course outlines the key guidelines laid out in PEP 8. It’s aimed at beginner to intermediate programmers.

Topics:

00:00:00 – Introduction
00:02:17 – Python 3.14.0 Alpha 2 Released
00:02:35 – Take the 2024 Django Developers Survey
00:03:17 – NumPy Practical Examples: Useful Techniques
00:07:09 – Loop Targets
00:09:19 – Python Dependency Management Is a Dumpster Fire
00:23:15 – Sponsor: AssemblyAI
00:24:00 – Package Compatibility With Free-Threading and Subinterpreters
00:27:02 – Suggestions for good programming practices?
00:37:59 – Video Course Spotlight
00:39:24 – terminal-tree: Experimental Filesystem Navigator in Textual
00:43:56 – django-tasks: Background Workers Reference Implementation
00:49:44 – Thanks and goodbye

News:

Topics:

NumPy Practical Examples: Useful Techniques – In this tutorial, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.
Loop Targets – Loop assignment allows you to assign to a dict item in a for loop. This post covers what that means and that it is no more costly than regular assignment.
Python Dependency Management Is a Dumpster Fire – Managing dependencies in Python can be a bit of a challenge. This deep dive article shows you all the problems and how the problems are mitigated if not solved.
Package Compatibility With Free-Threading and Subinterpreters – This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features.

Discussion:

Projects:

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marimo: Reactive Notebooks and Deployable Web Apps in Python

Fri, 29 Nov 2024 12:00:00 +0000

What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).

Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

Topics:

00:00:00 – Introduction
00:02:06 – Akshay’s background and studies
00:04:14 – Work at Google and PhD program
00:06:29 – Sharing notebooks
00:08:18 – Starting work on marimo 2 years ago
00:12:48 – Avoiding notebook issues and building a DAG
00:18:39 – The difference of reactivity
00:20:39 – What is a marimo notebook?
00:23:39 – Video Course Spotlight
00:24:50 – Reproducibility and managing package requirements
00:27:49 – Using decorators for cells
00:30:23 – Writing a design document before any coding
00:34:08 – Interactivity and UI widgets
00:38:20 – Design decisions and built-in widgets
00:42:05 – Creating a deployable web application
00:44:34 – Exploring examples and tutorials
00:46:13 – Supporting DataFrame libraries with narwhals
00:48:00 – Migrating from a Jupyter notebook
00:52:02 – Working with cells and not running code
00:54:30 – A couple favorite tutorials
00:56:17 – What are you excited about in the world of Python?
00:57:39 – What do you want to learn next?
00:59:34 – How can people follow the project and yourself?
01:00:12 – Thanks and goodbye

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The Joy of Tinkering & Python Free-Threading Performance

Fri, 22 Nov 2024 12:00:00 +0000

What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the joy of tinkering with Python as a way to keep your developer skills sharp. We dig into our techniques for continuing to learn and build projects.

Christopher shares an article that examines the performance of Python 3.13’s free-threading features. This piece uses a clever example to measure how the new features behave with large datasets and parallelization.

We share several other articles and projects from the Python community, including a group of new releases, common use cases and examples for Python closures, finding the opposite of cloud-native, Python’s soft keywords, a command-line utility for taking automated screenshots of websites, and putting the Django admin in the terminal with Textual.

This episode is sponsored by Windsurf.

Course Spotlight: Python Inner Functions

In this step-by-step course, you’ll learn what inner functions are in Python, how to define them, and what their main use cases are. You’ll see how to write helper functions, create closure factory functions, and how to add behavior to existing functions with decorators.

Topics:

00:00:00 – Introduction
00:02:18 – Django Bugfix Release Issued: 5.1.3
00:02:46 – Pillow Release 11.0.0
00:03:14 – Flask Version 3.1.0
00:03:30 – PyCon US 2025 (Pittsburgh) Call for Proposals
00:03:46 – Python Closures: Common Use Cases and Examples
00:09:20 – State of Python 3.13 Performance: Free-Threading
00:15:42 – Sponsor: Windsurf
00:16:32 – Opposite of Cloud Native Is…?
00:22:36 – Python’s Soft Keywords
00:24:50 – Video Course Spotlight
00:26:11 – The Joy of Tinkering
00:38:33 – shot-scraper: A command-line utility for taking automated screenshots of websites
00:41:13 – django-admin-tui: Django Admin in the Terminal!
00:42:37 – django-admin-dracula: Dracula Themes for the Django Admin
00:44:21 – Thanks and goodbye

News:

Show Links:

Python Closures: Common Use Cases and Examples – In this tutorial, you’ll learn about Python closures. A closure is a function-like object with an extended scope. You can use closures to create decorators, factory functions, stateful functions, and more.
State of Python 3.13 Performance: Free-Threading – This article does a comparison between code in single threaded, threaded, and multi-process versions under Python 3.12, 3.13, and 3.13 free-threaded with the GIL on and off.
Opposite of Cloud Native Is…? – Michael (from Talk Python fame) introduces the concept of “stack-native” as the opposite of “cloud-native”, and how it applies to Python web apps. Building applications with just enough full-stack building blocks to run reliably with minimal complexity, rather than relying on a multitude of cloud services.
Python’s soft keywords – Python includes soft keywords: tokens that are important to the parser but can also be used as variable names. This article shows you what a soft keyword is and how to find them in Python 3.12 (both the easy and hard way).

Discussion:

Habits of Great Software Engineers - The Joy of Tinkering

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Maintaining the Foundations of Python & Cautionary Tales

Fri, 15 Nov 2024 12:00:00 +0000

How do you build a sustainable open-source project and community? What lessons can be learned from Python’s history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

Paul has been an organizer in the Python community almost from the beginning. He shares how the project has navigated through multiple sponsors. We talk about the early governance models and the formation of the Python Software Foundation.

We contrast this journey with the current drama unfolding in the WordPress community. We discuss the potential problems of having a benevolent dictator for life. We also dig into sponsorship models and ways to get companies to give back to the open-source projects they rely on.

This episode is sponsored by Sentry.

Course Spotlight: Using pandas to Make a Gradebook in Python

With this course and Python project, you’ll build a script to calculate grades for a class using pandas. The script will quickly and accurately calculate grades from a variety of data sources. You’ll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics.

Topics:

00:00:00 – Introduction
00:01:55 – Meeting Jodie Burchell at PyCon 2022
00:02:51 – A non-traditional path into open-source
00:07:09 – The current turmoil around WordPress
00:13:49 – Keeping things fair in the age of extraction
00:16:03 – Sponsor: Sentry
00:17:07 – Early Python organizing history and conservation
00:20:41 – The Python Software Activity precursor to PSF
00:24:14 – Creating the Python Software Foundation
00:27:24 – Keeping the perfect distance of business and project
00:28:13 – Who gets to capture the value from open-source?
00:31:07 – Sponsorships becoming more common
00:33:24 – BDFL to a steering council
00:34:58 – Video Course Spotlight
00:36:16 – What is Plone?
00:38:11 – Starting in Python and finding community
00:50:07 – Companies contributing
00:53:16 – Examples of how JetBrains contributes back
00:55:41 – Understanding the support system
00:58:09 – Talking to decision makers
01:00:07 – Python 1994 talk and continuation
01:01:49 – What are you excited about in the world of Python?
01:03:06 – What do you want to learn next?
01:04:17 – How can people follow your work online?
01:07:16 – Thanks and goodbye

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New PEPs: Template Strings & External Wheel Hosting

Fri, 08 Nov 2024 12:00:00 +0000

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a set of recent Python Enhancement Proposals (PEPs). The idea of template strings has been under consideration for a while, and PEP 750 describes a new way forward. PEP 759 proposes a way for projects on PyPI to safely host resources on external sites using a new package upload format called a .rim file.

We share several other articles and projects from the Python community, including what didn’t make the headlines about Python 3.13, solving Sudoku with Python packaging, what’s sweet about Python’s syntactic sugar, creating database-generated columns using SQLite and Django, a discussion about mentoring, an adaptive web scraper, and a debugging tool for HTTP(S) client requests.

This episode is sponsored by Sentry.

Course Spotlight: Using Pydantic to Simplify Python Data Validation

Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on video course, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.

Topics:

00:00:00 – Introduction
00:02:08 – Python 3.14.0 Alpha 1 Released
00:02:38 – Python 3.13, What Didn’t Make the Headlines
00:05:23 – What’s up Python? 3.13 is out, t-strings look awesome
00:10:21 – Sponsor: Sentry
00:11:25 – Sudoku in Python Packaging
00:14:29 – Syntactic Sugar: Why Python Is Sweet and Pythonic
00:22:31 – Database generated columns: Django & SQLite
00:27:14 – Video Course Spotlight
00:28:39 – Mentors
00:42:23 – Scrapling: Lightning-Fast, Adaptive Web Scraping for Python
00:44:14 – httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests
00:46:04 – Request for project submissions to PyCoders
00:46:59 – Thanks and goodbye

News:

Python 3.14.0 Alpha 1 Released

Show Links:

Python 3.13, What Didn’t Make the Headlines – Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb, improvements to shutil, and small additions to the asyncio library.
What’s up Python? 3.13 is out, t-strings look awesome, dep groups come in handy…
Sudoku in Python Packaging – Simon writes about a Sudoku solver written by Konstin that uses the Python packaging mechanisms to do Sudoku puzzles. The results are output using a requirements.txt file, where sudoku-0-3==5 represents the (0,3) cell’s answer of 5.
Syntactic Sugar: Why Python Is Sweet and Pythonic – In this tutorial, you’ll learn what syntactic sugar is and how Python uses it to help you create more readable, descriptive, clean, and Pythonic code. You’ll also learn how to replace a given piece of syntactic sugar with another syntax construct.
Database generated columns: Django & SQLite – An introduction to database generated columns, using SQLite and the new GeneratedField added in Django 5.0

Discussion:

Mentors – Ryan just finished his second round of mentoring with the Djangonaut.Space program. This post talks about how you can help your mentor help you and how to be a good mentor.

Projects:

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PySheets: Spreadsheets in the Browser Using PyScript

Fri, 01 Nov 2024 12:00:00 +0000

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book “Communication for Engineers.”

As a software engineer, Chris has worked at IBM, Google, Uber, and several financial institutions. He speaks about developer productivity and communication skills as an engineer. We begin our conversation by digging into his background, his approach to building engineering teams, and strategies for improving communication.

Chris’ idea for PySheets is to have Excel inside Python with everything running locally in your browser. He was inspired by the success of Jupyter Notebooks but wanted to develop a tool more suited to a spreadsheet’s non-linear graph structure.

PySheets is built to run locally in the user’s browser, taking advantage of PyScript. We discuss finding the right solution for storing data in the browser and developing a graphic toolkit to create the UI. Chris also shares the novel method he found to get the interface up and running while the larger assets are loading.

This episode is sponsored by Sentry.

Course Spotlight: Understanding Python’s Global Interpreter Lock (GIL)

Python’s Global Interpreter Lock, or GIL, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. In this video course, you’ll learn how the GIL affects the performance of your Python programs.

Topics:

00:00:00 – Introduction
00:02:25 – Background with building engineering teams
00:08:43 – Communication for Engineers book
00:16:17 – What do customers want and experiences at IBM
00:24:28 – Starting the development of PySheets
00:27:19 – Working with the DOM
00:29:41 – Success of Jupyter notebooks
00:35:46 – Sponsor: Sentry
00:36:52 – Little Toolkit for PyScript
00:43:24 – Finding funding
00:46:58 – Building a product before selling
00:52:27 – Video Course Spotlight
00:53:46 – Finding the right data storage in IndexedDB
01:01:57 – Exploring the trial page and extensibility
01:08:26 – Contributing to the project or forking
01:11:56 – What are you excited about in the world of Python?
01:16:20 – What do you want to learn next?
01:17:25 – How can people follow your work online?
01:18:05 – Thanks and goodbye

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Python Getting Faster and Leaner & Ideas for Django Projects

Fri, 25 Oct 2024 12:00:00 +0000

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article about Python’s recent performance improvements. The piece covers the specialized adaptive interpreter and explains what those terms mean. It also includes details about the experimental feature of the Just-In-Time (JIT) compiler added in 3.13.

We dig into a collection of Django projects you can use to practice and develop your skills. The projects ramp up from detailed beginner tutorials to more advanced projects with guidelines on how to get started. We also discuss a collection of popular websites that use Django.

We share several other articles and projects from the Python community, including a batch of recent Python Enhancement Protocols (PEPs), a couple of Python releases, using DuckDB in the browser with Pyodide, building a contact book app with Textual, generating a tiny status page with a Python script, and a grep-like tool that understands code.

This episode is sponsored by AssemblyAI.

Course Spotlight: Building a Site Connectivity Checker

In this video course, you’ll build a Python site connectivity checker for the command line. While building this app, you’ll integrate knowledge related to making HTTP requests with standard-library tools, creating command-line interfaces, and managing concurrency with asyncio and aiohttp.

Topics:

00:00:00 – Introduction
00:03:11 – PEP 777: How to Re-Invent the Wheel
00:04:22 – PEP 758: Allow except and except* Expressions Without Parentheses
00:04:51 – PEP 760: No More Bare Excepts (Withdrawn)
00:05:42 – PEP 735: Dependency Groups in pyproject.toml
00:06:29 – PEP 761: Deprecating PGP Signatures for CPython Artifacts
00:06:59 – Python 3.12.7 Released
00:07:12 – Incremental GC and Pushing Back the 3.13.0 Release
00:09:10 – DuckDB in the Browser With Pyodide
00:15:35 – Sponsor: AssemblyAI
00:16:18 – Build a Contact Book App With Python, Textual, and SQLite
00:21:55 – Django Project Ideas
00:28:42 – Video Course Spotlight
00:30:00 – In the Making of Python Fitter and Faster
00:35:13 – tinystatus: Tiny Status Page Generated by a Python Script
00:38:06 – srgn: Grep-Like Tool That Understands Code
00:42:01 – Thanks and goodbye

News:

PEP 777: How to Re-Invent the Wheel – “The current wheel 1.0 specification was written over a decade ago, and has been extremely robust to changes in the Python packaging ecosystem… this PEP prescribes compatibility requirements on future wheel revisions.”
PEP 758: Allow except and except* Expressions Without Parentheses – “This PEP proposes to allow unparenthesized except and except* blocks in Python’s exception handling syntax. Currently, when catching multiple exceptions, parentheses are required around the exception types.”
PEP 760: No More Bare Excepts (Withdrawn)
PEP 735: Dependency Groups in pyproject.toml (Accepted)
PEP 761: Deprecating PGP Signatures for CPython Artifacts – Since Python 3.11.0, CPython has provided two verifiable digital signatures for all CPython artifacts: PGP and sigstore. This PEP proposes moving to sigstore as the only way of signing artifacts.
Python 3.12.7 Released
Python 3.13.0 Released
Incremental GC and Pushing Back the 3.13.0 Release – Some last minute performance considerations delayed the release of Python 3.13 with one of the features being backed out.

Show Links:

DuckDB in the Browser With Pyodide – Learn how to run DuckDB in an in-browser Python environment to enable simple querying on remote files, interactive documentation, and easy to use training materials.
Build a Contact Book App With Python, Textual, and SQLite – In this tutorial, you’ll be guided step by step through the process of building a basic contact book application. You’ll use Python and Textual to build the application’s text-based user interface (TUI), and then use SQLite to manage the database.
Django Project Ideas – Looking to experiment or build your portfolio? Discover creative Django project ideas for all skill levels, from beginner apps to advanced full-stack projects.
In the Making of Python Fitter and Faster – This post details how Python’s recent performance improvements work under the hood. It covers changes to the interpreter, better memory management, and the newly experimental JIT compiler.

Projects:

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Narwhals: Expanding DataFrame Compatibility Between Libraries

Fri, 18 Oct 2024 12:00:00 +0000

How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

Narwhals is a project aimed at library maintainers rather than end users. We discuss how the added compatibility benefits users by supporting modern features like lazy evaluation. We cover several projects Marco has been working with to implement Narwhals, including Altair, scikit-lego, and Ibis.

We also discuss how Marco started contributing to open-source projects. Marco has contributed to both pandas and Polars, which helps explain his interest in growing compatibility between libraries. He also offers advice on making your first contribution.

This episode is sponsored by CodeRabbit.

Course Spotlight: Differences Between Python’s Mutable and Immutable Types

In this video course, you’ll learn how Python’s mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code.

Topics:

00:00:00 – Introduction
00:02:02 – Euro SciPy 2024 and sprints
00:04:04 – How did you get involved in open source?
00:07:18 – Finding a good issue to get started
00:09:25 – Discord and open-source projects
00:11:12 – Who would you describe Narwhals?
00:16:47 – Working on Polars
00:19:17 – Apache Arrow and a data interchange protocol
00:22:55 – Sponsor: CodeRabbit
00:23:55 – Digging into eager vs lazy
00:27:04 – Ibis DataFrame library
00:28:57 – What do libraries need from Narwhals?
00:34:57 – The scikit-lego library
00:37:15 – Video Course Spotlight
00:38:45 – Other libraries interested in Narwhals
00:41:56 – Compatibility policy
00:45:18 – What should an end user expect?
00:46:32 – Have other projects that attempted this?
00:47:54 – Keeping the project light and pure Python
00:49:32 – Contributors and how to get involved
00:54:42 – What are you excited about in the world of Python?
00:57:18 – What do you want to learn next?
00:59:05 – How can people follow your work online?
00:59:27 – Thanks and goodbye

Show Links:

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Exploring the New Features of Python 3.13

Fri, 11 Oct 2024 12:00:00 +0000

Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, “Python 3.13: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.13” covers the topics from the article and shows the new features in action.

Geir Arne and Christopher dug into the release to create code examples of the new features for the tutorial and course. We look at the options for disabling the Global Interpreter Lock (GIL) and enabling the Just-in-Time (JIT) compiler. We also discuss the new interactive interpreter, better error messages, multiple improvements to static typing, and additional performance improvements.

We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.13.

This is episode is sponsored by Nvidia.

Course Spotlight: What’s New in Python 3.13

In this video course, you’ll learn about the new features in Python 3.13. You’ll take a tour of the new REPL and error messages and see how you can try out the experimental free threading and JIT versions of Python 3.13 yourself.

Topics:

00:00:00 – Introduction
00:03:14 – A Modern REPL
00:08:54 – Making the Global Interpreter Lock Optional in CPython
00:11:33 – JIT Compilation
00:15:48 – More improved error messages
00:18:30 – Sponsor: NVIDIA
00:19:13 – Marking deprecations using the type system
00:21:09 – Type Defaults for Type Parameters
00:22:44 – Narrowing types with TypeIs
00:25:24 – TypedDict: Read-only items
00:27:50 – Random command line interface
00:29:54 – New copy.replace()
00:33:43 – Video Course Spotlight
00:34:55 – Pathlib and globbing
00:39:33 – Stripping docstrings
00:41:28 – Import improvements
00:41:56 – Dynamically import non-code files
00:42:23 – Adding iOS as a supported platform
00:43:32 – More consistency with local namespace
00:44:30 – Support for deprecation in argparse
00:45:00 – Better entry points for breakpoint or set_trace
00:46:08 – Removing dead batteries
00:47:43 – When to upgrade to 3.13?
00:53:19 – core.py podcast
00:54:14 – Thanks and goodbye

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Using Virtual Environments in Docker & Comparing Python Dev Tools

Fri, 27 Sep 2024 12:00:00 +0000

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent post by Hynek Schlawack about building Python projects using Docker containers. Hynek argues for using virtual environments for these projects, like developing a local one. He’s found that keeping your code in an isolated, well-defined location and structure avoids confusion and complexity.

We also discuss our development setups, including Python versions, code editors, virtual environment practices, terminals, and customizations. We dig into how your programming history affects the tools you use.

We share several other articles and projects from the Python community, including a group of new releases, addressing the “why” in comments, comparing a data science workflow in Python and R, removing common problems from CSV files, and a project for creating HTML tables in Django.

This episode is sponsored by InfluxData.

Course Spotlight: Advanced Python import Techniques

The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code.

Topics:

00:00:00 – Introduction
00:02:55 – Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20
00:03:26 – Python Release Python 3.13.0rc2
00:04:07 – Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16
00:04:36 – Polars Has a New Lightweight Plotting Backend
00:05:49 – Why I Still Use Python Virtual Environments in Docker
00:11:37 – How to Use Conditional Expressions With NumPy where()
00:15:55 – Sponsor: InfluxData
00:16:39 – PythonistR: A Match Made in Data Heaven
00:23:44 – Why Not Comments
00:26:48 – Video Course Spotlight
00:28:10 – Discussion: Personal development setups
00:51:01 – csv_trimming: Remove Common Ugliness From CSV Files
00:53:01 – django-tables2: Create HTML Tables in Django
00:54:39 – Thanks and goodbye

News:

Show Links:

Polars Has a New Lightweight Plotting Backend – Polars 1.6 allows you to natively create beautiful plots without pandas, NumPy, or PyArrow. This is enabled by Narwhals, a lightweight compatibility layer between dataframe libraries.
Why I Still Use Python Virtual Environments in Docker – Hynek often gets challenged when he suggests the use of virtual environments within Docker containers, and this post explains why he still does.
How to Use Conditional Expressions With NumPy where() – This tutorial teaches you how to use the where() function to select elements from your NumPy arrays based on a condition. You’ll learn how to perform various operations on those elements and even replace them with elements from a separate array or arrays.
PythonistR: A Match Made in Data Heaven – In data science you’ll sometimes hear a debate between R and Python. Cosima says ‘why not choose both?’ She outlines a data pipeline that uses the best tool for each job.
Why Not Comments – This post talks about why you might want to include information in your code comments about why you didn’t take a particular approach.

Discussion:

Projects:

Additional Links:

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Thriving as a Developer With ADHD

Fri, 20 Sep 2024 12:00:00 +0000

What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast “ADHD For the Win!”

Chris struggled with productivity early in his career as a developer. He shares systems and strategies he’s discovered to harness the focusing power of ADHD.

We discuss time management, meetings, and maintaining productivity in a hectic world. Chris also shares resources for learning more about defining ADHD, self-evaluation, and how to keep getting things done.

This episode is sponsored by InfluxData.

Course Spotlight: Build a GUI Calculator With PyQt and Python

In this video course, you’ll learn how to create graphical user interface (GUI) applications with Python and PyQt. Once you’ve covered the basics, you’ll build a fully functional desktop calculator that can respond to user events with concrete actions.

Topics:

00:00:00 – Introduction
00:02:30 – Defining ADHD and how it aligns with coding
00:05:47 – Analogy for focus
00:06:51 – Can you sense the change in focus?
00:07:46 – The challenge of meetings
00:11:45 – Tips for managing time
00:15:44 – Capturing notes and defragging
00:18:48 – Sponsor: InfluxData
00:19:33 – Downtime and interruptions
00:25:26 – Remote work and focus
00:33:16 – Sitting still and meetings
00:37:39 – Video Course Spotlight
00:39:13 – Anything worth doing is worth doing poorly
00:47:36 – Prototypes and working on interesting things
00:50:26 – Deadlines and pomodoro timers
00:54:21 – Have your symptoms changed over time?
00:56:18 – Starting ADHDftw.com
00:59:12 – Decision to keep podcast episodes short
01:00:01 – Deciding on medication
01:02:02 – Resources available
01:03:29 – What motivates you to continue to learn programming?
01:04:06 – What do you want to learn next?
01:04:55 – What are other ways to follow your work online?
01:05:28 – Thanks and goodbye

Show Links:

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Support the podcast & join our community of Pythonistas

Configuring Git Pre-Commit Hooks & Estimating Software Projects

Fri, 13 Sep 2024 12:00:00 +0000

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a trio of articles by previous guest Stefanie Molin about Git pre-commit hooks. Across the series, she provides step-by-step instructions for building your own hooks, managing them, and learning how they operate.

We discuss the process of estimating software development projects. We dig into the art of “guesstimation,” rough calculation, and napkin math. Christopher shares his experience in agile scenarios and measuring projects by story counts.

We share several other articles and projects from the Python community, including a news roundup, 10 Python programming optimization techniques, and building a blog in Django using GraphQL & Vue. We also explore experimenting with Python’s preprocessor, a toolkit for writing UIs in PyScript, and a couple of projects for working with Django Admin.

This episode is sponsored by InfluxData.

Course Spotlight: Python mmap: Doing File I/O With Memory Mapping

In this video course, you’ll learn how to use Python’s mmap module to improve your code’s performance when you’re working with files. You’ll get a quick overview of the different types of memory before diving into how and why memory mapping with mmap can make your file I/O operations faster.

Topics:

00:00:00 – Introduction
00:02:28 – Python Top Language of 2024
00:02:59 – Python Developers Survey 2023 Results
00:03:46 – How Pre-Commit Works
00:10:00 – Build a Blog Using Django, GraphQL, and Vue
00:13:38 – Sponsor: InfluxData
00:14:23 – 10 Python Programming Optimization Techniques
00:22:45 – Python’s Preprocessor
00:26:42 – Video Course Spotlight
00:28:16 – You don’t have to guess to estimate
00:44:18 – LTK is a little toolkit for writing UIs in PyScript
00:49:06 – django-admin-action-forms: Forms for Django Admin
00:52:09 – django-public-admin: A Public and Read-Only Django Admin
00:53:24 – Thanks and goodbye

News:

Python Top Language of 2024 – “Python continues to cement its overall dominance, buoyed by things like popular libraries for hot fields such as A.I.” Read the article to see where other languages have placed.
Python Developers Survey 2023 Results – Official Python Developers Survey 2023 Results by Python Software Foundation and JetBrains: more than 25k responses from almost 200 countries.

Show Links:

How Pre-Commit Works – As a user of pre-commit hooks, do you know what happens when you run pre-commit install or why you have to run it in the first place? How does pre-commit actually work with Git? In this article, Stefanie takes you behind the scenes of how your pre-commit setup works.
How to Set Up Pre-Commit Hooks - Stefanie Molin
How to Create a Pre-Commit Hook - Stefanie Molin
Build a Blog Using Django, GraphQL, and Vue – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use.
10 Python Programming Optimization Techniques – Optimization should be your last step, but once you’re there, just what can you do? This article covers ten different techniques that address memory size and code performance.
Python’s Preprocessor – Every now and then you hear outrageous claims such as “Python has no preprocessor,” well it is there if you’re willing to dig deep enough. Learn how to hack Python’s compile step.

Discussion

Projects:

Additional Links:

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Astrophysics and Astronomy With Python & PyCon Africa 2024

Fri, 06 Sep 2024 12:00:00 +0000

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a pair of his recent Real Python video courses about exploring astronomy and astrophysics with Python. Throughout the courses, you’ll get to practice using a variety of data science libraries, such as NumPy, Matplotlib, pandas, pint, and Astropy.

We speak with Mannie Young who is the Organizing Committee Chair of PyCon Africa. Real Python is excited to be a contributing sponsor of this year’s conference. Mannie discusses reinvigorating a continent-spanning conference after a multiyear hiatus. He also talks about introducing Python to students and new developers across Africa through PyClubs, PyLadies, and PyData programs.

We share several other articles and projects from the Python community, including a news roundup, logging in Python, understanding operator precedence, reconciling why it only works on your machine, a fast way to create an HTML app, and a tool for deep inspection of Python objects.

This episode is sponsored by InfluxData.

Course Spotlight: Sorting Dictionaries in Python: Keys, Values, and More

In this video course, you’ll learn how to sort Python dictionaries. By the end, you’ll be able to sort by key, value, or even nested attributes. But you won’t stop there, you’ll also measure the performance of variations when sorting and compare different key-value data structures.

Topics:

00:00:00 – Introduction
00:03:05 – PEP 750: Tag Strings for Domain-Specific Languages
00:05:19 – PEP 752: Package Repository Namespaces
00:07:32 – PyCon US 2024 Recap and Recording Release
00:08:01 – Logging in Python
00:14:57 – It Works on My Machine. Why?
00:17:33 – Python’s Operator Precedence
00:20:54 – Exploring Astrophysics in Python With pandas and Matplotlib
00:24:03 – Using Astropy for Astronomy With Python
00:26:37 – Sponsor: InfluxData
00:27:22 – fasthtml: The Fastest Way to Create an HTML App
00:32:50 – wat: Deep Inspection of Python Objects
00:38:08 – PyCon Africa 2024
00:40:47 – What goes into re-energizing a conference?
00:44:20 – Talks and speakers
00:46:58 – Video Course Spotlight
00:48:29 – How did you get involved?
00:52:19 – PyClubs and growing Python education
00:58:41 – What industries are using Python in Ghana?
01:00:20 – Sponsorship and support
01:01:51 – Travel in and outside the continent
01:04:23 – Call to action
01:05:05 – Thanks and goodbye

News:

PEP 750: Tag Strings for Domain-Specific Languages (Added)
PEP 752: Package Repository Namespaces (Added)
PyCon US 2024 Recap and Recording Release – PyCon US 2024 had a record breaking attendance with over 2,700 in-person tickets sold. This article is a recap from the conference runners and links to all the available recordings.

Show Links:

Logging in Python – If you use Python’s print() function to get information about the flow of your programs, then logging is the natural next step for you. This tutorial will guide you through creating your first logs and show you ways to curate them to grow with your projects.
It Works on My Machine. Why? – A list of things to check when something works on your computer but not on someone else’s.
Python’s Operator Precedence – Stephen uses a story-telling style to explain how operator precedence works in Python.
Exploring Astrophysics in Python With pandas and Matplotlib – This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint.
Using Astropy for Astronomy With Python – This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star.

Projects:

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Exploring Robotics and Python Through Electronic Projects

Fri, 23 Aug 2024 12:00:00 +0000

Are you interested in learning robotics with Python? Can physical electronics-based projects grow a child’s interest in coding? This week on the show, we speak with author Marwan Alsabbagh about his book “Build Your Own Robot - Using Python, CRICKIT, and Raspberry Pi.”

Marwan discusses his two conferences talks about building electronics projects with his children. He provides advice on equipment and techniques to make learning Python engaging.

We explore his robotics project and the literal balancing act of designing a robot around the Raspberry Pi. Marwan shares his successes and disappointments while working to incorporate computer vision, joystick controls, and voice commands.

This episode is sponsored by Mailtrap.

Course Spotlight: Python Debugging With pdb

In this hands-on course, you’ll learn the basics of using pdb, Python’s interactive source code debugger. pdb is a great tool for tracking down hard-to-find bugs, and it allows you to fix faulty code more quickly.

Topics:

00:00:00 – Introduction
00:02:14 – How did you get started with Python and electronics?
00:04:27 – Snow globe intruder alert system
00:06:57 – Things to keep in mind with a child
00:12:50 – Challenges in teaching a child Python
00:16:34 – Sponsor: Mailtrap
00:17:11 – What are other projects you’ve tried?
00:21:12 – Powering the robot project
00:24:56 – Putting together the robot librarian talk
00:29:47 – Was there any friction teaching kids robotics?
00:32:47 – Adding the complexity of a Raspberry Pi
00:38:27 – Video Course Spotlight
00:39:48 – Hardware components of the robot
00:41:51 – Thinking about access to the equipment
00:45:37 – Assembling the robot project?
00:49:14 – Various control systems
00:54:42 – What experience level is required with Python?
00:55:40 – What concepts were you excited to share?
00:57:59 – Do you think Python is a good language for robotics?
00:59:21 – MicroPython Cookbook
01:00:07 – What are projects you tried that didn’t work out?
01:03:01 – What are you excited about in the world of Python?
01:04:04 – What do you want to learn next?
01:04:56 – How can people follow your work online?
01:05:19 – Thanks and goodbye

Show Links:

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Packaging Data Analyses & Using pandas GroupBy

Fri, 16 Aug 2024 12:00:00 +0000

What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss Joshua Cook’s recent article “How I Use Python to Organize My Data Analyses.” The article covers how his process for building data analysis projects has evolved and now incorporates modern Python packaging techniques.

Christopher shares his recent video course on grouping real-world data with pandas. The course offers a quick refresher before digging into how to use pandas GroupBy to manipulate, transform, and summarize data.

We also share several other articles and projects from the Python community, including a news roundup, working with JSON data in Python, running an Asyncio event loop in a separate thread, knowing the why behind a system’s code, a retro game engine for Python, and a project for vendorizing packages from PyPI.

This episode is sponsored by Mailtrap.

Course Spotlight: pandas GroupBy: Grouping Real World Data in Python

In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs.

Topics:

00:00:00 – Introduction
00:02:18 – Setuptools Breaks Things, Then Fixes Them
00:04:57 – PEP 751: A File Format to List Python Dependencies
00:07:04 – Python 3.13.0 Release Candidate 1 Released
00:07:15 – Python Insider: Python 3.12.5 released
00:07:22 – Django 5.1 released - Django Weblog
00:07:27 – Django security releases issued: 5.0.8 and 4.2.15
00:07:49 – How I Use Python to Organize My Data Analyses
00:13:45 – Sponsor: Mailtrap
00:14:21 – pandas GroupBy: Grouping Real World Data in Python
00:20:33 – Working With JSON Data in Python
00:25:01 – Asyncio Event Loop in Separate Thread
00:30:33 – Video Course Spotlight
00:31:47 – Habits of great software engineers
00:49:17 – pyxel: A Retro Game Engine for Python
00:52:36 – python-vendorize: Vendorize Packages From PyPI
00:54:18 – Thanks and goodbye

News:

Setuptools Breaks Things, Then Fixes Them – This post is Bite Code’s monthly summary, but the lead story happened just days ago. In line with a 7 year old deprecation, setuptools finally removed the ability to call its test command. Many packages promptly broke. The following day the change was undone.
PEP 751: A File Format to List Python Dependencies for Installation Reproducibility (New) – This PEP proposes a new file format for dependency specification to enable reproducible installation in a Python environment.
Python 3.13.0 Release Candidate 1 Released
Python Insider: Python 3.12.5 released
Django 5.1 released - Django Weblog
Django security releases issued: 5.0.8 and 4.2.15 - Django Weblog

Show Links:

How I Use Python to Organize My Data Analyses – This is a description of how Joshua uses Python in a package-centric way to organize his approach to data analyses. This is a system he has evolved while working on his computational biology Ph.D. and working in industry.
pandas GroupBy: Grouping Real World Data in Python – In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs.
Working With JSON Data in Python – In this tutorial, you’ll learn how to read and write JSON-encoded data in Python. You’ll begin with practical examples that show how to use Python’s built-in “json” module and then move on to learn how to serialize and deserialize custom data.
Asyncio Event Loop in Separate Thread – Typically, the asyncio event loop runs in the main thread, but as that is the one used by the interpreter, sometimes you want the event loop to run in a separate thread. This article talks about why and how to do just that.

Discussion:

Habits of great software engineers

Projects:

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Learning Through Building the Black Python Devs Community

Fri, 09 Aug 2024 12:00:00 +0000

What hurdles must be cleared when starting an international organization? How do you empower others in a community by sharing responsibilities? This week on the show, we speak with Jay Miller about Black Python Devs.

Jay shares how the idea of forming a community began through attending conferences. They wanted to welcome more black developers into the Python community. We discuss the introduction of Black Python Devs as part of their PyCon 2024 keynote presentation.

Jay explains working with a few key people to build the group’s foundations. They talk about the difficulty of letting other people share in the responsibilities and ownership as the membership grew. We also discuss the advantages of partnering with a non-profit organization.

This episode is sponsored by InfluxData.

Course Spotlight: Interacting With REST APIs and Python

In this video course, you’ll learn how to use Python to communicate with REST APIs. You’ll learn about REST architecture and how to use the requests library to get data from a REST API. You’ll also explore different Python tools you can use to build REST APIs.

Topics:

00:00:00 – Introduction
00:02:50 – PyCon 2024 Keynote
00:06:02 – New role at Aiven
00:11:32 – Nobody knows what Dev Rel is
00:19:43 – Podcasting about productivity
00:24:12 – Sponsor: InfluxData
00:24:57 – Starting Black Python Devs
00:33:11 – Distinct perspectives and problems
00:37:10 – Partnering with Gnome Foundation
00:40:31 – What were hurdles in starting Black Python Devs?
00:45:31 – Video Course Spotlight
00:47:01 – What do you wish you knew before you started?
00:50:56 – What’s your latest win?
00:53:28 – Helping people prepare for jobs and new roles
00:58:03 – What’s your call to action?
01:00:26 – What are you excited about in the world of Python?
01:03:48 – How do you stay motivated to keep learning Python?
01:06:19 – What do you want to learn next?
01:09:02 – How can people follow your work online?
01:11:02 – Thanks and goodbye

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

Using GraphQL in Django With Strawberry & Prototype Purgatory

Fri, 02 Aug 2024 12:00:00 +0000

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django’s battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent tutorial for building GraphQL APIs in Django using the Python library Strawberry. The tutorial digs into creating a project, defining models, and creating GraphQL queries and mutations using Strawberry.

We discuss a blog post from Nat Bennet titled “Why do prototypes suck?” We dig into the common pitfalls of building prototypes and the misconceptions between developers and end users.

We also share several other articles and projects from the Python community, including a news roundup, using HTMX with FastAPI, creating an unbelievably stupid airline Wi-Fi package, extracting wisdom from conference videos, writing pixel images to the terminal, and a macOS app for Jupyter Notebooks.

This episode is sponsored by Mailtrap.

Course Spotlight: Building a URL Shortener With FastAPI and Python

In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.

Topics:

00:00:00 – Introduction
00:02:27 – Python 3.13.0 Beta 4 Released
00:03:15 – Using HTMX With FastAPI
00:09:51 – Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights
00:13:37 – Sponsor: Mailtrap
00:14:13 – “Extracting Wisdom” From Conference Videos
00:22:34 – Developing GraphQL APIs in Django With Strawberry
00:30:01 – Video Course Spotlight
00:31:33 – Why do prototypes suck?
00:42:53 – Satyrn: macOS App for Jupyter Notebooks
00:46:41 – rich-pixels: A Rich-compatible library for writing pixel images
00:48:23 – Thanks and goodbye

News:

Python 3.13.0 Beta 4 Released

Topics:

Using HTMX With FastAPI – This tutorial looks at how use HTMX with FastAPI by creating a simple todo web app and deploying it on Render.
Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights – Deep in a need to procrastinate on a flight between London and San Francisco, Robert discovered that changing his name on an airline’s frequent flyer account was free over the plane’s WiFi. What’s a developer to do? Work on their tickets? No, create an entire TCP/IP protocol using this loophole. The result is the PySkyWiFi package.
“Extracting Wisdom” From Conference Videos – There are so many conferences and so many videos, you can’t possibly watch them all. This post shows you how to extract information to summarize a talk so you can quickly decide what you want to watch.
Developing GraphQL APIs in Django With Strawberry – This tutorial details how to integrate GraphQL with Django using Strawberry.

Discussion:

Why do prototypes suck?

Project:

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Build Captivating Display Tables in Python With Great Tables

Fri, 26 Jul 2024 12:00:00 +0000

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library.

Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library.

We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday.

This episode is sponsored by Mailtrap.

Course Spotlight: Graph Your Data With Python and ggplot

In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way.

Topics:

00:00:00 – Introduction
00:02:00 – Michael’s background in open source
00:04:07 – Rich’s background in open source
00:05:27 – Advice for someone starting out
00:08:55 – What do you mean by the term “display” table
00:11:32 – What components were missing from other tables?
00:13:31 – Using examples to explain features
00:16:09 – Why was there an absence of this functionality in Python?
00:19:35 – A progressive approach and the grammar of tables
00:21:26 – Sponsor: Mailtrap
00:22:01 – The design philosophy of great tables
00:25:31 – Nanoplots, spark lines, and column spanners
00:27:06 – Building a gallery of examples
00:28:56 – Heat mapping cells and automatically adjusting text color
00:32:54 – Output formats for the tables
00:34:46 – Building in accessibility
00:36:55 – Dependencies
00:37:42 – What is the common workflow?
00:41:39 – Video Course Spotlight
00:43:15 – Adding graphics
00:46:41 – Using a table contest to get examples
00:49:47 – quartodoc and documenting the project
00:55:00 – Tidy Tuesday and data science community
01:00:29 – What are you excited about in the world of Python?
01:03:46 – What do you want to learn next?
01:08:05 – How can people follow the work you do online?
01:09:57 – Thanks and goodbye

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Constraint Programming & Exploring Python's Built-in Functions

Fri, 19 Jul 2024 12:00:00 +0000

What are discrete optimization problems? How do you solve them with constraint programming in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects

Christopher discusses an article about constraint programming using Python. He describes the fundamentals and how the problems resemble logic problems you may have experienced in school. The article shows how to solve a weekly work scheduling problem using the open-source CP-SAT package.

We discuss Leodanis Pozo Ramos’s recent tutorial, “Python’s Built-in Functions: A Complete Exploration.” These functions are available for use directly in your code without importing.

We also share several other articles and projects from the Python community, including a news roundup, spotting ships with satellites, grappling with Apple’s App Store rejecting Python applications, considering changes to Python’s security model, discussing pivoting from one development path to another, prettifying Jinja and Django templates, and generating static sites with Python.

This episode is sponsored by Sentry.

Course Spotlight: Parallel Iteration With Python’s zip() Function

In this course, you’ll learn how to use the Python zip() function to solve common programming problems. You’ll learn how to traverse multiple iterables in parallel and create dictionaries with just a few lines of code.

Topics:

00:00:00 – Introduction
00:02:35 – Polars 1.0 Released
00:03:26 – Psycopg 3.2 Released
00:04:06 – Django security releases issued: 5.0.7 and 4.2.14
00:04:40 – PyBay 2024 Call for Proposals
00:05:16 – Python’s Built-in Functions: A Complete Exploration
00:12:10 – Satellites Spotting Ships
00:16:02 – Sponsor: Sentry
00:17:09 – Python Grapples With Apple App Store Rejections
00:20:27 – Python’s Security Model After the xz-utils Backdoor
00:25:38 – Video Course Spotlight
00:26:56 – Constraint Programming Using CP-SAT and Python
00:31:40 – Any Web Devs Successfully Pivoted to AI/ML Development?
00:43:12 – aurora: Static Site Generator Implemented in Python
00:45:14 – Running Prettier Against Django or Jinja Templates
00:46:58 – Thanks and goodbye

News:

Show Links:

Python’s Built-in Functions: A Complete Exploration – In this tutorial, you’ll learn the basics of working with Python’s numerous built-in functions. You’ll explore how you can use these predefined functions to perform common tasks and operations, such as mathematical calculations, data type conversions, and string manipulations.
Satellites Spotting Ships – Umbra Space has released a data set consisting of satellite based radar images of shipping. This article from Mark shows you how to grab the data, visualize, and annotate it.
Python Grapples With Apple App Store Rejections – A string that is part of the urllib parser module in Python references a scheme for apps that use the iTunes feature to install other apps, which is disallowed. Auto scanning by Apple is rejecting any app that uses Python 3.12 underneath. A solution has been proposed for Python 3.13.
Python’s Security Model After the xz-utils Backdoor – The backdoor introduced to the xz-utils compression project through social engineering was one of the topics at the Python Language Summit. Participants discussed what can be done to prevent similar social engineering attacks on the Python source.
Constraint Programming Using CP-SAT and Python – Constraint programming is the process of looking for solutions based on a series of restrictions, like employees over 18 who have worked the cash before. This article introduces the concept and shows you how to use open source libraries to write constraint solving code.

Discussion:

Any Web Devs Successfully Pivoted to AI/ML Development?

Projects:

aurora: Static Site Generator Implemented in Python
Running Prettier Against Django or Jinja Templates – “Prettier” is a JavaScript based linting tool for templates. For folks not familiar with the world of npm, it can be a bit daunting to get it going. Simon fiddled with it so you don’t have to and posted how he got it working on his system.

Additional Links:

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Digging Into Graph Theory in Python With David Amos

Fri, 12 Jul 2024 12:00:00 +0000

Have you wondered about graph theory and how to start exploring it in Python? What resources and Python libraries can you use to experiment and learn more? This week on the show, former co-host David Amos returns to talk about what he’s been up to and share his knowledge about graph theory in Python.

David started a Ph.D. program studying mathematics, with a focus on graph theory. Though life interrupted his pursuit after three years, he is still passionate about the subject. He’s been using these skills to create documentation and teach users as part of RelationalAI’s education team.

David has also been exploring the Julia programming language. He wrote about it on his blog, created videos, and started a podcast on the topic. He shares his excitement about learning new techniques from different languages and how these ideas can enhance your coding in Python.

This episode is sponsored by Sentry.

Course Spotlight: Defining Python Constants for Code Maintainability

In this video course, you’ll learn how to properly define constants in Python. By coding a bunch of practical example, you’ll also learn how Python constants can improve your code’s readability, reusability, and maintainability.

Topics:

00:00:00 – Introduction
00:02:09 – What have you been up to?
00:03:31 – Exploring the Julia language
00:07:24 – Where do you see Julia being used?
00:10:17 – Cross pollination of languages
00:12:45 – Connecting with RelationalAI
00:16:33 – Sponsor: Sentry
00:17:42 – Digging into graph theory
00:21:54 – Edges as connections
00:24:55 – Defining terms
00:31:30 – Storing graph information
00:41:55 – Applications once the graph is built
00:49:07 – Video Course Spotlight
00:50:40 – Additional resources to learn more
00:53:59 – What are you excited about in the world of Python?
00:58:05 – What do you want to learn next?
01:00:25 – How can people follow your work online?
01:02:55 – Thanks and goodbye

Show Links:

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Support the podcast & join our community of Pythonistas

Python Doesn't Round Numbers the Way You Might Think

Fri, 05 Jul 2024 12:00:00 +0000

Does Python round numbers the same way you learned back in math class? You might be surprised by the default method Python uses and the variety of ways to round numbers in Python. Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his recent video course, “Rounding Numbers in Python.” He covers rounding bias and how to avoid introducing it into your dataset. We dig into the various rounding strategies and how to implement them in Python.

We also share several other articles and projects from the Python community, including a news roundup, a fast Python linter for error-free and maintainable code, the decline of the user interface, more thoughts on Python in Excel, a discussion about calendar versioning for Python, a financial database as a Python module, and a project to prettify the colors of your terminal user interfaces.

This episode is sponsored by Sentry.

Course Spotlight: Rounding Numbers in Python

In this video course, you’ll learn about the mistakes you might make when rounding numbers and how to best manage or avoid them. It’s a great place to start for the early to intermediate Python developer who’s interested in using Python for finance, data science, or scientific computing.

Topics:

00:00:00 – Introduction
00:02:06 – NumPy 2.0.0 Release Notes
00:02:52 – Python 3.13.0 beta 3 released
00:03:05 – Announcing the PSF Board Candidates for 2024!
00:03:27 – Prohibiting Outlook Email Domains
00:04:31 – Ruff: A Python Linter for Error-Free and Maintainable Code
00:09:31 – Sponsor: Sentry
00:10:35 – The Decline of the User Interface
00:19:14 – My Thoughts on Python in Excel
00:26:30 – Rounding Numbers in Python
00:30:53 – Video Course Spotlight
00:32:13 – PEP 2026: Calendar Versioning for Python
00:42:37 – Financial Database as a Python Module
00:45:34 – prettypretty: Build Awesome Terminal User Interfaces
00:47:48 – Thanks and goodbye

News:

NumPy 2.0.0 Release Notes — NumPy v2.0 Manual – The long awaited 2.0 release of NumPy landed this week. Not all the docs are up to date yet, but this final draft of the release notes shows you what is included.
Python Insider: Python 3.13.0 beta 3 released
Python Software Foundation News: Announcing the PSF Board Candidates for 2024!
Prohibiting Outlook Email Domains – Due to an inordinate amount of bot accounts coming from outlook.com and hotmail.com, PyPI has disallowed new account sign-ups with email addresses from these domains.

Show Links:

Ruff: A Python Linter for Error-Free and Maintainable Code – Ruff is an extremely fast, modern linter with a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for other linting and formatting tools, like Pylint, isort, and Black. It’s no surprise it’s quickly becoming one of the most popular Python linters.
The Decline of the User Interface – “Software has never looked cooler, but user interface design and user experience have taken a sharp turn for the worse.”
My Thoughts on Python in Excel – Microsoft’s new Python in Excel functionality was released almost a year ago. Having now had time to play with it, Felix gives his take.
Rounding Numbers in Python – In this video course, you’ll learn about the mistakes you might make when rounding numbers and how to best manage or avoid them. It’s a great place to start for the early to intermediate Python developer who’s interested in using Python for finance, data science, or scientific computing.

Discussion:

PEP 2026: Calendar Versioning for Python – This PEP proposes updating the versioning scheme for Python to include the calendar year. This aims to make the support lifecycle clear by making it easy to see when a version was first released, and easier to work out when it will reach end of life (EOL).
Associated discussion
Semantic Versioning 2.0.0 - Semantic Versioning
Calendar Versioning — CalVer

Projects:

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Creating a Guitar Synthesizer & Generating WAV Files With Python

Fri, 28 Jun 2024 12:00:00 +0000

What techniques go into synthesizing a guitar sound in Python? What higher-level programming and Python concepts can you practice while building advanced projects? This week on the show, we talk with Real Python author and core team member Bartosz Zaczyński about his recent step-by-step project, Build a Guitar Synthesizer: Play Musical Tablature in Python.

Bartosz shares how he had built an early prototype of the guitar synth using a different language. He describes recreating the Karplus-Strong algorithm in Python to create the plucked guitar sounds. We discuss teaching advanced Python concepts while guiding readers through constructing a complete project.

We also dig into reading and writing WAV files in Python, using Spotify’s pedalboard library, implementing Poetry in a project, and designing a file format for guitar tablature.

This episode is sponsored by Mailtrap.

Course Spotlight: Playing and Recording Sound in Python

In this course, you’ll learn about libraries that can be used for playing and recording sound in Python, such as PyAudio and python-sounddevice. You’ll also see code snippets for playing and recording sound files and arrays, as well as for converting between different sound file formats.

Topics:

00:00:00 – Introduction
00:02:04 – Reading and writing WAV files in Python
00:04:48 – What did you want to learn about the topic?
00:08:02 – Finding new ways to explain Python concepts
00:09:52 – Turning audio into plots and visualizations
00:12:11 – Using Python for synthesis and the guitar synth project
00:13:19 – What is your music background?
00:18:36 – Sponsor: Mailtrap
00:19:11 – First prototype of the project
00:22:23 – What does the project cover?
00:25:08 – Audio examples of the output
00:26:31 – Digging into the algorithm
00:31:40 – Convolution reverb
00:34:15 – Physical modeling and pedalboard
00:38:08 – Video Course Spotlight
00:39:31 – What Python concepts are you practicing?
00:41:11 – Using Python poetry
00:43:00 – Why use YAML for the TAB files?
00:46:13 – What does it mean to be a polyglot programmer?
00:48:37 – Potential upcoming Real Python resources
00:52:10 – How can people follow your work online?
00:52:30 – What do you want to learn next?
00:54:56 – Thanks and goodbye

Show Links:

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Python's Command-Line Utilities & Music Information Retrieval Tools

Fri, 21 Jun 2024 12:00:00 +0000

What are the built-in Python modules that can work as useful command-line tools? How can these tools add more functionality to Windows machines? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article by Trey Hunner about Python’s extensive collection of command-line utilities? The piece digs into general-purpose tools that format JSON data or start a simple web server and additional utilities for working with your Python code from the terminal.

We cover a set of Jupyter Notebooks for teaching and learning the art of music processing and Music Information Retrieval (MIR). The notebooks are resources for working through the textbook, “Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications.”

We also share several other articles and projects from the Python community, including a news roundup, a discussion of CRUD operations, a description of Python’s built-in bytes sequence, favorite essays on development and programming, Python resources for working with Excel, and a project for creating finite state machines in Python.

This episode is sponsored by APILayer.

Course Spotlight: Binary, Bytes, and Bitwise Operators in Python

In this course, you’ll learn how to use Python’s bitwise operators to manipulate individual bits of data at the most granular level. With the help of hands-on examples, you’ll see how you can apply bitmasks and overload bitwise operators to control binary data in your code.

Topics:

00:00:00 – Introduction
00:02:21 – Python 3.12.4 Released
00:02:52 – Python 3.13.0 Beta 2 Released
00:03:01 – PEP 712 Rejected
00:04:18 – Django in Action - Mr. Trudeau’s Book has Launched!
00:06:23 – What Are CRUD Operations?
00:10:12 – Python’s Many Command-Line Utilities
00:14:04 – Sponsor: APILayer
00:14:55 – Notebooks for Fundamentals of Music Processing
00:22:55 – bytes: The Lesser-Known Python Built-in Sequence
00:26:57 – Video Course Spotlight
00:28:34 – Essays on Programming I Think About a Lot
00:41:28 – Python Resources for Working With Excel
00:46:13 – Python Finite State Machines Made Easy
00:50:10 – Thanks and goodbye

News:

Python 3.12.4 Released – See the full list of changes in this release
Python 3.13.0 Beta 2 Released
PEP 712 Rejected – This Python Enhancement Proposal “Adding a ‘converter’ parameter to dataclasses.field” was determined to have an insufficient number of use cases.
Django in Action

Show Links:

What Are CRUD Operations? – CRUD operations are the cornerstone of application functionality. Whether you access a database or interact with a REST API, you usually want to create, retrieve, update, and delete data. In this tutorial, you’ll explore how CRUD operations work in practice.
Python’s Many Command-Line Utilities – This article describes every command-line tool included with Python, each of which can be run with python -m module_name.
Notebooks for Fundamentals of Music Processing – This is a collection of Python Notebooks for teaching and learning the fundamentals of music processing. Examples include illustrations, sound samples, math, and more.
bytes: The Lesser-Known Python Built-in Sequence – The bytes data type looks a bit like a string, but it isn’t a string. This article explores it and also looks at the main Unicode encoding, UTF-8

Discussion:

Essays on Programming I Think About a Lot – A collection of essays on software from a variety of sources. Content includes how to choose your tech stack, products, abstractions, and more.
Falsehoods programmers believe about time - Infinite Undo
Falsehoods programmers believe about email
Falsehoods programmers believe about geography – Thias の blog
awesome-falsehood: 😱 Falsehoods Programmers Believe In

Projects:

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Detecting Outliers in Your Data With Python

Fri, 14 Jun 2024 12:00:00 +0000

How do you find the most interesting or suspicious points within your data? What libraries and techniques can you use to detect these anomalies with Python? This week on the show, we speak with author Brett Kennedy about his book “Outlier Detection in Python.”

Brett describes initially getting involved with detecting outliers in financial data. He discusses various applications and techniques in security, manufacturing, quality assurance, and fraud. We also dig into the concept of explainable AI and the differences between supervised and unsupervised learning.

This episode is sponsored by APILayer.

Course Spotlight: Using k-Nearest Neighbors (kNN) in Python

In this video course, you’ll learn all about the k-nearest neighbors (kNN) algorithm in Python, including how to implement kNN from scratch. Once you understand how kNN works, you’ll use scikit-learn to facilitate your coding process.

Topics:

00:00:00 – Introduction
00:01:56 – Describing the book
00:03:22 – How did you get involved in outlier detection?
00:06:50 – Initially looking at the data to spot errors
00:08:22 – Amount of fraud and financial errors
00:09:50 – Understanding the nature of the outliers
00:12:15 – Industries that would be interested in detection
00:18:21 – Sponsor: APILayer.com
00:19:15 – Who is the intended audience for the book?
00:22:16 – Differences between supervised vs unsupervised learning
00:25:48 – Autonomous vehicles detecting anomalous imagery
00:29:08 – What is explainable AI?
00:36:21 – Video Course Spotlight
00:37:43 – Detecting an outlier across multiple columns
00:44:32 – Detection of LLM and bot activity
00:49:49 – Proving you are a human checkbox
00:52:25 – What are Python libraries for outlier detection?
00:53:57 – Creating synthetic data to work through examples
00:57:10 – Tools developed and described in the book
01:01:29 – How to find the book
01:02:27 – What are you excited about in the world of Python?
01:04:55 – What do you want to learn next?
01:05:52 – How can people follow your work online?
01:06:16 – Thanks and goodbye

Show Links:

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Decomposing Software Problems & Avoiding the Trap of Clever Code

Fri, 07 Jun 2024 12:00:00 +0000

How do you effectively break a software problem into individual steps? What are signs you’re writing overly clever code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an article about de-warping images of book pages. We both found the piece a good study on decomposing a complex software problem.

Christopher discusses an article titled “Clever code is probably the worst code you could write.” Early in a programming career, it’s easier to write complex and difficult-to-read code. The real challenge is progressing towards writing clearer and readable code.

We also share several other articles and projects from the Python community, including a news roundup, what the __pycache__ folder is for in Python, what’s new in Django 5.1, a discussion about software engineering hiring and firing, a project for setting up repeated tasks, and a simple way to create reusable template components in Django.

This episode is sponsored by Sentry.

Course Spotlight: Efficient Iterations With Python Iterators and Iterables

Topics:

00:00:00 – Introduction
00:02:18 – PEP 667: Consistent Views of Namespaces (Accepted)
00:03:08 – PEP 649 Re-targeted to 3.14
00:03:50 – Untold Stories From 6 Years Working on Python Packaging
00:04:38 – What Is the __pycache__ Folder in Python?
00:09:57 – Sponsor: Sentry
00:11:04 – What’s New in Django 5.1
00:17:48 – Page Dewarping
00:26:55 – Video Course Spotlight
00:28:26 – Clever Code Is Probably the Worst Code You Could Write
00:33:19 – Software Engineering Hiring and Firing
00:51:22 – Metronomes: An Easy Way to Set Up Regular Tasks
00:52:26 – django-web-components: Create reusable template components in Django
00:54:03 – Thanks and goodbye

News:

PEP 667: Consistent Views of Namespaces (Accepted)
PEP 649 Re-targeted to 3.14 – Python Enhancement Proposal 649: Deferred Evaluation Of Annotations Using Descriptors has been re-targeted to the Python 3.14 release
Untold Stories From 6 Years Working on Python Packaging – Sumana gave the closing keynote address at PyCon US this year and this posting shares all the links and references from the talk.

Show Links:

What Is the __pycache__ Folder in Python? – In this tutorial, you’ll explore Python’s __pycache__ folder. You’ll learn about when and why the interpreter creates these folders, and you’ll customize their default behavior. Finally, you’ll take a look under the hood of the cached .pyc files.
What’s New in Django 5.1 – Django 5.1 has gone alpha so the list of features targeting this release has more or less solidified. This article introduces you to what is coming in Django 5.1.
Page Dewarping – This article shows the techniques behind a page flattening algorithm. It starts with images of a book’s page which are curled from the spine of the book, and creates a resulting PDF that is a flat version.
Clever Code Is Probably the Worst Code You Could Write – When you come across a clever bit of code, it is hard not to admire it, but often times, clear, readable code is the hardest code to write.

Discussion:

Software Engineering Hiring and Firing – This article is a deep dive on the hiring and firing practices in the software field, and unlike most articles focuses on senior engineering roles. It isn’t a “first job” post, but a “how the decision process works” article.

Projects:

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Building Python Unit Tests & Exploring a Data Visualization Gallery

Fri, 31 May 2024 12:00:00 +0000

How do you start adding unit tests to your Python code? Can the built-in unittest framework cover most or all of your needs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We dig into a recent tutorial by Leodanis Pozo Ramos about writing unit tests using Python’s unittest. The tutorial covers organizing your tests, exploring assert methods, creating test fixtures, and debugging failing tests.

We explore a collection of Python data visualizations and tutorials from the Python Graph Gallery. The website features hundreds of charts and graphs built using popular plotting libraries. Each chart type features a foundational tutorial that introduces the structure and application.

We also share several other articles and projects from the Python community, including a news roundup, the new REPL coming in Python 3.13, a pytest daemon to 10X test iteration speed, a discussion about software friction, a Raspberry Pi document scanner, and a project for controlling time per iteration loop.

Course Spotlight: Building a Python GUI Application With Tkinter

Topics:

00:00:00 - Introduction
00:02:08 - Python Software Foundation Board Election Dates for 2024
00:02:35 - 2023 PSF Annual Impact Report
00:03:03 - Python’s unittest: Writing Unit Tests for Your Code
00:09:41 - What’s New in Python 3.13
00:10:38 - The New REPL in Python 3.13
00:13:39 - Best Python Chart Examples
00:15:27 - Animation with text that highlights important events
00:16:39 - Sankey Diagram with Python and Plotly
00:18:55 - Video Course Spotlight
00:20:25 - Pytest Daemon: 10X Local Test Iteration Speed
00:23:58 - Software Friction
00:35:41 - A Raspberry Pi Document Scanner
00:39:00 - pacemaker: For Controlling Time Per Iteration Loop in Python
00:41:55 - Thanks and goodbye

News:

Show Links:

Python’s unittest: Writing Unit Tests for Your Code – In this tutorial, you’ll learn how to use the unittest framework to create unit tests for your Python code. Along the way, you’ll also learn how to create test cases, fixtures, test suites, and more.
What’s New in Python 3.13 – Python 3.13 has gone into beta, which means the feature freeze is now in place. This is the official listing of the new features in 3.13. This release includes changes to the REPL, new typing features, experimental support for disabling the GIL, dead battery removal, and more.
The New REPL in Python 3.13 – Python 3.13 just hit feature freeze with the first beta release, and it includes a host of improvements to the REPL. Automatic indenting, block-level editing, and more make the built-in REPL more powerful and easier to use.
Best Python Chart Examples
Animation with text that highlights important events - Python Graph Gallery
Sankey Diagram with Python and Plotly - Python Graph Gallery
Pytest Daemon: 10X Local Test Iteration Speed – Discord has a large Python monolith with lots of imports, which now takes 13 seconds to start up. On the server that’s not a problem but to run a test it is. Ruby’s solution is to have a daemon that hot loads a test on a process that already has the imports completed.

Discussion:

Software Friction – Friction is everywhere in software development. Two setbacks are more than twice as bad as one setback. This article discusses the sources of software friction and what you can do about it.

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Considering Accessibility & Assistive Tech as a Python Developer

Fri, 17 May 2024 12:00:00 +0000

What’s it like to learn Python as a visually impaired or blind developer? How can you improve the accessibility of your Python web applications and learn current guidelines? This week on the show, Real Python community member Audrey van Breederode discusses her programming journey, web accessibility, and assistive technology.

Audrey shares her background as a system administrator and instructor. While she was learning Python, she discovered Real Python. Audrey provided some feedback about the built-in video player’s accessibility. Dan reached out and worked with Audrey on some website improvements for the visually impaired.

We discuss navigating modern websites and using assistive technology. Audrey also provides resources Python developers can use to improve the accessibility of their applications.

Course Spotlight: HTML and CSS Foundations for Python Developers

There’s no way around HTML and CSS when you want to build web apps. Even if you’re not aiming to become a web developer, knowing the basics of HTML and CSS will help you understand the Web better. In this video course, you’ll get an introduction to HTML and CSS for Python programmers.

Topics:

00:00:00 – Introduction
00:03:12 – Work background
00:08:30 – Language for assistive tools and programming
00:10:30 – What led you to learning Python?
00:13:48 – Screen readers, braille display, and white space
00:17:22 – Discovering Real Python
00:22:41 – Accessibility survey and navigating websites
00:30:04 – Digging deeper into learning Python
00:35:42 – Video Course Spotlight
00:37:03 – Navigating changes in code
00:39:53 – Working with the terminal
00:42:14 – Accessibility of Python GUI libraries
00:44:22 – Django framework
00:47:11 – Screen readers and JAWS
00:53:19 – What are you excited about in the world of assistive technology?
00:57:03 – What are you excited about in the world of Python?
00:59:11 – What do you want to learn next?
01:00:09 – Thanks and goodbye

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Querying OpenStreetMaps via API & Lazy Evaluation in Python

Fri, 10 May 2024 12:00:00 +0000

Would you like to get more practice working with APIs in Python? How about exploring the globe using the data from OpenStreetMap? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share an article from the Pybites blog about building queries using the Overpass API for OpenStreetMap. The post explores the data structures, tags, query formats, and how to use Overpass in Python.

Christopher discusses a Real Python article by recent guest Stephen Gruppetta about lazy evaluation in Python. The piece covers the advantages of generator expressions or functions and the potential disadvantages of using lazy versus eager evaluation methods.

We also share several other articles and projects from the Python community, including a news roundup, handling control-c in asyncio, preventing data leakage in pandas and scikit-learn, discussing the Django developer survey results, asking developers why they aren’t shipping faster, using UV to install into isolated environments, and a couple of tools for retrying in Python.

This episode is sponsored by Sentry.

Course Spotlight: How to Set Up a Django Project

Topics:

00:00:00 – Introduction
00:02:33 – PyPy v7.3.16 Release
00:02:54 – PEP 745: Python 3.14 Release Schedule
00:03:31 – The BASIC programming language turns 60
00:05:24 – Asyncio Handle Control-C (SIGINT)
00:07:37 – OpenStreetMaps, Overpass API and Python
00:11:53 – What’s Lazy Evaluation in Python?
00:16:10 – Sponsor: Sentry
00:17:17 – How to Prevent Data Leakage in pandas & scikit-learn
00:24:09 – Django Developers Survey 2023 Results
00:33:07 – Video Course Spotlight
00:34:21 – I Asked 100 Devs Why They Aren’t Shipping Faster?
00:47:03 – pipxu: Install in Isolated Environments Using UV
00:49:05 – tenacity: Retrying Library for Python
00:50:04 – stamina: Production-Grade Retries for Python
00:52:00 – Thanks and goodbye

News:

Show Links:

Asyncio Handle Control-C (SIGINT) – When the user presses CTRL-C on the keyboard, the OS raises an interrupt signal to your program. When writing concurrent code this can get complicated as the signal goes to the process. This article shows you how to handle capturing CTRL-C elegantly when using asyncio.
OpenStreetMaps, Overpass API and Python – OpenStreetMaps (OSM) is an open source mapping project that allows people to browse the world map and to plan routes. Not only does it have the expected web interface, but it also has an API known as Overpass. This article shows you two ways to use Python to query Overpass.
What’s Lazy Evaluation in Python? – This tutorial explores lazy evaluation in Python and looks at the advantages and disadvantages of using lazy and eager evaluation methods. By the end of this tutorial, you’ll clearly understand which approach is best for you, depending on your needs.
How to Prevent Data Leakage in pandas & scikit-learn – How you impute missing values in machine learning data sets can affect the quality of your training. This article teaches you what data leakage is and what steps you should take to avoid it.

Discussion

Django Developers Survey 2023 Results
I Asked 100 Devs Why They Aren’t Shipping Faster? – Daksh asked 100 developers why they aren’t shipping faster and this blog post shares what he learned. Problems include dependency bugs, overly complicated code bases, waiting on requirements, and more.

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Embarking on a Relaxed and Friendly Python Coding Journey

Fri, 03 May 2024 12:00:00 +0000

Do you get stressed while trying to learn Python? Do you prefer to build small programs or projects as you continue your coding journey? This week on the show, Real Python author Stephen Gruppetta is here to talk about his new book, “The Python Coding Book.”

Stephen has been teaching Python to adults and children for many years. With his new book, he wants to share the relaxed and friendly learning environment he’s developed. We discuss using analogies to explain programming concepts and constructing complete programs as chapter goals.

Over the last year, Stephen focused on writing. He started his newsletter, The Python Coding Stack, wrote more tutorials for Real Python and authored the book.

This episode is sponsored by Mailtrap.

Course Spotlight: Python Basics: Code Your First Python Program

In this video course, you’ll write your first Python program. Along the way, you’ll learn about errors, declare variables and inspect their values, and try your hand at writing comments.

Topics:

00:00:00 – Introduction
00:02:32 – Education and programming background
00:05:50 – Developing a teaching style
00:10:36 – A friendly and relaxed programming book
00:14:31 – Making mistakes
00:18:29 – Sponsor: Mailtrap
00:19:03 – What was your curation process like?
00:21:22 – First chapter building an actual program
00:25:08 – Glossary terms and exercises
00:27:48 – Why feature an IDE?
00:34:07 – Monty and the White Room analogy
00:37:46 – What, no turtle?
00:42:21 – Video Course Spotlight
00:44:00 – Shift toward teaching
00:46:50 – Teaching adults and children
00:51:23 – Python sequences tutorial
00:53:48 – Building community and social media
00:58:12 – What are you excited about in the world of Python?
01:00:49 – What do you want to learn next?
01:03:07 – Thanks and goodbye

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Pydantic Data Validation & Python Web Security Practices

Fri, 26 Apr 2024 12:00:00 +0000

How do you verify and validate the data coming into your Python web application? What tools and security best practices should you consider as a developer? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the recent Real Python tutorial “Pydantic: Simplifying Data Validation in Python.” The piece covers installing the library with optional dependencies, working with base models, validating functions, and managing environment variables.

We continue our conversation about web development with another article about Python security best practices. This article covers several Python libraries and crucial steps you can take to help make your web-based applications more secure.

We also share several other articles and projects from the Python community, including a news roundup, why Python lists multiply oddly, inline run dependencies in pipx, a discussion about open-source contribution assignments, playing sounds in Python, and a Python library to access ISO country definitions.

This episode is sponsored by Mailtrap.

Course Spotlight: Using raise for Effective Exceptions

In this video course, you’ll learn how to raise exceptions in Python, which will improve your ability to efficiently handle errors and exceptional situations in your code. This way, you’ll write more reliable, robust, and maintainable code.

Topics:

00:00:00 – Introduction
00:02:40 – Python 3.12.3, Python 3.11.9, and 3.13.0a6 Released
00:03:43 – Django Bugfix Release Issued: 5.0.4
00:04:48 – PEP 738 Accepted: Adding Android as a Supported Platform
00:05:53 – EuroPython Tickets on Sale: Prague/Remote July 8-14
00:06:38 – PyCon Portugal 2024
00:07:17 – Pydantic: Simplifying Data Validation in Python
00:15:24 – Sponsor: Mailtrap
00:15:58 – Why Do Python Lists Multiply Oddly?
00:22:21 – Best Python Security Practices for Web Developers
00:34:13 – Video Course Spotlight
00:35:38 – Inline Run Dependencies in pipx 1.4.2
00:40:16 – So Your Teacher Wants You to Do Open Source
00:54:49 – nava: Play Sounds in Python
00:56:25 – pycountry: A Python library to access ISO country definitions
00:58:18 – Thanks and goodbye

News:

Show Links:

Pydantic: Simplifying Data Validation in Python – Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on tutorial, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.
Why Do Python Lists Multiply Oddly? – In Python you can use the multiplication operator on sequences to return a repeated version of the value. When you do this with a list containing an empty list you get what might be unexpected behavior. This article explains what happens and why.
Best Python Security Practices for Web Developers – Coding on the web means you have to be more security conscious as everyone has access to your software. This article discusses key steps you can take to help make your code more secure.
Inline Run Dependencies in pipx 1.4.2 – PEP 723 adds the ability to specify dependencies within a Python script itself. The folks who write pipx have added an experimental feature that takes advantage of this future language change. This article shows you how the new feature looks and what pipx does with it.
Install and Execute Python Applications Using pipx – In this tutorial, you’ll learn about a tool called pipx, which lets you conveniently install and run Python packages as standalone command-line applications in isolated environments. In a way, pipx turns the Python Package Index (PyPI) into an app marketplace for Python programmers.

Discussion:

So Your Teacher Wants You to Do Open Source – Sometimes teachers or mentors ask students to contribute to an open source project, without the context of what that entails. This opinion piece covers just how much noise that causes for the projects and why you shouldn’t do it unless you truly mean to contribute.
503 Days Working Full-Time on FOSS: Lessons Learned – For a year and a half, Rodrigo worked at Textualize the company behind the popular open source Python projects Rich and Textual. This blog post talks about what he learned while he was there.

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Decoupling Systems to Get Closer to the Data

Fri, 19 Apr 2024 12:00:00 +0000

What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.

Phillip contrasts Ibis’s workflow with other Python dataframe libraries. We discuss how “getting close to the data” speeds things up and conserves memory.

He describes the different approaches Ibis provides for querying data and how to select a specific backend. We discuss ways to get started with the library and how to access example data sets to experiment with the platform.

Phillip discovered Ibis while looking for a tool that allowed him to reuse SQL queries written for a specific data platform on a different one. He recounts how he got involved with the Ibis project, sharing his background in open source and learning how to contribute to a first project.

This episode is sponsored by Mailtrap.

Course Spotlight: Creating Web Maps From Your Data With Python Folium

You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country.

Topics:

00:00:00 – Introduction
00:02:18 – How did you get started with Ibis?
00:08:10 – First contribution to open source
00:13:46 – Comparing Ibis to other dataframe libraries
00:20:09 – Sponsor: Mailtrap
00:20:43 – What goes into the selection of backend?
00:27:07 – Database connections vs SQL compilers
00:30:03 – Raw SQL approach
00:34:06 – Dataframe approach
00:38:31 – What does “getting close to the data” mean?
00:41:52 – Video Course Spotlight
00:43:24 – Phillip in the cloud - YouTube channel
00:44:56 – Access to sample data sets
00:50:11 – Additional resources
00:52:50 – What are some of the backends Ibis supports?
00:54:13 – Entry points to the platform
00:55:00 – How are you supported?
00:57:10 – Exporting a SQL query
00:59:23 – What are you excited about in the world of Python?
01:04:28 – What do you want to learn next?
01:07:12 – How can people follow your work online?
01:08:00 – Thanks and goodbye

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Avoiding Error Culture and Getting Help Inside Python

Fri, 12 Apr 2024 12:00:00 +0000

What is error culture, and how do you avoid it within your organization? How do you navigate alert and notification fatigue? Hey, it’s episode #200! Real Python’s editor-in-chief, Dan Bader, joins us this week to celebrate. Christopher Trudeau also returns to bring another batch of PyCoder’s Weekly articles and projects.

We discuss an article series about error culture. We dig into false positives, hero culture, and the tendency to start ignoring alerts. We contrast our personal experiences and propose possible remedies. Dan describes configuring Real Python’s alert system.

We also share several other articles and projects from the Python community, including a news roundup, reading and writing WAV files in Python, moving beyond flat files toward SQLite and SQLAlchemy, getting help in Python, exploring four kinds of optimization, a framework for building web scrapers, and a project to simply subprocesses.

This week’s episode is brought to you by Sentry.

Course Spotlight: SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files

In this video course, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Topics:

00:00:00 – Introduction
00:02:42 – PyPI Hiring a Support Specialist
00:03:19 – PyPI Temporarily Halted New Users and Projects
00:04:14 – What we know about the xz Utils backdoor
00:05:38 – CPython, PyPI, and the backdoor of xz
00:07:18 – Episode 200 appreciation and the journey
00:09:18 – A visit from Dan
00:14:14 – Reading and Writing WAV Files in Python
00:19:56 – Sponsor: Sentry
00:21:03 – SQLite and SQLAlchemy in Python
00:27:36 – Getting Help (In Python)
00:32:49 – Laurence Tratt: Four Kinds of Optimization
00:40:54 – Video Course Spotlight
00:42:26 – Discussion: Error Culture
00:58:03 – botasaurus: The All in One Framework to Build Awesome Scrapers
01:01:04 – suby: Slightly Simplified Subprocesses
01:02:50 – Thanks and goodbye

News:

PyPI Hiring a Support Specialist (Remote)
PyPI Temporarily Halted New Users and Projects – To fend off a supply-chain attack, PyPI temporarily halted new users and projects for about 10 hours last week. This article discusses why, and the scourge of supply-chain attacks.
What we know about the xz Utils backdoor that almost infected the world - Ars Technica
CPython, PyPI, and many Python packages are not affected by the backdoor of xz - Core Development - Discussions on Python.org

Topics:

Reading and Writing WAV Files in Python – In this tutorial, you’ll learn how to work with WAV audio files in Python using the standard-library wave module. Along the way, you’ll synthesize sounds from scratch, visualize waveforms in the time domain, animate real-time spectrograms, and apply special effects to widen the stereo field.
SQLite and SQLAlchemy in Python: Beyond Flat Files – In this video course, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.
How SQLite Is Tested – The page describes how SQLite is rigorously tested using four test harnesses, fuzz testing, anomaly testing like crash and I/O error simulations, and other techniques to ensure reliability.
Getting Help (In Python) – When trying to remember just where sleep() was in the Python standard library, Ishaan stumbled through the built-in help and learned how to use it to answer just these kinds of questions.
Laurence Tratt: Four Kinds of Optimization – “Premature optimization might be the root of all evil, but overdue optimization is the root of all frustration. No matter how fast hardware becomes, we find it easy to write programs which run too slow.” Read on to learn what to do about it.

Discussion:

Projects:

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Leveraging Documents and Data to Create a Custom LLM Chatbot

Fri, 05 Apr 2024 12:00:00 +0000

How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.

Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares a recent project for a family-owned seed company that wanted to build a tool for customers to access years of farm research. These documents were stored as brochure-style PDFs and spanned 50 years.

We discuss several of the tools used to augment a LLM. Calvin covers working with LangChain and vectorizing data with ChromaDB. We talk about the obstacles and limitations of capturing documentation.

Calvin also shares a smaller project that you can try out yourself. It takes the information from a conference website and creates a chatbot using Django and Python prompt-toolkit.

This episode is sponsored by Mailtrap.

Course Spotlight: Command Line Interfaces in Python

Command line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.

Topics:

00:00:00 – Introduction
00:02:21 – Background on the project
00:03:51 – Complexity of adding documents
00:09:01 – Retrieval-augmented generation and providing links
00:13:46 – Updating information and larger conversation context
00:18:08 – Sponsor: Mailtrap
00:18:43 – Working with context
00:21:02 – Temperature adjustment
00:22:07 – Rally Conference Chatbot Project
00:26:20 – Vectorization using ChromaDB
00:32:49 – Employing Python prompt-toolkit
00:35:07 – Learning libraries on the fly
00:37:38 – Video Course Spotlight
00:39:00 – Problems with tables in documents
00:42:30 – Everything looks like a chat box
00:44:26 – Finding the right fit for a client and customer
00:49:05 – What are questions you ask a new client now?
00:51:54 – Canada Air anecdote
00:56:20 – How do you stay up to date on these topics?
01:01:03 – What are you excited about in the world of Python?
01:03:22 – What do you want to learn next?
01:04:58 – How can people follow your work online?
01:05:31 – IndyPy
01:07:13 – Thanks and goodbye

Show Links:

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Build a Video Game With Python Turtle & Visualize Data in Seaborn

Fri, 29 Mar 2024 12:00:00 +0000

Can you build a Space Invaders clone using Python’s built-in turtle module? What advantages does the Seaborn data visualization library provide compared to Matplotlib? Christopher Trudeau is back on the show this week, along with special guest Real Python core team member Bartosz Zaczyński. We’re sharing another batch of PyCoder’s Weekly articles and projects.

Bartosz shares a Real Python step-by-step project for building a video game using the Python turtle module. The turtle module provides an interactive environment that lets users create pictures and shapes on a virtual canvas. The project takes you through game design concepts such as animating sprites, detecting a collision, and building a game loop.

We discuss another Real Python resource, “Visualizing Data in Python With Seaborn.” Seaborn is a significant next step if you’ve already been working with Matplotlib. It produces impressive visualizations and offers a functional or object-based approach to designing plots.

We also share several other articles and projects from the Python community, including a news roundup, finding Python Easter eggs, exploring whether Python has pointers, styling Excel cells with OpenPyXL, using weird tests to capture tacit knowledge, inspecting and running Django commands in a TUI, building reactive web UIs in Python, and a project for predictable Python datetimes.

This week’s episode is brought to you by Posit.

Course Spotlight: Python Turtle for Beginners

In this step-by-step course, you’ll learn the basics of Python programming with the help of a simple and interactive Python library called turtle. If you’re a beginner to Python, then this tutorial will definitely help you on your journey as you take your first steps into the world of programming.

Topics:

00:00:00 – Introduction
00:03:06 – Python 3.13.0 alpha 5 is now available
00:03:38 – Allow disabling the GIL with flags
00:04:17 – Django security releases issued: 5.0.3, 4.2.11, and 3.2.25
00:04:32 – The Python Coding Book
00:05:03 – Finding Python Easter Eggs – Code Conversation
00:12:48 – Sponsor: Posit
00:13:34 – Visualizing Data in Python With Seaborn
00:18:18 – Does Python have pointers?
00:21:43 – Build a Python Turtle Game: Space Invaders Clone
00:30:42 – Video Course Spotlight
00:32:04 – Styling Excel Cells with OpenPyXL and Python
00:35:22 – Use weird tests to capture tacit knowledge
00:37:09 – whenever: Strict, predictable, and typed datetimes
00:42:25 – hyperdiv: Build Reactive Web UIs in Python
00:46:19 – django-tui: Inspect and run Django Commands in a TUI
00:48:40 – Thanks and goodbye

News:

Show Links:

Finding Python Easter Eggs – Code Conversation - Video Course – Python has its fair share of hidden surprises, commonly known as Easter eggs. From clever jokes to secret messages, these little mysteries are often meant to be discovered by curious developers like you!
Visualizing Data in Python With Seaborn – In this tutorial, you’ll learn how to use the Python seaborn library to produce statistical data analysis plots to allow you to better visualize your data. You’ll learn how to use both its traditional classic interface and more modern objects interface.
Does Python have pointers? - Ned Batchelder – Depending on how you’re using the term “pointer” changes the answer to the question. Read on to better understand the programming terminology and whether Python has pointers.
Build a Python Turtle Game: Space Invaders Clone – In this step-by-step tutorial, you’ll use Python’s turtle module to write a Space Invaders clone. You’ll learn about techniques used in animations and games, and consolidate your knowledge of key Python topics.
Styling Excel Cells with OpenPyXL and Python - Many Python libraries that deal with Excel only handle data, but OpenPyXL gives you the ability to style your cells in many different ways. Learn how to give your spreadsheets pizazz!
Use weird tests to capture tacit knowledge - Applied Cartography – Sometimes adding code in one place means configuration elsewhere also needs to be updated. One way of ensuring this is happening properly in a large project is to use unit tests. This post covers a few examples, complete with pytest code.

Projects:

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Using Python in Bioinformatics and the Laboratory

Fri, 22 Mar 2024 12:00:00 +0000

How is Python being used to automate processes in the laboratory? How can it speed up scientific work with DNA sequencing? This week on the show, Chemical Engineering PhD Student Parsa Ghadermazi is here to discuss Python in bioinformatics.

Parsa provides background on his research and the bioinformatic techniques used to discover gut microbes’ role in human health and diseases. We talk about automating lab experiments with liquid handling robots and Python.

We dig into libraries to shatter and reassemble DNA sequences. Parsa also shares current projects from the Chan Lab at Colorado State University and his GitHub repository.

Course Spotlight: Building Python Project Documentation With MkDocs

In this video course, you’ll learn how to build professional documentation for a Python package using MkDocs and mkdocstrings. These tools allow you to generate nice-looking and modern documentation from Markdown files and, more importantly, from your code’s docstrings.

Topics:

00:00:00 – Introduction
00:01:51 – Engineering Background and Current PhD Program
00:05:52 – What is Bioinformatics?
00:08:11 – Where do you use Python in the lab?
00:10:35 – Using lab robotics
00:15:22 – Python development environment
00:16:33 – Lab robotics allow for precision
00:19:03 – How are using these tools for research?
00:22:14 – What are the techniques for measurements?
00:26:17 – Video Course Spotlight
00:27:33 – How is the data output from the machine?
00:29:20 – Moving into DNA sequencing and extraction
00:32:08 – Shattering to work with smaller DNA sequences
00:34:34 – Python libraries for DNA re-assembly
00:36:28 – Building ADToolbox
00:40:24 – How do you store the data?
00:41:32 – Inferring microbial interactions
00:44:02 – Types of hardware used for these projects
00:47:07 – What are you excited about in the world of Python?
00:48:09 – What do you want to learn next?
00:49:16 – How can people follow your work online?
00:50:00 – Thanks and goodbye

Show Links:

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Exploring Duck Typing in Python & Dynamics of Monkey Patching

Fri, 15 Mar 2024 12:00:00 +0000

What are the advantages of determining the type of an object by how it behaves? What coding circumstances are not a good fit for duck typing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher covers a recent Real Python tutorial by Leodanis Pozo Ramos titled Duck Typing in Python: Writing Flexible and Decoupled Code. The tutorial explains the concepts of duck typing within object-oriented programming and its use within Python’s built-in tools.

We discuss a recent article on monkey patching in Python. This practice of dynamically modifying a class or module’s behavior at runtime allows for testing, debugging, and experimentation.

We also share several other articles and projects from the Python community, including a news roundup, why names are not the same as objects in Python, using IPython Jupyter magic commands, a discussion about becoming a senior developer, a data exploration challenge, a Python evaluation game, and a terminal UI for regex testing.

This week’s episode is brought to you by Sentry.

Course Spotlight: Pointers and Objects in Python

In this video course, you’ll learn about Python’s object model and see why pointers don’t really exist in Python. You’ll also cover ways to simulate pointers in Python without managing memory.

Topics:

00:00:00 – Introduction
00:02:38 – Listener feedback
00:04:02 – DjangoCon US Call for Proposals
00:04:38 – White House Recommends Use of Python
00:05:41 – JupyterLab 4.1 and Notebook 7.1 Released
00:06:05 – What’s in a Name?
00:11:52 – Duck Typing in Python: Writing Flexible and Decoupled Code
00:15:07 – Sponsor: Sentry
00:16:11 – Using IPython Jupyter Magic Commands
00:22:31 – A Guide to Monkey Patching
00:25:27 – Falsehoods Junior Developers Believe About Becoming Senior
00:33:01 – Video Course Spotlight
00:34:11 – Falsehoods continued
00:43:43 – Where in the data?
00:46:55 – the eval game
00:48:03 – rexi: Terminal UI for Regex Testing
00:49:43 – Thanks and goodbye

News:

Show Links:

What’s in a Name? – An article about names in Python, and why they’re not the same as objects. The article discusses reference counts and namespaces.
Duck Typing in Python: Writing Flexible and Decoupled Code – In this tutorial, you’ll learn about duck typing in Python. It’s a typing system based on objects’ behaviors rather than on inheritance. By taking advantage of duck typing, you can create flexible and decoupled sets of Python classes that you can use together or individually.
Using IPython Jupyter Magic Commands – “IPython Jupyter Magic commands (e.g. lines in notebook cells starting with % or %%) can decorate a notebook cell, or line, to modify its behavior.” This article shows you how to define them and where they can be useful.
Monkeying Around With Python: A Guide to Monkey Patching – Monkey patching is the practice of modifying live code. This article shows you how it’s done and why and when to use the practice.

Discussion:

Falsehoods Junior Developers Believe About Becoming Senior – This opinion piece by Vadim discusses how newer developers perceive what it means to be a senior developer, and how they’re often wrong.

Projects:

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Building a Healthy Developer Mindset While Learning Python

Fri, 08 Mar 2024 12:00:00 +0000

How do you get yourself unstuck when facing a programming problem? How do you develop a positive developer mindset while learning Python? This week on the show, Bob Belderbos from Pybites is here to talk about learning Python and building healthy developer habits.

Bob created the Pybites learning platform with his friend Julian Sequeira. They initially focused on exercises and coding challenges to motivate new Python students. As they grew their community, they created a podcast and moved into coaching.

They noticed that most new developers share common struggles of tutorial paralysis, imposter syndrome, and motivation. Bob discusses techniques for developing a positive mindset, overcoming coding blocks, and delivering projects.

Course Spotlight: Grow Your Python Portfolio With 13 Intermediate Project Ideas

Get started on 13 Python project ideas that are just right for intermediate Python developers. They’ll challenge you enough to help you become a better Pythonista.

Topics:

0:00:00 – Introduction
0:02:06 – How did you start Pybites?
0:04:32 – Building a community through challenges
0:06:17 – When did you start your podcast?
0:08:22 – Defining developer mindset
0:11:31 – Learning Python outside of a classroom
0:16:15 – Podcast is a good place to discuss mindset
0:19:37 – Video Course Spotlight
0:20:56 – Sharing Python tips
0:30:14 – Sharing content as a creator
0:35:03 – Writing cleaner code
0:40:20 – Moving from challenges to projects
0:47:08 – Helping yourself when you’re stuck
0:51:31 – Dealing with imposter syndrome
0:55:50 – What are you excited about in the world of Python?
0:57:43 – What do you want to learn next?
0:59:15 – How can people follow your work online?
0:59:51 – Thanks and goodbye

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

Automate Tasks With Python & Building a Small Search Engine

Fri, 01 Mar 2024 12:00:00 +0000

What are the typical computer tasks you do manually every week? Could you automate those tasks with a Python script? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent Hacker News thread about frequently used automation scripts. We share the kinds of tasks we’ve automated with Python in our work and personal lives.

Christopher shares a tutorial about building a micro-search engine from scratch using Python. The post takes you through coding the components of a crawler, index, and ranker. The finished engine is designed to search the posts of the blogs you follow.

We also share several other articles and projects from the Python community, including a news roundup, how a Polars query works under the hood, using Python for data analysis, understanding open-source licensing, summarizing the significant changes between Python versions, a robust TUI hex editor, and a lightweight dataframe library with a universal interface for data wrangling.

This week’s episode is brought to you by Intel.

Course Spotlight: Building Command Line Interfaces With argparse

In this step-by-step Python video course, you’ll learn how to take your command line Python scripts to the next level by adding a convenient command line interface that you can write with argparse.

Topics:

00:00:00 – Introduction
00:02:23 – uv: Python Packaging in Rust
00:02:43 – Rye Grows With uv
00:03:20 – Python 3.13.0 Alpha 4 Is Now Available
00:03:45 – A Bird’s Eye View of Polars
00:07:28 – Polars: Why We Have Rewritten the String Data Type
00:09:33 – A Search Engine in 80 Lines of Python
00:13:14 – Using Python for Data Analysis
00:18:22 – Sponsor: Intel
00:18:53 – Understanding Open Source Licensing
00:21:54 – Summary of Major Changes Between Python Versions
00:23:19 – What Python automation scripts do you reuse frequently at work?
00:34:21 – Video Course Spotlight
00:35:52 – hexabyte: A modern, modular, and robust TUI hex editor
00:39:56 – ibis: The Flexibility of Python With the Scale of Modern SQL
00:43:31 – Thanks and goodbye

News:

uv: Python Packaging in Rust – uv is an extremely fast Python package installer and resolver, designed as a drop-in alternative to pip and pip-tools. This post introduces you to uv and shows some of its performance numbers.
Rye Grows With uv - Armin Ronacher’s Thoughts and Writings
Python 3.13.0 Alpha 4 Is Now Available

Show Links:

A Bird’s Eye View of Polars – This post on the Polars blog introduces you to how Polars works, showing the steps from queries, plans, optimizations, and then the final execution.
Polars: Why We Have Rewritten the String Data Type – A large refactor on the string data type is underway in Polars. This deep dive explains why and what is changing.
A Search Engine in 80 Lines of Python – In this post Alex explains how he built a micro-search engine from scratch using Python. The resulting search engine is used to search in the posts of the blogs he follows.
Using Python for Data Analysis – In this tutorial, you’ll learn the importance of having a structured data analysis workflow, and you’ll get the opportunity to practice using Python for data analysis while following a common workflow process.
Understanding Open Source Licensing – This article discusses the importance of open-source licensing in software development and its implications for stakeholders.
Summary of Major Changes Between Python Versions – This article is a quick reference covering the major changes introduced with each new version of Python. Can’t remember when the walrus operator was introduced? This is the place to look that up.

Discussion:

What Python automation scripts do you reuse frequently at work? - Hacker News

Projects

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Wes McKinney on Improving the Data Stack & Composable Systems

Fri, 23 Feb 2024 12:00:00 +0000

How do you avoid the bottlenecks of data processing systems? Is it possible to build tools that decouple storage and computation? This week on the show, creator of the pandas library Wes McKinney is here to discuss Apache Arrow, composable data systems, and community collaboration.

Wes briefly describes the humble beginnings of the pandas project in 2008 and moving the project to open source in 2011. Since then, he’s been thinking about improvements across the data processing ecosystem.

Wes collaborated with members of the broader data science community to build the in-memory analytics infrastructure of Apache Arrow. Arrow avoids the bottlenecks of repeated data serialization and format conversion. He shares examples of Arrow’s use across the spectrum in tools like Polars and DuckDB.

Wes advocates moving from vertically integrated tools toward composable data systems. We discuss his work on Ibis, a portable dataframe API for data manipulation and exploration in Python. Ibis supports multiple backends by decoupling the API from the execution engine.

This week’s episode is brought to you by Posit Connect.

Course Spotlight: Unleashing the Power of the Console With Rich

Rich is a powerful library for creating text-based user interfaces (TUIs) in Python. It enhances code readability by pretty-printing complex data structures and adds visual appeal with colored text, tables, animations, and more.

Topics:

00:00:00 – Introduction
00:02:26 – Dealing with limitations in early data science
00:04:53 – Making pandas open source
00:07:10 – Making changes to an existing platform
00:12:34 – Decoupling storage and computation
00:23:04 – Sponsor: Posit Connect
00:23:54 – Apache Arrow solving multiple issues
00:27:40 – DuckDB efficient analytic SQL database
00:30:24 – Polars dataframe library
00:31:04 – pandas 2.0 adding Arrow
00:35:56 – Video Course Spotlight
00:37:20 – Apache Software Foundation background
00:41:29 – Shifting from developer to organizer and collaborator
00:45:56 – Creating a portable query layer with Ibis
00:55:34 – Casualties of the language wars
00:57:57 – What’s your role at Posit?
01:01:23 – What are you excited about in the world of Python?
01:04:52 – What do you want to learn next?
01:06:21 – How can people follow your work online?
01:08:20 – Thanks and goodbye

Show Links:

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Practical Python Decorator Uses & Avoiding datetime Pitfalls

Fri, 16 Feb 2024 12:00:00 +0000

What are real-life examples of using Python decorators? How can you harness their power in your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article series that digs into Python decorators. The first two articles discuss the basics of constructing decorators. The third part describes how popular Python libraries use decorators with call interception, function registration, and enriching the behavior of a function.

Christopher shares a piece about the common pitfalls of working with the Pythondatetime library. The article considers how current third-party libraries don’t address most of these quirks and offers a potential solution with a new library.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about the popularity of the Rust language, handling unset values in FastAPI with Pydantic, working with Python’s mini-language for formatting strings, mocking Django queryset functions, and a modern replacement for the Requests library.

This week’s episode is brought to you by Sentry.

Course Spotlight: Python Decorators 101

In this course on Python decorators, you’ll learn what they are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions in Python. By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

Topics:

00:00:00 – Introduction
00:02:53 – Django security releases issued: 5.0.2, 4.2.10, and 3.2.24
00:03:10 – Python 3.12.2 and 3.11.8 are now available
00:03:21 – Introducing PSF Grants Program Office Hours
00:04:19 – Python’s Format Mini-Language for Tidy Strings
00:12:22 – Ten Python datetime Pitfalls
00:18:34 – Sponsor: Sentry
00:19:37 – Real Life Use of Decorators
00:29:18 – Handling Unset Values in FastAPI With Pydantic
00:35:43 – Video Course Spotlight
00:37:06 – The Python Rust-Aissance
00:50:19 – django-mock-queries: Mock Django Queryset Functions
00:53:09 – niquests: Requests but Multiplexed
00:55:55 – Thanks and goodbye

News:

Show Links:

Python’s Format Mini-Language for Tidy Strings – In this tutorial, you’ll learn about Python’s format mini-language. See how to use it for creating working format specifiers and build nicely formatted strings and messages in your code.
Ten Python datetime Pitfalls – It’s no secret that the Python datetime library has its quirks. Not only are there probably more than you think, but third-party libraries don’t address most of them! Arie created a new library to explore what a better datetime library could look like.
Real Life Use of Decorators – Part 3 in a series on how Python decorators are used. This part covers real-life use cases including call interception, function registration, and behavioral enrichment.
Handling Unset Values in FastAPI With Pydantic – When using the HTTP PATCH method only those fields that got changed are updated. Pydantic sets fields not given as arguments as None so there is no way to distinguish between an explicit None value and an unset field. This post explains how you process this scenario.

Discussion:

The Python Rust-Aissance – Companies like Polars are showing how with Rust, Python developers now have a better, smoother path towards building high-performance libraries.
Rye: A Python Developer Experience Vision Continued
PyO3: Rust bindings for the Python interpreter
Rust in Linux: Where we are and where we’re going next
RustPython: OSS CPython Written in Rust
granian: Rust HTTP Server for Python Applications

Projects:

Additional Links:

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Focusing on Data Science & Less on Engineering and Dependencies

Fri, 09 Feb 2024 12:00:00 +0000

How do you manage the dependencies of a large-scale data science project? How do you migrate that project from a laptop to cloud infrastructure or utilize GPUs and multiple instances in parallel? This week on the show, Savin Goyal returns to discuss the updates to the open-source framework Metaflow.

Savin briefly describes the Metaflow platform and the goal of simplifying engineering overhead for data scientists and programmers. We discuss how the platform captures snapshots of a project as you work, allowing you to go back in time or share the state of your project with another team member.

We dig into the complicated process of managing dependencies for machine learning and data science projects. Savin describes how the required external libraries can be specified within a flow with the new @pypi or @conda decorators. This allows a project to scale from a local machine to the cloud or multiple instances with all dependencies included.

He talks about starting a new company, Outerbounds, with fellow co-workers from Netflix. Their vision is to continue to build the Metaflow open-source platform and offer customers scalable enterprise-grade infrastructure.

This week’s episode is brought to you by Intel.

Course Spotlight: Everyday Project Packaging With pyproject.toml

Topics:

00:00:00 – Introduction
00:02:25 – Update on Metaflow
00:04:13 – What is Outerbounds?
00:07:26 – An ML platform to serve data scientists needs
00:13:02 – Dependency reproducibility via @conda and @pypi decorators
00:26:18 – Sponsor: Intel
00:27:10 – Storing lock files along with snapshots
00:29:17 – Working alongside code and dependency management systems
00:34:03 – Scaling a project from laptop to the cloud
00:40:13 – Video Course Spotlight
00:41:41 – Getting visibility on processes
00:47:23 – Adjusting your project due to GPU availability
00:52:27 – Example of jumping back into a project one year later
00:55:54 – What are you excited about in the world of Python?
00:57:39 – What do you want to learn next?
00:59:35 – How can people follow your work online?
01:00:19 – Thanks and goodbye

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

Great Starting Points for Contributing to Open Source

Fri, 02 Feb 2024 12:00:00 +0000

What’s it like to sit down for your first developer sprint at a conference? How do you find an appropriate issue to work on as a new open-source contributor? This week on the show, author and software engineer Stefanie Molin is here to discuss starting to contribute to open-source projects.

Stefanie is a data scientist and software engineer on Bloomberg’s Security Data Science team. She recently wrote an article titled “5 Ways to Get Started in Open Source.” We discuss finding ways to contribute that fit your interests and developer skills. We dig into the experience of participating in community sprints at a conference.

Stefanie is the author of Hands-On Data Analysis with Pandas. We also discuss the different processes between writing technical articles and authoring a book.

This week’s episode is brought to you by Intel.

Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs

In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read The Docs to automatically build and publish your code documentation.

Topics:

00:00:00 – Introduction
00:02:55 – Being asked about how to get started
00:07:13 – Differences in short vs long form writing
00:09:03 – What was your introduction to contributing?
00:17:52 – What are additional benefits of contributing?
00:22:32 – Sponsor: Intel
00:23:22 – Sprints as an entry point
00:34:36 – Other requirements of a sprint
00:36:05 – Differences in conferences
00:41:52 – Other sprint experiences
00:42:50 – Contributing examples to documentation
00:45:59 – Video Course Spotlight
00:47:11 – Looking for good first issues
00:52:04 – Is this a bug?
00:54:10 – Proposing a new feature
00:56:36 – Data Morph and working on personal projects
01:07:29 – Showing up in the Python community
01:12:14 – What are you excited about in the world of Python?
01:14:59 – How can people follow the work you do online?
01:15:28 – What do you want to learn next?
01:18:23 – Thanks and goodbye

Show Links:

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Building a Python Debugger & Preparing for NumPy 2.0

Fri, 26 Jan 2024 12:00:00 +0000

How does a debugger work? What can you learn about Python by building one from scratch? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a two-part tutorial on building a debugger in Python? Creating a simple one requires less code than you might think.

We also talk about an article from Itamar Turner-Trauring about how to prepare for the upcoming changes to NumPy. The new version is not backward compatible and will require some inspection of your project dependencies. Itamar includes advice, techniques, and tools for updating your code.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about managing advice as a new developer, moving to Python as a former R developer, building a Markov chain to generate readable nonsense, optimizing fonts to individual glyphs on your website, and a project for working with units of measurement in Python.

This week’s episode is brought to you by Posit Connect.

Course Spotlight: Create a Tic-Tac-Toe Python Game Engine With an AI Player

In this video course, you’ll create a universal game engine in Python for tic-tac-toe with two computer players, one of which will be an AI player using the powerful minimax algorithm. You’ll give your game library a text-based graphical interface and explore two front ends.

Topics:

00:00:00 – Introduction
00:02:18 – DjangoCon Europe 2024 CFP Now Open
00:02:38 – Python Insider: Python 3.13.0 alpha 3 is now available
00:03:04 – NumPy 2 Is Coming: Preventing Breakage, Updating Your Code
00:07:37 – Using a Markov Chain to Generate Readable Nonsense
00:12:53 – Sponsor: Posit Connect
00:13:43 – Python Rgonomics
00:20:10 – Let’s Create a Python Debugger Together
00:23:49 – Video Course Spotlight
00:25:08 – Advice for New Devs Who’ve Read Other Advice Essays
00:42:44 – Fontimize: Optimize Fonts to the Glyphs on Your Site
00:44:57 – Pint: Units for Python
00:46:55 – Thanks and Goodbye

News:

Show Links:

NumPy 2 Is Coming: Preventing Breakage, Updating Your Code – NumPy 2 is coming, and it’s backwards incompatible. Learn how to keep your code from breaking, and how to upgrade.
Using a Markov Chain to Generate Readable Nonsense – Describes a simple Markov chain algorithm to generate reasonable-sounding but utterly nonsensical text, and presents some example outputs as well as a Python implementation in only 20 lines of code.
Python Rgonomics – If you’re coming to Python from R, this article outlines some libraries that have an R-like feel, helping you make the transition to Pythonic workflows.
Let’s Create a Python Debugger Together – Ever wondered how a debugger works? Implementing a simple one requires less code than you might think. Read on to find out how.

Discussion:

Advice for New Devs Who’ve Read Other Advice Essays – After reading some programming advice posts, this author decided a lot of them concentrated on the wrong things. Here is his own take.
Associated Hacker News Discussion

Projects:

Additional Links:

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Measuring Bias, Toxicity, and Truthfulness in LLMs With Python

Fri, 19 Jan 2024 12:00:00 +0000

How can you measure the quality of a large language model? What tools can measure bias, toxicity, and truthfulness levels in a model using Python? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to discuss techniques and tools for evaluating LLMs With Python.

Jodie provides some background on large language models and how they can absorb vast amounts of information about the relationship between words using a type of neural network called a transformer. We discuss training datasets and the potential quality issues with crawling uncurated sources.

We dig into ways to measure levels of bias, toxicity, and hallucinations using Python. Jodie shares three benchmarking datasets and links to resources to get you started. We also discuss ways to augment models using agents or plugins, which can access search engine results or other authoritative sources.

This week’s episode is brought to you by Intel.

Course Spotlight: Learn Text Classification With Python and Keras

Topics:

00:00:00 – Introduction
00:02:19 – Testing characteristics of LLMs with Python
00:04:18 – Background on LLMs
00:08:35 – Training of models
00:14:23 – Uncurated sources of training
00:16:12 – Safeguards and prompt engineering
00:21:19 – TruthfulQA and creating a more strict prompt
00:23:20 – Information that is out of date
00:26:07 – WinoBias for evaluating gender stereotypes
00:28:30 – BOLD dataset for evaluating bias
00:30:28 – Sponsor: Intel
00:31:18 – Using Hugging Face to start testing with Python
00:35:25 – Using the transformers package
00:37:34 – Using langchain for proprietary models
00:43:04 – Putting the tools together and evaluating
00:47:19 – Video Course Spotlight
00:48:29 – Assessing toxicity
00:50:21 – Measuring bias
00:54:40 – Checking the hallucination rate
00:56:22 – LLM leaderboards
00:58:17 – What helped ChatGPT leap forward?
01:06:01 – Improvements of what is being crawled
01:07:32 – Revisiting agents and RAG
01:11:03 – ChatGPT plugins and Wolfram-Alpha
01:13:06 – How can people follow your work online?
01:14:33 – Thanks and goodbye

Background Links:

Dataset Links:

Tutorials and Documentation for Python Packages:

Measurement Links:

Training Data for LLMs:

Agents and Plugin Links:

Additional Links:

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Serializing Data With Python & Underscore Naming Conventions

Fri, 12 Jan 2024 12:00:00 +0000

Do you need to transfer an extensive data collection for a science project? What’s the best way to send executable code over the wire for distributed processing? What are the different ways to serialize data in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a tutorial by Real Python author Bartosz Zaczyński called “Serialize Your Data With Python.” This comprehensive guide moves beyond XML and JSON to explore multiple data formats and their potential use cases. It’s a deep dive into the topic and provides a thorough resource for future reference.

We also discuss a Real Python tutorial about naming conventions in Python that use single and double underscores. The piece covers differentiating between public and non-public names in APIs, writing safe classes for subclassing purposes, and avoiding name clashes with keywords.

We also share several other articles and projects from the Python community, including a couple of release announcements and news items, a discussion about never being taught how to construct quality software, building a small REPL in Python, using the key parameter in Python functions and methods, a framework for RESTful APIs using Flask and SQLAlchemy, and a Rust-based HTML sanitizer for your Python projects.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

Topics:

00:00:00 – Introduction
00:02:47 – JIT Coming to Python 3.13
00:03:13 – A copy-and-patch JIT compiler - Pull Request #113465
00:03:54 – Django bugfix releases issued: 4.2.9 and 5.0.1
00:04:24 – Single and Double Underscores in Python Names
00:16:42 – Building a Small REPL in Python
00:20:07 – The Key to the key Parameter in Python
00:25:23 – Video Course Spotlight
00:26:44 – Serialize Your Data With Python
00:30:23 – You Are Never Taught How to Build Quality Software
00:48:43 – flask-muck: RESTful APIs Using Flask and SQLAlchemy
00:51:26 – nh3: Python binding to Ammonia HTML sanitizer Rust crate
00:53:33 – Thanks and goodbye

News:

JIT Coming to Python 3.13 – Slides related to the upcoming JIT commit for Python 3.13. Note that GitHub paginates the slides if you don’t download them, so click the More Pages button to keep reading.
A copy-and-patch JIT compiler by brandtbucher - Pull Request #113465
Django bugfix releases issued: 4.2.9 and 5.0.1 - Weblog Django

Show Links:

Single and Double Underscores in Python Names – In this tutorial, you’ll learn a few Python naming conventions involving single and double underscores (_). You’ll learn how to use this character to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.
Building a Small REPL in Python – Learn how to write your own REPL by building on top of the one that comes with Python. With a few lines of code, you can customize Python’s REPL environment as your own.
The Key to the key Parameter in Python – A parameter named key is present in several Python functions, such as sorted(). This article explores what it is and how to use it.
Serialize Your Data With Python – In this in-depth tutorial, you’ll explore the world of data serialization in Python. You’ll compare and use different data serialization formats, serialize Python objects and executable code, and handle HTTP message payloads.

Discussion:

You Are Never Taught How to Build Quality Software – Learning how to build quality software isn’t part of computer science education. How do we learn it?
Hacker News discussion about the article

Projects:

Additional Links:

PEP 8: The Style Guide for Python Code
Python’s Magic Methods: Leverage Their Power in Your Classes – Real Python
A Philosophy of Software Design Book - John Ousterhout
A Philosophy of Software Design - John Ousterhout - Talks at Google - YouTube
Episode #49: The Challenges of Developing Into a Python Professional – The Real Python Podcast
Django: Sanitize Incoming HTML Fragments With nh3 – Allowing users to input HTML in comments or blog posts is problematic and can lead to exploits on your site. For years, the Django community used django-bleach, but since its deprecation, Adam has been using nh3, a Rust-based HTML sanitizer.
nh3 · PyPI

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Exploring Python in Excel

Fri, 05 Jan 2024 12:00:00 +0000

Are you interested in using your Python skills within Excel? Would you like to share a data science project or visualization as a single Office file? This week on the show, we speak with Principal Architect John Lam and Sr. Cloud Developer Advocate Sarah Kaiser from Microsoft about Python in Excel.

John shares the multi-year journey of adding Python to Excel. He describes how the project moved beyond writing user functions in Python to something much more elaborate. He details assembling a team with diverse skills in interface design, languages, and security.

Sarah discusses the instant convenience of having familiar Python and pandas techniques at your fingertips inside Excel. We cover typical data science workflows and the potential of interactive visualizations within a spreadsheet. We also share multiple resources for you to learn more.

Note: Python in Excel is currently a preview accessible by joining the Microsoft 365 Insider Program and selecting the Beta Channel.

Course Spotlight: Data Cleaning With pandas and NumPy

In this video course, you’ll learn how to clean up messy data using pandas and NumPy. You’ll become equipped to deal with a range of problems, such as missing values, inconsistent formatting, malformed records, and nonsensical outliers.

Topics:

00:00:00 – Introduction
00:01:53 – Sr. Cloud Developer Advocate Sarah Kaiser
00:02:46 – Principal Architect John Lam
00:04:08 – What is Dev Div?
00:04:33 – Python data science inside Excel
00:09:05 – Designing features with a focus on sharing
00:14:28 – Moving between Excel and Python objects
00:18:20 – What libraries are imported by default?
00:23:11 – Sharing a workbook with others
00:26:12 – Recalculating data workflow
00:30:07 – Working in Jupyter Notebook vs Excel
00:33:03 – Creating a Python object
00:33:38 – Video Course Spotlight
00:35:02 – More history and project team
00:40:19 – Immediate wins of having Python in Excel
00:42:28 – Interactive visualizations
00:44:34 – Answering security concerns
00:49:17 – Limitations and potential
00:54:34 – Creating demo projects
01:00:25 – Resources to learn more
01:02:59 – What are you excited about in the world of Python?
01:10:41 – What do you want to learn next?
01:12:09 – How can people follow your work online?
01:13:26 – Thanks and goodbye

Show Links:

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2023 Real Python Tutorial & Video Course Wrap-Up

Fri, 29 Dec 2023 12:00:00 +0000

Three members of the Real Python team are joining us this week: Kate Finegan, Tappan Moore, and Philipp Acsany. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, code conversations, and video courses that showcase what our team created this year.

Kate helps to shepherd articles through the multi-stage editing process. She and the rest of the team ensure these resources impart crucial Python knowledge and provide a thorough didactic experience. Kate was also instrumental in helping introduce a new group of tutorial authors to the Real Python editorial process and house style.

Philipp returns to the podcast after our conversation earlier this year, and it was great to talk to him about onboarding new video instructors. Tappan edits all our video courses and ensures that the sound, picture, and animations are just right. He also helped provide feedback to the new instructors on our video creation process.

We hope you enjoy this review! We look forward to bringing you another year full of great guests, articles, and topics.

Course Spotlight: Recursion in Python

A recursive function is one that calls itself. In this video course, you’ll see what recursion is, how it works in Python, and under what circumstances you should use it.

Topics:

00:00:00 – Introduction
00:02:50 – New Video Instructors
00:05:31 – New Tutorial Authors
00:07:37 – Shout-out to Aldren Santos
00:09:27 – Build a Wordle Clone With Python and Rich
00:11:37 – Python Classes: The Power of Object-Oriented Programming
00:13:43 – SOLID Principles: Improve Object-Oriented Design in Python
00:14:47 – Using the NumPy Random Number Generator
00:17:58 – Recursion in Python
00:19:44 – Filtering Iterables With Python
00:21:04 – Creating Web Maps From Your Data With Python Folium
00:24:51 – Video Course Spotlight
00:26:13 – Python Basics Exercises: Building Systems With Classes
00:31:00 – Real Python Quizzes
00:33:53 – Process Images Using the Pillow Library and Python
00:36:00 – How to Sort Unicode Strings Alphabetically in Python
00:38:36 – The Python Rich Package: Unleash the Power of Console Text
00:42:31 – Embeddings and Vector Databases With ChromaDB
00:46:41 – Advent of Code: Solving Puzzles With Python
00:51:07 – Thanks and goodbye

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PyCoder's Weekly 2023 Wrap Up

Fri, 22 Dec 2023 12:00:00 +0000

It’s been a fascinating year for the Python language and community. PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2023. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and Python trends from across the year.

Christopher shares the top five links explored by PyCoder’s readers. We also dig into trends we noticed across all the articles and stories this year, including removing dead batteries from the standard library, continuing the effort to speed up Python, using Rust code in libraries, and moving away from microservices.

We hope you enjoy this review! We look forward to bringing you an upcoming year full of great Python news, articles, topics, and projects.

Course Spotlight: Python Basics: Building Systems With Classes

In this video course, you’ll learn how to work with classes to build complex systems in Python. By composing classes, inheriting from other classes, and overriding class behavior, you’ll harness the power of object-oriented programming (OOP).

Topics:

00:00:00 – Introduction
00:02:21 – Python 3.13.0 Alpha 2 Is Now Available
00:02:46 – Welcoming the Supporting Developer in Residence
00:03:25 – Django 5.0 Released
00:03:34 – Django Ninja 1.0 Released
00:04:11 – Top Five PyCoder Links
00:05:00 – Python 3.12: Cool New Features for You to Try
00:05:27 – Speeding Up Your Code When Multiple Cores Aren’t an Option
00:05:46 – Learning About Code Metrics in Python With Radon
00:06:07 – Python 3.12 Preview: More Intuitive and Consistent F-Strings
00:06:28 – Design and Guidance: Object-Oriented Programming in Python
00:07:49 – Python 3.12: What Didn’t Make the Headlines
00:11:34 – Python 3.13 Removes 20 Stdlib Modules
00:13:32 – Missing Batteries: Essential Libraries You’re Missing Out On
00:16:21 – More Batteries Please
00:19:19 – Three Python Trends in 2023
00:24:36 – Video Course Spotlight
00:26:23 – Mojo, a Superset of Python
00:27:54 – Why Mojo?
00:28:14 – Mojo SDK Released for Linux
00:28:19 – Mojo: Head-to-Head With Python and Numba
00:29:22 – How We Organize Our Very Large Python Monolith
00:34:11 – Python and Folium to Visualize My Outdoor Activities
00:37:13 – Thanks and goodbye

News:

Exploring Code Reviews in Python and Automating the Process

Fri, 08 Dec 2023 12:00:00 +0000

What goes into a code review in Python? Is there a difference in how a large organization practices code review compared to a smaller one? What do you do if you’re a solo developer? This week on the show, Brendan Maginnis and Nick Thapen from Sourcery return to talk about code review and automated code assistance.

Nick and Brendan discuss their personal experiences with code reviews. From working together at a large company to starting Sourcery as a pair of developers to growing a team, they’ve experienced how various organizations review code.

We discuss the importance of keeping code reviews positive and maintaining developer momentum. Nick and Brendan share techniques for automating organizational code rules and retaining institutional developer knowledge.

They’ve been busy developing new features for Sourcery, moving beyond the current refactoring features. Their team is leveraging LLMs to build an automated code assistant that can perform code reviews, write tests, and answer questions about your codebase. The goal is for it to act as a personalized computer pair programmer.

Course Spotlight: Using the bpython Enhanced REPL

In this video course, you’ll learn about bpython, an alternative Python REPL that brings code suggestions and many other IDE-like features to the terminal. Once you discover how much bpython can improve your productivity, you’ll never want to return to using the vanilla Python REPL again.

Topics:

00:00:00 – Introduction
00:02:41 – Why discuss code reviewing?
00:04:16 – What were your first code reviews like?
00:06:31 – Developing positive techniques as a team
00:14:07 – Time between reviews
00:17:04 – Will the techniques vary based on the organization size?
00:18:43 – Things to standardize and remove from code review
00:20:06 – Video Course Spotlight
00:22:06 – Creating a tool that goes beyond refactoring
00:23:42 – Do newer models provide better context for code review?
00:25:52 – Working toward an automated pair programmer
00:33:42 – Creating rules for an organization’s code standards
00:42:55 – Comparing completion tools with code assistance
00:48:18 – Working on an existing codebase vs a new project
00:52:11 – Current state of Sourcery
00:56:19 – What are you excited about in the world of Python?
01:00:12 – What do you want to learn next?
01:03:41 – How can people follow your work online?
01:05:17 – Thanks and goodbye

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Building a Python JSON Parser & Discussing Ideas for PEPs

Fri, 01 Dec 2023 12:00:00 +0000

Have you thought of a way to improve the Python language? How do you share your idea with core developers and start a discussion in the Python community? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We consider a couple of Python syntax and functional ideas posted to the discussions on python.org. The first idea is for simplifying the syntax of a function’s keyword arguments, and the second is for the ability to return a NamedTuple from a function. The threads reveal steps within the Python Enhancement Proposal (PEP) process and the goal of finding a sponsor.

Christopher covers a tutorial on building a JSON-like parser in Python. The project is a solid place to start if you want to learn about parsing and developing rules for recognizing syntax and extracting data.

We also share several other articles and projects from the Python community, including a couple of release announcements and news items, a step-by-step guide to building a hangman game for the command line in Python, the reasons why the Django admin is supposedly ugly and ways to customize it to differentiate admin environments, an explanation of confusing git terminology, a project to extract links from a remote HTML resource, and a regex crossword game.

Course Spotlight: Advent of Code: Solving Puzzles With Python

Advent of Code is an online advent calendar that shares new programming puzzles each day from December 1 to the 25. In this Code Conversation, you’ll learn why solving programming puzzles can be beneficial and how you can get started with Advent of Code using Python.

Topics:

00:00:00 – Introduction
00:02:46 – PyPI has completed its first security audit
00:03:55 – Python Developers Survey 2023
00:04:45 – Wagtail 5.2 (LTS) Released
00:05:02 – How many Python core devs use typing?
00:07:51 – Build a Hangman Game for the Command Line in Python
00:17:30 – Why Is the Django Admin “Ugly”?
00:20:37 – Customize the Django Admin to Differentiate Environments
00:22:48 – Confusing git Terminology
00:30:01 – Video Course Spotlight
00:31:31 – Let’s Make a Silly JSON-like Parser
00:34:31 – Idea: Return a NamedTuple
00:45:08 – Idea: Syntactic Sugar to Encourage Use of Named Arguments
00:50:31 – grablinks: Extract Links From a Remote HTML Resource
00:52:43 – Regex Crossword
00:55:46 – Thanks and goodbye

News:

Show Links:

Build a Hangman Game for the Command Line in Python – In this step-by-step project, you’ll learn how to write the game of hangman in Python for the command line. You’ll learn how to structure the game as a text-based interface (TUI) application.
Why Is the Django Admin “Ugly”? – When Vince was talking with people at DjangoCon US, one question kept coming up: Why is the Django admin so “ugly”?
Customize the Django Admin to Differentiate Environments – A quick post about changing the color scheme of the Django admin depending on what environment the code is deployed within. Although Django specific, this is a great idea—visually reminding your admins and developers whether they’re in dev, staging, or production.
Confusing git Terminology – Julia is working on a doc that explains git and, in doing so, polled some people about what git terminology they found confusing. This post covers the most common responses and attempts to clear up the confusion.
Let’s Make a Silly JSON-like Parser – This article goes into deep detail on how you would construct a JSON parser in Python. If you’re new to parsing, then this is a great place to start.

Discussion:

Projects:

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Computational Thinking & Learning Python During an AI Revolution

Fri, 17 Nov 2023 12:00:00 +0000

Has the current growth of artificial intelligence (AI) systems made you wonder what the future holds for Python developers? What are the hidden benefits of learning to program in Python and practicing computational thinking? This week on the show, we speak with author Lawrence Gray about his upcoming book “Mastering Python: A Problem Solving Approach.”

Lawrence shares how learning Python helped him through a dark and trying time. He developed lifelong skills that he wants to pass along through teaching and authoring a book.

We discuss what you can do to prepare for a future where coding jobs are automated through AI. He shares ways that Python can help build the higher-order thinking skills required by future careers. We also talk about how Python can help with computational thinking and promote cognitive development.

This week’s episode is brought to you by Site24x7.

Course Spotlight: Looping With Python enumerate()

Once you learn about for loops in Python, you know that using an index to access items in a sequence isn’t very Pythonic. So what do you do when you need that index value? In this course, you’ll learn all about Python’s built-in enumerate(), where it’s used, and how you can emulate its behavior.

Topics:

00:00:00 – Introduction
00:02:29 – Learning Python and changing how you think
00:05:07 – What is the goal of the book?
00:05:59 – Discovering Python during a dark period
00:10:32 – What is unique to Python that helped you?
00:12:43 – How did you start teaching Python?
00:14:40 – Teaching Python to art students
00:18:52 – Sponsor: Site24x7.com
00:19:48 – Why write about computational thinking?
00:21:55 – Why learn Python now?
00:25:18 – Multiple modalities for Python
00:29:32 – Other optimistic thoughts on the future
00:34:15 – Reskilling a workforce
00:37:16 – What skills can be developed?
00:41:13 – Video Course Spotlight
00:42:40 – Bloom’s taxonomy
00:48:00 – Sharing the positive impact of Python
00:50:26 – What are you excited about in the world of Python?
00:51:32 – What do you want to learn next?
00:52:45 – How can people follow your work online?
00:52:47 – Thanks and goodbye

Show Links:

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Studying Python Software Architecture & Creating Lambda Expressions

Fri, 10 Nov 2023 12:00:00 +0000

Have you moved through the fundamentals of Python, and are you now considering building a more extensive project or complete application? Where can you study the architecture of existing Python projects and learn best practices? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a set of resources that developers can study to learn how to structure projects. The collection was shared in a blog post titled “Great Resources a Beginner Might Not Find So Easily.” It includes a pair of books on the architecture of large software applications and another aimed at more modest projects.

We consider when you should use lambda expressions in your Python code. These one-line expressions create anonymous functions. How do they differ from standard functions, and where is it appropriate to use them?

We also share several other articles and projects from the Python community, including a couple of release announcements, an explanation of Python as a compiled language, a discussion covering the controversy about the recent Flask release, a project for writing less Selenium code, and a project to create ASCII art with Python.

This week’s episode is brought to you by Site24x7.

Course Spotlight: Design and Guidance: Object-Oriented Programming in Python

In this video course, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that is more maintainable, extensible, scalable, and testable.

Topics:

00:00:00 – Introduction
00:02:50 – PyCon US 2024 Call for Proposals
00:03:18 – Django 5.0 Beta 1 Released
00:03:29 – Django security releases issued: 4.2.7, 4.1.13, and 3.2.23
00:03:51 – The Ruff Formatter: Python’s Fastest Formatter
00:04:50 – What Are Lambda Expressions?
00:12:01 – Sponsor: Site24x7.com
00:12:56 – Python Is a Compiled Language
00:16:01 – Great Resources a Beginner Might Not Find So Easily
00:27:01 – Video Course Spotlight
00:28:32 – We Have to Talk About Flask
00:41:46 – selenium-python-helium: Write Less Selenium Code
00:45:23 – pyfiglet: An implementation of figlet written in Python
00:47:17 – Thanks and goodbye

News:

PyCon US 2024 Call for Proposals
Django 5.0 Beta 1 Released
Django security releases issued: 4.2.7, 4.1.13, and 3.2.23 | Weblog | Django
The Ruff Formatter: Python’s Fastest Formatter – You may have come across Ruff, a linter that’s been on the scene for about a year. Well, it recently added formatting to its features. This article shows you how, including the configuration option to allow single-quote style.

Show Links:

What Are Lambda Expressions? – This post teaches you what lambda expressions are and how they get used in Python. It shows several examples and also covers when to avoid lambda expressions.
Python Is a Compiled Language – Python is interpreted, but it interprets compiled code. This distinction can be confusing to students, and this instructor does a deep dive on where the line between the concepts sits.
Great Resources a Beginner Might Not Find So Easily – Are you having trouble making the modules work together in a larger project? Have you tried looking at popular projects as models, but did their size and scope put you off? Did you find it hard to see why they did what they did? Resources about this do exist, but they’re scattered all over…unless you know where to look.
We Have to Talk About Flask – The most recent release of Flask and Werkzeug have introduced backward incompatible changes that are affecting popular add-ons. Miguel writes about how this is a common occurrence and why it keeps happening.

Discussion:

Projects:

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Improving Your Git Developer Experience in Python

Fri, 03 Nov 2023 12:00:00 +0000

Are you getting by with a few fundamental commands for Git when building your Python projects? Would you like to improve your version control techniques and become more efficient with the Git command line? This week on the show, Adam Johnson is back to talk about his new book, “Boost Your Git DX.”

Adam brings advice about creating a Git configuration, enabling autocorrect, and customizing a global ignore file. He combed through the cryptic documentation and found the valuable developer settings, so you won’t have to.

Adam also shares tools and settings to speed up your terminal workflows. We discuss configuring Zsh, creating aliases, and comparing diffs. We briefly dig into the most recent update to Django and his work on the project.

This week’s episode is brought to you by Taipy.

Course Spotlight: Introduction to Git and GitHub for Python Developers

What is Git, what is GitHub, and what’s the difference? Learn the basics of Git and GitHub from the perspective of a Pythonista in this step-by-step video course.

Topics:

00:00:00 – Introduction
00:02:13 – When did you start working on this book?
00:06:11 – Git’s tendency to be inscrutable
00:08:57 – Audience for the book
00:09:47 – What have you learned about writing?
00:13:41 – What were you most excited to share?
00:14:34 – Global Git ignore
00:19:53 – Sponsor: Taipy
00:20:58 – Backing up configuration
00:22:37 – Git autocorrect
00:24:48 – Recovering from mistakes
00:27:58 – Creating terminal aliases in Zsh
00:31:59 – Using a Colemak keyboard layout
00:34:33 – Changing the diff-options to histogram
00:37:22 – Altering the global configuration file
00:39:36 – Video Course Spotlight
00:41:05 – Brief history of the Git project
00:44:27 – delta - syntax-highlighting pager for git, diff, and grep output
00:45:22 – pygments-git - Pygments lexers for Git output and files
00:46:56 – What are you excited about in the world of Git?
00:49:17 – What are you excited about in the world of Python?
00:52:51 – What do you want to learn next?
00:53:50 – How can people follow your work online?
00:55:23 – Thanks and goodbye

Show Links:

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Guiding Scientific Python Library Development

Fri, 27 Oct 2023 12:00:00 +0000

How do you prepare a scientific Python project for sharing with others? Could you use some best practices and guidance for packaging, documentation, and testing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about the creation of the Scientific Python Library Development Guide. The guide was finalized during the 2023 Scientific Python Developer Summit and is a resource for modern packaging. It includes sections of tutorials, principles, templates, and common patterns.

Christopher shares a recent Real Python tutorial about sorting Unicode strings in Python. He covers some of the pitfalls and ways to avoid them. The tutorial includes multiple third-party libraries to assist in wrangling Unicode characters.

We also share several other articles and projects from the Python community, including a couple of release announcements, information on namespaces and variable scope in Python, benchmark comparisons of Numba and Mojo, a discussion of recent so-called AI fails, a TUI for log files with a merged timeline, a cross-platform GUI building tool similar to HyperCard, and a project for reproducing exact argparse arguments.

Course Spotlight: Exploring Scopes and Closures in Python

Topics:

00:00:00 – Introduction
00:02:30 – Python 3.13.0 alpha 1 is now available
00:03:20 – core.py - Podcast
00:03:53 – PEP 730 - Adding iOS as a supported platform
00:05:22 – Mojo🔥 is now available on Mac
00:06:28 – How to Sort Unicode Strings Alphabetically in Python
00:11:48 – Python Variables: Namespaces and Variable Scope
00:18:18 – Mojo: Head-to-Head With Python and Numba
00:27:25 – Video Course Spotlight
00:28:50 – The Scientific Python Library Development Guide
00:32:47 – I’m banned for life from advertising on Meta
00:48:03 – logmerger: TUI for Log Files With Merged Timeline
00:50:34 – CardStock: Cross-Platform GUI Building Tool
00:54:24 – reverse_argparse: Tell the User What They Ran
00:56:25 – Thanks and goodbye

News:

Show Links:

How to Sort Unicode Strings Alphabetically in Python – In this tutorial, you’ll learn how to correctly sort Unicode strings in Python while avoiding common pitfalls. You’ll explore powerful third-party libraries implementing the complete Unicode Collation Algorithm (UCA), as well as standard library modules and a few handmade solutions.
Python Variables: Namespaces and Variable Scope – This post is a comprehensive guide on namespaces and variable scope. Learn about the four different namespaces and how to access each.
Mojo: Head-to-Head With Python and Numba – This article covers a Mandelbrot-based benchmark of Python, variations of Numba, and the newly available Mojo. Although Mojo is fast, it takes a lot more work than the author expected to translate Python to it, and with the right parameters, Numba still beats it.
The Scientific Python Development Guide – This article talks about the release of the Scientific Python Development Guide, which documents Python package development. It includes cookie-cutter templates and repo tools.

Discussion:

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Welcoming PyPI's Safety & Security Engineer Mike Fiedler

Fri, 20 Oct 2023 12:00:00 +0000

You may remember a recent Python Package Index (PyPI) announcement about hiring a full-time security engineer. We’ve also mentioned several current security initiatives from PyPI. This week on the show, we talk with Mike Fiedler about accepting this new role and securing accounts on PyPI.

Mike talks about how he started as a contributor to PyPI and eventually became a maintainer. We dig into why he fits this new role well and what his responsibilities are.

We discuss the initiative to secure accounts using two-factor authentication (2FA) methods. Mike also explains how package maintainers can adopt a new, more secure publishing method called trusted publishing that doesn’t require long-lived passwords.

We also discuss Mike’s recent talk called “How to Give Back to Open Source Without Losing Your Mind.” Mike shares advice and resources for finding your own contribution entry points.

Course Spotlight: Publishing Python Packages to PyPI

Topics:

00:00:00 – Introduction
00:02:11 – PyPI Safety and Security Engineer
00:05:21 – Why did you initially become a PyPI contributor?
00:11:26 – What are you most excited about in your new role?
00:12:02 – Current security concerns
00:15:07 – Focus on malicious package reporting
00:16:30 – 2FA enforcement and building trust
00:26:51 – Managing credentials and password managers
00:29:24 – Forms of 2FA
00:31:48 – Trusted publishers
00:38:08 – Video Course Spotlight
00:39:28 – Updating an older project
00:41:44 – Evolution of security
00:43:06 – Typosquatting and evolving security
00:49:13 – How To Give Back to Open Source Without Losing Your Mind
00:52:48 – What are you excited about in the world of Python?
00:54:45 – What do you want to learn next?
00:57:06 – How can people follow your work online?
00:57:37 – Thanks and goodbye

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Building Python Best Practices and Fundamental Skills

Fri, 13 Oct 2023 12:00:00 +0000

What fundamental developer skills are new Python users missing? What best practices might developers without a computer science background be lacking? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher opens our discussion by sharing a recent social media thread about teaching software engineering best practices to scientists and others without a computer science background. We talk about software design philosophy and strategies for sharing knowledge within an organization.

We cover the results from the sixth annual official Python Developer Survey. The survey covers Python usage, language versions, frameworks, libraries, and various demographics. We dig into the details and share our insights.

We also share several other articles and projects from the Python community, including a couple of release announcements, so many Python dataframes, ways to speed up your code when multiple cores aren’t an option, Python’s syntactic sugar, a computer algebra system named SymPy, the process of building a blog in Django, code metrics in Python with Radon, and a TUI app for daily writing.

This week’s episode is brought to you by Snyk.

Course Spotlight: Filtering Iterables With Python

In this video course, you’ll learn how Python’s filter() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace filter() and make your code more Pythonic.

Topics:

00:00:00 – Introduction
00:02:25 – Django 5.0 Alpha 1 Released
00:03:45 – Mojo SDK Released for Linux
00:04:50 – Python Developers Survey 2022 Results
00:13:44 – Why Are There So Many Python Dataframes?
00:20:24 – Sponsor: Snyk
00:21:15 – Speeding Up Your Code When Multiple Cores Aren’t an Option
00:28:41 – Python Is (Mostly) Made of Syntactic Sugar
00:32:57 – Towards a New SymPy
00:36:48 – Video Course Spotlight
00:37:54 – Building a Blog in Django
00:41:22 – What are fundamental skills and best-practices that might be lacking?
00:55:02 – Learning About Code Metrics in Python With Radon
00:59:43 – words-tui: A TUI App for Daily Writing
01:02:34 – Thanks and goodbye

News:

Show Links:

Python Developers Survey 2022 Results
Why Are There So Many Python Dataframes? – Ever wonder why there are so many ways libraries that have dataframes in Python? This article talks about the different perspectives of popular tool kits to explore why they are what they are.
Speeding Up Your Code When Multiple Cores Aren’t an Option – Parallelism isn’t the only answer: often you can optimize low-level code to get significant performance improvements.
Python Is (Mostly) Made of Syntactic Sugar – “Programming languages are often made up of a (mostly) irreducible core, with lots of sugary constructs sprinkled on top–the syntactic sugar.” This article summarizes a lot of Brett Cannon’s recent work exploring what’s sugar and what’s fundamental in Python.
Towards a New SymPy – SymPy is a computer algebra system, meaning that it does math on symbolic concepts. This can provide a lot more accuracy than typical floating-point mathematics found in numeric systems. This is part one of a multi-part article, explaining SymPy along with its recent improvements. Part two discusses how SymPy handles polynomials.
Building a Blog in Django – Very little code is needed to get a blog working using the Django framework. This post highlights what you need, including each of the key code components. There’s also an associated Hacker News discussion.

Discussion:

Projects:

Learning About Code Metrics in Python With Radon – Radon is a code metrics tool. This article introduces you to it and teaches you how you can improve your code based on its measurements.
words-tui: A TUI App for Daily Writing

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Exploring the New Features of Python 3.12

Fri, 06 Oct 2023 12:00:00 +0000

Python 3.12 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. Geir Arne coordinated a series of preview articles with several members of the Real Python team this year, and his showcase tutorial, “Python 3.12: Cool New Features for You to Try,” came out on October 2. Christopher’s video course was posted the next day, covering the topics from the article with visual examples of Python 3.12 in action.

Geir Arne and Christopher collaborated to create code examples of the new features. We discuss better error messaging, more intuitive f-strings, subinterpreters, the Linux perf profiler, improved typing syntax, and more.

We dig into the updates and offer advice on incorporating them into your projects. We also consider when you should start running Python 3.12.

Course Spotlight: What’s New in Python 3.12

In this video course, you’ll explore the new features that Python 3.12 brings to the table. These include improved f-strings, better error messages, changes to CPython internals, additions to static typing, and more.

Topics:

00:00:00 – Introduction
00:02:15 – Preview tutorials
00:05:08 – Ever better error messages
00:10:03 – More powerful f-strings
00:15:46 – Comprehension inlining & additional optimizations
00:21:24 – Support for subinterpreters
00:27:55 – Support for the Linux perf profiler
00:32:02 – Immortal objects
00:35:01 – Video Course Spotlight
00:36:03 – Calendar constants for days and months
00:38:48 – New itertools.batched() for grouping Items
00:40:53 – Improved type variable syntax
00:50:51 – New Path.walk() for listing files in subdirectories
00:53:04 – Override decorator for static typing
00:57:36 – Advice on upgrading
01:03:08 – Thanks to the core developer team
01:04:39 – Thanks to the Real Python team and goodbye

Show Links:

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Considering ChatGPT's Technical Review of a Programming Book

Fri, 29 Sep 2023 12:00:00 +0000

What can you learn from feeding an entire book on Python programming into ChatGPT-4 and asking it to provide a technical review? What are the potential pitfalls of using an LLM as a learning tool? This week on the show, author Al Sweigart talks about his recent experiments using ChatGPT and Python.

Al wanted to test how well an LLM could understand the computer science concepts covered in his recent book, The Recursive Book of Recursion. We talk about the positive and negative results of this technical review. We consider if this would be a valuable tool for technical review of your projects.

Al shares his thoughts on using ChatGPT as a learning tool and its potential pitfalls. We also cover the current strengths of this type of tool for Python developers.

Course Spotlight: Python Basics Exercises: Conditional Logic and Control Flow

In this Python Basics Exercises course, you’ll review how to use conditional logic to write programs that perform different actions based on different conditions. Paired with functions and loops, conditional logic allows you to write complex programs that can handle many different situations.

Topics:

00:00:00 – Introduction
00:01:51 – Reasons for experimenting
00:04:24 – Using ChatGPT to review his recursion book
00:10:45 – Why do this review?
00:11:34 – The trouble of confabulation
00:18:45 – Meaning of artificial intelligence over time
00:28:21 – Video Course Spotlight
00:29:54 – Detailed prompts and asking for specifics
00:36:44 – Potential pitfalls for learning
00:49:48 – Checking for flaws in generated code
00:54:58 – How can it be useful?
01:01:33 – Touching briefly on ethics
01:02:35 – What are you excited about in the world of Python?
01:05:21 – What do you want to learn next?
01:07:36 – Python Programming Exercises, Gently Explained
01:09:20 – How can people follow your work online?
01:09:49 – Thanks and goodbye

Show Links:

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Getting Involved in Open Source & Generating QR Codes With Python

Fri, 22 Sep 2023 12:00:00 +0000

Have you thought about contributing to an open-source Python project? What are possible entry points for intermediate developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article by Stefanie Molin called “5 Ways to Get Started in Open Source.” Christopher shares his experience with suggesting features and potential bug fixes. We talk about common entry points for beginners and provide additional resources.

We cover a recent Real Python tutorial about creating QR codes with Python. The tutorial introduces the library Segno and tours the features. By working through the examples, you’ll be ready to build a QR code project yourself.

We also cover several other articles and projects from the Python community, including a couple of release announcements, an introduction to Python’s functools module, Hatch as an alternative for packaging, options for when NumPy is too slow, a simple diceware generator project, and a collection of machine learning recipes.

Course Spotlight: Caching in Python With lru_cache

Caching is an essential optimization technique. In this video course, you’ll learn how to use Python’s @lru_cache decorator to cache the results of your functions using the LRU cache strategy. This is a powerful technique you can use to leverage the power of caching in your implementations.

Topics:

00:00:00 – Introduction
00:02:04 – 2023 Django Developers Survey
00:02:36 – Python 3.12.0 Release Candidate 2 Available
00:03:04 – Pandas 2.1.0 Released
00:03:27 – PEP 713: Callable Modules - Rejected
00:04:52 – Generate Beautiful QR Codes With Python
00:10:17 – Introduction to Python’s Functools Module
00:14:00 – Switching to Hatch
00:20:08 – Video Course Spotlight
00:21:27 – When NumPy is too slow
00:26:31 – 5 Ways to Get Started in Open Source
00:42:28 – nodice-cli: A simple diceware generator with no dependencies
00:44:32 – ML-Recipes: Collection of Machine Learning Recipes
00:47:00 – Thanks and goodbye

News:

Show Links:

Generate Beautiful QR Codes With Python – In this tutorial, you’ll learn how to use Python to generate QR codes, from your standard black-and-white QR codes to beautiful ones with your favorite colors. You’ll learn how to format QR codes, rotate them, and even replace the static background with moving images.
Introduction to Python’s Functools Module – This article introduces you to the functions in Python’s functools module with real-world examples to help show you how and when to use each feature.
Switching to Hatch – Oliver used Poetry for most of his projects, but he recently tried out Hatch instead. This blog post covers what it took to get things going and what features he used, including how he ditched tox.
When NumPy is too slow – NumPy is typically faster than plain Python for numeric calculations. What should you do when you find your NumPy-based code is too slow?

Discussion:

5 Ways to Get Started in Open Source – This article shares ideas for finding and making your first open-source contribution, using examples from contributions the author has made to various projects.
The In-Person Event Handbook
Development Sprints - PyCon US 2023
Hacktoberfest 2023

Projects:

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Measuring Multiple Facets of Python Performance With Scalene

Fri, 15 Sep 2023 12:00:00 +0000

When choosing a tool for profiling Python code performance, should it focus on the CPU, GPU, memory, or individual lines of code? What if it looked at all those factors and didn’t alter code performance while measuring it? This week on the show, we talk about Scalene with Emery Berger, Professor of Computer Science at the University of Massachusetts Amherst.

Emery talks about his background in memory management and his collaboration on Hoard, a scalable memory manager system used in Mac OS X. We discuss the need for improving code performance on modern computer architecture. He highlights this idea by contrasting the familiar limitations of Moore’s law with the lesser-known rule of Dennard scaling.

Working with his students in the university lab, they developed Scalene. Scalene is a high-performance CPU, GPU, and memory profiler. It can look at code from the individual function or line-by-line level and compare time spent in Python vs C code. Emery talks about the recent Scalene feature of AI-powered optimization proposals and covers a couple of examples. He also shares a collection of additional Python code-assistant tools from their lab.

Course Spotlight: What Does if name == “main” Mean in Python?

In this video course, you’ll learn all about Python’s name-main idiom. You’ll learn what it does in Python, how it works, when to use it, when to avoid it, and how to refer to it.

Topics:

00:00:00 – Introduction
00:02:13 – College of Information and Computer Sciences
00:03:25 – Memory management systems background
00:05:15 – Dennard Scaling vs Moore’s Law
00:10:12 – Starting work on Python profiling
00:15:00 – Deciding on a statistical profiler
00:17:05 – Wanting to trace memory
00:21:21 – Finding memory issues
00:23:59 – Line-by-line analysis
00:25:56 – Video Course Spotlight
00:27:14 – Measuring profiler performance
00:30:30 – Memory leak detection
00:34:31 – When should you run a profiler?
00:37:27 – Considerations for measuring cloud performance
00:39:12 – Working with Jupyter and Conda
00:42:18 – Common issues and AI solutions
00:45:50 – Using a profiler to learn a codebase
00:50:48 – Examples of AI-powered optimizations
00:55:50 – What are you excited about in the world of Python?
00:58:30 – What do you want to learn next?
01:01:48 – How can people follow your work online?
01:02:56 – Thanks and goodbye

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Making Each Line of Code Efficient & Python In Excel

Fri, 08 Sep 2023 12:00:00 +0000

Are you writing efficient Python with as few lines of code as possible? Are you familiar with the many built-in language features that will simplify your code and make it more Pythonic? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent post from Bob Belderbos titled “Make Each Line Count, Keeping Things Simple in Python.” We provide many of our favorite Pythonic examples and the language mistakes that we’ve learned from. We also share multiple resources to add to your learning path.

Microsoft has announced a limited beta program for Python in Excel. We dig into the current details, requirements, and potential use cases.

We cover several other articles and projects from the Python community, including a group of announcements from the Python Software Foundation, a showcase of the Polars DataFrame library, immortal objects in Python, a code image generator Python project, an MS Paint clone in the terminal, and a Django ORM cheatsheet.

Course Spotlight: Process Images Using the Pillow Library and Python

In this video course, you’ll learn how to use the Python Pillow library to deal with images and perform image processing. You’ll also explore using NumPy for further processing, including to create animations.

Topics:

00:00:00 – Introduction
00:02:10 – Python 3.12.0 release candidate 1 released
00:02:53 – PSF Announces New PyPI Safety & Security Engineer
00:03:44 – 2022 PSF Annual Report
00:04:13 – Pydantic has been downloaded … 1 BILLION times!
00:04:28 – Python Polars: A Lightning-Fast DataFrame Library
00:12:26 – Introducing Immortal Objects for Python
00:15:32 – Video Course Spotlight
00:17:01 – Introducing Python in Excel
00:26:34 – Build a Code Image Generator With Python
00:31:49 – Make Each Line Count, Keeping Things Simple in Python
00:44:08 – Textual-paint
00:46:04 – Django ORM Cheatsheet
00:49:48 – Thanks and goodbye

News:

Python Insider: Python 3.12.0 release candidate 1 released – “The second candidate (and the last planned release preview) is scheduled for Monday, 2023-09-04, while the official release of 3.12.0 is scheduled for Monday, 2023-10-02.”
Python 3.11.5, 3.10.13, 3.9.18, and 3.8.18 is now available - Python Insider
PSF Announces New PyPI Safety & Security Engineer
2022 PSF Annual Report – The annual report from the Python Software Foundation details all the changes and events at the PSF last year.
Pydantic has been downloaded … 1 BILLION times! - Twitter

Show Links:

Python Polars: A Lightning-Fast DataFrame Library – Welcome to the world of Polars, a powerful DataFrame library for Python! In this showcase tutorial, you’ll get a hands-on introduction to Polars’ core features and see why this library is catching so much buzz.
Introducing Immortal Objects for Python – This article explains immortal objects (PEP 683), which are excluded from garbage collection. This causes performance and shared memory improvements for large architectures.
Introducing Python in Excel – Microsoft has announced that they’re embedding Python in Excel through a partnership with Anaconda. Read on for details.
Build a Code Image Generator With Python – In this step-by-step tutorial, you’ll build a code image generator that creates nice-looking images of your code snippets to share on social media. Your code image generator will be powered by the Flask web framework and include exciting packages like Pygments and Playwright.

Discussion:

Make Each Line Count, Keeping Things Simple in Python – Simplicity is hard. This article talks briefly about how you approach coding while keeping things simple.
itertools — Functions creating iterators for efficient looping — Python documentation
More Itertools - more-itertools 10.1.0 documentation
Python enumerate(): Simplify Loops That Need Counters – Real Python
Python’s all(): Check Your Iterables for Truthiness – Real Python
How to Use any() in Python – Real Python

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Finding the Right Coding Font for Programming in Python

Fri, 01 Sep 2023 12:00:00 +0000

What should you consider when picking a font for coding in Python? What characters and their respective glyphs should you check before making your decision? This week on the show, we talk with Real Python author and core team member Philipp Acsany about his recent article, Choosing the Best Coding Font for Programming.

Philipp shares some of his background as a font engineer and graphic designer. We talk about how font design tools were his introduction to programming in Python.

We discuss how the frequent use of underscores, at signs, parentheses, and asterisks in Python’s syntax should affect your decision. Philipp’s tutorial provides several resources to help you find a monospace font that fits your coding requirements.

Course Spotlight: Create a Python Wordle Clone With Rich

In this step-by-step project, you’ll build your own Wordle clone with Python. Your game will run in the terminal, and you’ll use Rich to ensure your word-guessing app looks good. Learn how to build a command-line application from scratch and then challenge your friends to a wordly competition!

Topics:

00:00:00 – Introduction
00:01:32 – Previous podcast appearances
00:03:18 – Programming-environment fussiness and monospace fonts
00:07:29 – Researching the tutorial and curating the collection
00:10:51 – Philipp’s background
00:18:07 – Differentiating characters
00:21:37 – Monospace, typewriters, and alignment
00:25:08 – Character sets to study
00:32:38 – The comma and the period
00:37:04 – Video Course Spotlight
00:38:30 – Python’s use in font development
00:42:48 – Different fonts for different languages
00:49:27 – Non-English comments
00:55:40 – Our font choices
00:59:00 – What are you excited about in the world of Python?
01:01:35 – What do you want to learn next?
01:03:41 – Where can people follow your work online?
01:04:38 – Thanks and goodbye

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Improving Classification Models With XGBoost

Fri, 25 Aug 2023 12:00:00 +0000

How can you improve a classification model while avoiding overfitting? Once you have a model, what tools can you use to explain it to others? This week on the show, we talk with author and Python trainer Matt Harrison about his new book Effective XGBoost: Tuning, Understanding, and Deploying Classification Models.

Matt talks about the process of developing the book and how he wanted it to be an interactive experience for the reader. He explains the concept of gradient boosting and provides metaphors for developing a model. He shares his appreciation for exploratory data analysis as a crucial step in understanding your data.

He also shares additional libraries to help you explain your model. We discuss how difficult it is to develop the story of how the model works to share it with stakeholders.

He illustrates why covering the complete process is essential, from exploring data and building a model to finally deploying it. He shares many of the tools he found along the way.

This week’s episode is brought to you by Scout APM.

Course Spotlight: Starting With Linear Regression in Python

In this video course, you’ll get started with linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.

Topics:

00:00:00 – Introduction
00:02:16 – Starting on the book
00:04:36 – What is tabular prediction?
00:06:50 – Who could leverage XGBoost?
00:09:46 – Background to get started
00:11:50 – Using XGBoost to explore data
00:21:06 – Sponsor: ScoutAPM
00:21:54 – Focusing on using the tool
00:26:37 – Not being a developer
00:30:53 – Contrasting XGBoost and logistic regression
00:41:57 – Video Course Spotlight
00:43:21 – Using SHAP to explain the model
00:48:06 – Working with hyperparameters
00:51:40 – Deploying your model
00:53:09 – XGBoost Feature Interactions Reshaped (XGBFIR)
00:55:47 – Communicating the story of a model
00:57:57 – How to find the book
00:59:07 – What are you excited about in the world of Python?
01:02:46 – What do you want to learn next?
01:03:12 – How can people follow what you do online?
01:03:59 – Thanks and goodbye

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Common Python Stumbling Blocks & Quirky Behaviors

Fri, 11 Aug 2023 12:00:00 +0000

Have you ever encountered strange behavior when trying something new in Python? What are common quirks hiding within the language? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent blog post that lists a collection of quirky Python behaviors. We share a few examples with explanations but leave several as puzzles to dig into.

Christopher transitions our discussion into Python features that can be difficult to explain to a new programmer. We also share some of our own stumbling blocks while learning the language.

We cover several other articles and projects from the Python community, including a news update, previewing Python 3.12’s more intuitive and consistent f-strings, finding performance bottlenecks with profiling, emulating the 6502 processor in Python, using Rich to inspect Python objects, and plotting statistical data with Lets-Plot.

This week’s episode is brought to you by Porkbun.

Course Spotlight: Graph Your Data With Python and ggplot

Topics:

00:00:00 – Introduction
00:02:12 – Steering Council Announces Optional GIL
00:04:05 – Polars is Starting a Company
00:05:13 – Python 3.12 Preview: More Intuitive and Consistent F-Strings
00:09:29 – Sponsor: Porkbun
00:10:22 – Profiling in Python: How to Find Performance Bottlenecks
00:21:01 – Writing a 6502 Emulator in Python
00:24:36 – Python Quirks
00:32:30 – Video Course Spotlight
00:34:02 – What Python feature would you have trouble explaining to a new programmer?
00:42:25 – Using Rich Inspect to Interrogate Python Objects
00:44:36 – Lets-Plot: Plotting Library for Statistical Data
00:48:01 – Thanks and goodbye

News:

Show Links:

Python 3.12 Preview: More Intuitive and Consistent F-Strings – In this tutorial, you’ll preview one of the upcoming features of Python 3.12, which introduces a new f-string syntax formalization and implementation. The new implementation lifts some restrictions and limitations that affect f-string literals in Python versions lower than 3.12.
Profiling in Python: How to Find Performance Bottlenecks – In this tutorial, you’ll learn how to profile your Python programs using numerous tools available in the standard library, third-party libraries, as well as a powerful tool foreign to Python. Along the way, you’ll learn what profiling is and cover a few related concepts.
Writing a 6502 Emulator in Python – The 6502 processor from Motorola was quite popular and could be found in the Nintendo and Sega consoles as well as the Commodore 64. This very detailed article shows you how to build an emulator for the processor in Python.
Python Quirks – A straight-out list of code snippets showing off some of the weird and unexpected behavior of your favorite language.

Discussion

What Python feature would you have trouble explaining to a new programmer? - Trey Hunner on Twitter

Projects:

Using Rich Inspect to Interrogate Python Objects – You might know the Rich library as the terminal color tool, but it has a few utilities that are generally helpful in your code. This article shows you the inspect feature, which gives you loads of information on an object.
Lets-Plot: Plotting Library for Statistical Data

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Exploring pandas 2.0 & Targets for Apache Arrow

Fri, 04 Aug 2023 12:00:00 +0000

What are the new ways to describe your data in pandas 2.0? Will the addition of Apache Arrow to the data back end foster the growth of data interoperability? This week on the show, we talk with pandas core developer Marc Garcia about the release of pandas 2.0.

Marc shares his background and work on pandas. We discuss the history of data representation in pandas and the need to move beyond NumPy. We also talk about how Apache Arrow only solves some of the issues.

We dig into the potential of an Apache Arrow back end and how it could offer interoperability between data platforms. We also cover the moderate adoption and backward-compatibility concerns. Marc also shares his thoughts on making pandas more extensible.

Course Spotlight: The pandas DataFrame: Working With Data Efficiently

In this course, you’ll get started with pandas DataFrames, which are powerful and widely used two-dimensional data structures. You’ll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a pandas DataFrame.

Topics:

00:00:00 – Introduction
00:02:07 – Getting involved with the pandas project
00:03:48 – Continued growth of the platform
00:06:49 – Parallel branch development
00:09:19 – The introduction of Apache Arrow
00:18:53 – Working with NumPy data in pandas
00:30:18 – Arrow data types and strings
00:41:23 – Video Course Spotlight
00:42:37 – Interoperability of Arrow data back end
00:50:36 – Could pandas be more extensible?
01:00:49 – Python DataFrame Summit 2023
01:08:12 – What are you excited about in the world of Python?
01:11:13 – What do you want to learn next?
01:12:12 – How can people follow your work online?
01:13:46 – Thanks and Goodbye

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Differentiating the Versions of Python & Unlocking IPython's Magic

Fri, 28 Jul 2023 12:00:00 +0000

What are all the different versions of Python? You may have heard of Cython, Brython, PyPy, or others and wondered where they fit into the Python landscape. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article from the Bite Code blog about all the different forms that Python can take. CPython is the reference implementation of the language, which is what we usually discuss. He lists several alternative projects and the use cases.

We also discuss a recent Real Python tutorial about IPython. IPython is an interactive Python shell from the team that developed Jupyter Notebooks. It includes a set of IDE-like features and unique magic commands. The tutorial digs into using the tool to learn more about Python and explore your code.

We cover several other articles and projects from the Python community, including several news updates, the state of WASI support for CPython, how Python uses garbage collection, a discussion about the current AI echo chamber, an async Python web microframework, a stand-alone CSV editor, and a project for identifying unused dependencies to avoid a bloated virtual environment.

This week’s episode is brought to you by Scout APM.

Course Spotlight: Mazes in Python Part 1: Building and Visualizing

In this two-part video course project, you’ll build a maze solver in Python using graph algorithms from the NetworkX library. Along the way, you’ll design a binary file format for the maze, represent it in an object-oriented way, and visualize the solution using scalable vector graphics (SVG).

Topics:

00:00:00 – Introduction
00:02:19 – Python 3.12.0 Beta 4 Released
00:02:53 – Django in Action - Christopher’s Book
00:04:28 – State of WASI Support for CPython: June 2023
00:08:32 – What’s the Deal With CPython, PyPy, MicroPython, Jython…?
00:12:10 – Sponsor: Scout APM
00:12:57 – Unlock IPython’s Magical Toolbox for Your Coding Journey
00:18:33 – How Python Uses Garbage Collection
00:21:31 – Video Course Spotlight
00:23:04 – Are People in Tech Inside an AI Echo Chamber?
00:39:39 – quart: An Async Python Web Microframework
00:41:33 – Modern CSV: CSV Editor/Viewer
00:43:09 – creosote: Identify Unused Dependencies
00:45:09 – Thanks and Goodbye

News:

Show Links:

State of WASI Support for CPython: June 2023 – This post from Brett Cannon covers the current state of WebAssembly targets in Python.
What’s the Deal With CPython, PyPy, MicroPython, Jython…? – This comprehensive article introduces you to all the different ways that you can Python. CPython isn’t the only choice. Learn what else is out there and why you might choose an alternative.
Unlock IPython’s Magical Toolbox for Your Coding Journey – IPython is a powerful tool that can prove useful on your journey to mastering Python. Its friendly interface will enable you to comfortably take control of your learning. In this tutorial, you’ll cover the basic concepts of using IPython and learn how its features can make coding efficient.
How Python Uses Garbage Collection – This article outlines how Python stores variables as references and how that relates to memory management

Discussion:

Projects:

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Leveraging the Features of Your Database With Postgres and Python

Fri, 21 Jul 2023 12:00:00 +0000

Are you getting the most out of your Postgres database? What features could you leverage to improve your Python project? This week on the show, Craig Kerstiens from Crunchy Data is here to discuss getting the most out of Postgres.

Craig shares his years of PostgreSQL expertise with advice on getting more from the platform. We talk about rich data types for describing ranges, geospatial data, and JSON.

Craig shares tools for accessing performance statistics from the command line and strategies for optimizing your terminal settings for SQL searches. He discusses Postgres extensions for customizing the database to your needs. Craig also provides multiple resources for learning more and an online tool for practicing within a playground environment.

Course Spotlight: Creating Web Maps From Your Data With Python Folium

Topics:

00:00:00 – Introduction
00:02:36 – What are reasons for considering Postgres?
00:07:41 – Timeline of recent features
00:11:06 – Unique data types
00:16:34 – Storing JSON information
00:20:19 – Video Course Spotlight
00:21:50 – Storing geographic information
00:25:16 – What’s the process for adding extensions?
00:31:33 – Advice for Python developers using Postgres
00:33:31 – Advice on writing SQL
00:38:06 – Command-line tools and customizations
00:48:18 – Django as an entry to Python
00:51:13 – Resources for learning and practicing with Postgres
00:53:45 – What are you excited about in the world of Python?
00:55:55 – What do you want to learn next?
00:58:34 – How can people follow your work online?
00:59:20 – Thanks and goodbye

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Constructing Python Library APIs & Tackling Jinja Templating

Fri, 14 Jul 2023 12:00:00 +0000

What principles should you consider when designing a Python library? How do you construct a library API that’s understandable and easy to use? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share an article about building library APIs. The piece provides advice for package structure, naming, error handling, and more. The author guides you toward Pythonic principles by comparing clunky vs elegant design examples.

Christopher discusses his recent video course on Jinja templating. The course covers creating text files with programmatic content and employing rich templates to structure the front end of Python web applications.

We cover several other articles and projects from the Python community, including several news updates, reasons why membership tests are fast for the range() function, CLI tools hidden in the Python standard library, a thread about the right way to install Python, recipes for using the Polars library, and a project for feature flags within Django.

This week’s episode is brought to you by Snyk.

Course Spotlight: Jinja Templating

With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.

Topics:

00:00:00 – Introduction
00:02:16 – PyLadies Conference (Dec 2023) Call for Volunteers
00:02:32 – PSF Board Election Results
00:03:47 – PSF Announces New Security Developer in Residence
00:04:39 – Django Security Releases Issued
00:04:50 – Deprecation of bdist_egg Uploads to PyPI
00:05:54 – Why Are Membership Tests So Fast for range() in Python?
00:11:51 – CLI Tools Hidden in the Python Standard Library
00:15:59 – Sponsor: Snyk
00:16:49 – Designing Pythonic Library APIs
00:28:27 – Jinja Templating
00:32:49 – Kill a Developer in 4 Words or Less
00:37:28 – Video Course Spotlight
00:38:51 – What is “the right way” to install Python on a new M2 MacBook?
00:44:11 – polars-cookbook: Recipes for Using Python’s Polars Library
00:46:48 – waffle: Feature Flags for Django
00:49:54 – Thanks and goodbye

News:

PyLadies Conference (Dec 2023) Call for Volunteers
PSF Board Election Results
PSF Announces New Security Developer in Residence
I Am the First PSF Security Developer-in-Residence – Seth was recently hired as the first security developer in residence at the PSF. His blog post talks about what his responsibilities are and how he defines success for the position.
Deputy CPython Developer in Residence - Python Software Foundation - Career Page
Django Security Releases Issued: 4.2.3, 4.1.10, and 3.2.20
Deprecation of bdist_egg Uploads to PyPI – PEP 715 has been accepted and as of August 1, 2023, the .egg format will no longer be accepted as an upload. Existing eggs on PyPI will remain in place.

Show Links:

Why Are Membership Tests So Fast for range() in Python? – In Python, range() is most commonly used in for loops. However, ranges have some other use cases too, as they share many properties with lists. In this tutorial, you’ll explore why it’s so fast to perform a membership test on a Python range.
CLI Tools Hidden in the Python Standard Library – There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty-print JSON. This article introduces you to what options are available and how Simon discovered them.
Designing Pythonic Library APIs – This article summarizes principles that Ben has found useful when designing Python library APIs. Topics include structure, naming, error handling, and type annotations.
Jinja Templating – With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.

Discussion:

Projects:

Additional Links:

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Python Crash Course & Learning Enough to Start Creating

Fri, 07 Jul 2023 12:00:00 +0000

How much Python do you need to learn to start creating projects? What’s a good balance of information and hands-on practice? This week on the show, Eric Matthes is here to discuss his book Python Crash Course.

As a former high school science, math, and programming teacher, Eric saw something missing in the programming publishing landscape. We discuss the guiding questions that inspired the book’s development and the title. Eric covers how the crash course takes readers through a fast-paced introduction to Python that culminates in three unique projects.

We also discuss Eric’s blog, Mostly Python, where he digs deeper into technical subjects. He also occasionally shares more topical posts and includes many exercises.

Course Spotlight: Plot With pandas: Python Data Visualization Basics

In this course, you’ll get to know the basic plotting possibilities that Python provides in the popular data analysis library pandas. You’ll learn about the different kinds of plots that pandas offers, how to use them for data exploration, and which types of plots are best for certain use cases.

Topics:

00:00:00 – Introduction
00:02:04 – Why Alaska?
00:05:33 – What do you like about Python?
00:06:40 – Becoming an author & the intended reader
00:15:56 – History of programming books
00:17:39 – Having a purpose for learning
00:20:22 – Exercising as you go
00:24:45 – Including projects in the book
00:26:32 – Is the “crash course” a book model?
00:29:20 – Video Course Spotlight
00:30:29 – How much of a topic to reveal
00:35:08 – Guiding questions
00:42:43 – Moving from functions to testing in four chapters
00:46:21 – What were you excited to update in the book?
00:50:28 – Were there doubts about preparing readers for projects?
00:53:52 – Space invaders clone pygame project
00:55:52 – Data visualization project
01:00:38 – Django web app project
01:04:15 – Cheat sheets and additional resources
01:07:54 – Python flash cards
01:09:54 – What are you excited about in the world of Python?
01:10:54 – What do you want to learn next?
01:11:46 – How can people follow the work you do?
01:13:06 – Thanks and goodbye

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Exploring the Zen of Python & pandas Features for Finance

Fri, 30 Jun 2023 12:00:00 +0000

What advice can you extract from the Zen of Python? How can these nineteen guiding principles help you write more idiomatic Python? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a Real Python tutorial by Bartosz Zaczyński titled “What’s the Zen of Python?” We talk about the poem’s origin and ways to access the Zen within Python. We also discuss how different sections provide contradictory advice for what makes good Python code.

We cover a recent post by previous guest Matt Harrison about using Python and pandas for finance. Matt’s article covers methods in the pandas library for aggregation, resampling, and rolling averages.

We cover several other articles and projects from the Python community, including staying up-to-date with news, solving a Legend of Zelda puzzle with Python, avoiding “simply” providing advice, displaying better stack traces, and creating files with fake data.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

Topics:

00:00:00 – Introduction
00:01:49 – Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and Python 3.12.0 beta 3 released
00:02:24 – Enforcement of 2FA for PyPI Began June 1st
00:02:54 – Faster Python 3.13 Plan
00:03:14 – PyPy v7.3.12 Released
00:03:28 – Migrate to .readthedocs.yaml Configuration
00:05:14 – PyCon US 2023 and PyCascades 2023 Videos Are Up
00:05:37 – What’s the Zen of Python?
00:09:22 – Python for Finance: Pandas Resample, Groupby, and Rolling
00:15:24 – Python and the Legend of Zelda
00:17:47 – Don’t Say “Simply” Use Pyenv, Poetry or Anaconda
00:28:54 – Is Parallel Programming Hard?
00:40:54 – Video Course Spotlight
00:42:20 – pymg: A Better Display for Stack Traces
00:44:58 – faker-file: Create Files With Fake Data
00:49:14 – Thanks and goodbye

News:

Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and 3.12.0 Beta 2 Released
Python Insider: Python 3.12.0 beta 3 released
Enforcement of 2FA for PyPI Began June 1st – For those accounts that have two-factor authentication turned on for PyPI uploads, the use of 2FA is now required. Users with 2FA who were only using a password in the past will now have to perform 2FA as well. This is all part of the PyPI transition to 2FA across the board.
Faster Python 3.13 Plan – This brief outline highlights the plan for the faster CPython project for the 3.13 release. It includes PEP 669, PEP 554, improved memory management, and more. Here’s the associated Hacker News discussion.
PyPy v7.3.12 Released
Migrate to .readthedocs.yaml Configuration – The Read the Docs site has announced the new requirement that all builds must move to using a .readthedocs.yaml configuration file, version 2. There are some test windows where they’ll be temporarily enforcing the change, but the final release date is September 25, 2023. Read on for details on how to migrate your project.
PyCascades 2023 Videos Are Up
PyCon US 2023 Videos Are Up

Topic Links:

What’s the Zen of Python? – In this tutorial, you’ll be exploring the Zen of Python, a collection of nineteen guiding principles for writing idiomatic Python. You’ll find out how they originated and whether you should follow them. Along the way, you’ll uncover several inside jokes associated with this humorous poem.
Python for Finance: Pandas Resample, Groupby, and Rolling – When working with time series data such as financial information, the resample, grouping, and rolling features of pandas can make your life easier.
Python and the Legend of Zelda – The Game Boy Color version of Legend of Zelda: Oracle of Ages contains a grid-based puzzle. Gaz writes about creating a brute-force program to solve the challenge using Python.
Don’t Say “Simply” Use Pyenv, Poetry or Anaconda – This article talks about the issues that newer Python coders might encounter by adopting more complicated package management mechanisms and explains why sticking with pip is often the better choice.

Discussion:

Is Parallel Programming Hard?
AsyncIO: Why I Hate It – Charles is the creator of Peewee ORM and often gets the question when will it support asyncio? In this opinion piece, he talks about why he doesn’t like asyncio and which alternatives he prefers.

Projects:

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Resources and Advice for Building CircuitPython Projects

Fri, 23 Jun 2023 12:00:00 +0000

Are you looking to advance your CircuitPython projects? Would you like a collection of resources and tools to help you along your path? This week on the show, Tod Kurt is here to discuss building projects with CircuitPython.

Tod has been working with embedded electronics for a long time and has been an active member of the Arduino community. He recently started to build projects using CircuitPython, and it has become his preferred prototype method.

He shares software resources, hardware tools, and advice about working with CircuitPython and embedded electronics. We discuss several of his music hardware projects and the libraries and modules that he uses for synthesis. He also shares a powerful online prototyping tool to plan your project before spending money or plugging in a soldering iron.

Course Spotlight: Python Basics: Reading and Writing Files

In this video course, you’ll learn how to move data back and forth between your Python programs and external software by reading and writing files. You’ll practice reading and writing data stored in the CSV file format, one of the most widely supported file formats for transferring tabular data.

Topics:

00:00:00 – Introduction
00:02:17 – Tod’s background and ThingM
00:06:37 – The Bootloader Podcast
00:09:58 – Why did you move from C to Python?
00:13:47 – Starting new projects with CircuitPython instead of Arduino
00:17:14 – Advantages of the Arduino language
00:21:07 – Modules that extend CircuitPython with C
00:25:23 – Synthesis and sample playback in CircuitPython
00:32:54 – Video Course Spotlight
00:34:13 – ulab for NumPy-like functionality in CircuitPython
00:39:20 – Raspberry Pi Pico as inexpensive project board
00:46:08 – STEMMA QT and breadboards
00:48:40 – Powering projects with a USB power bank
00:51:25 – Software tools - tio, circup, and discotool
00:56:55 – CircuitPython tricks GitHub repo
00:59:44 – Wokwi simulator for CircuitPython and Raspberry Pi Pico
01:01:25 – PCB board design resources
01:03:27 – todbot projects on Tindie
01:06:11 – Hardware tools for projects
01:08:21 – What are you excited about in the world of Python?
01:09:42 – What do you want to learn next?
01:11:30 – How can people follow your work online?
01:12:50 – Thanks and goodbye

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Inheriting a Large Python Code Base & Building a GUI With Kivy

Fri, 16 Jun 2023 12:00:00 +0000

What are the unique challenges of a large Python code base? What techniques can you implement to simplify the management of a big project? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent thread on Hacker News about working with a large Python code base. Christopher advises configuring tests and using tools to keep your code consistent across an organization. He also answers several questions about code complexity, typing, and leveraging third-party libraries.

We cover several other articles and projects from the Python community, including news from the Python Language Summit, Kivy for GUI development, the power of bit manipulation, the removal of unused import statements in your code, and a Python-based iOS project for adding reminders about links from a podcast.

Course Spotlight: Build Cross-Platform GUI Apps With Kivy

In this video course, you’ll learn how to build a cross-platform mobile application with Python and the Kivy GUI framework. You’ll discover how to develop an application that can run on your desktop as well as your phone. Then, you’ll package your app for Windows, Linux, and macOS.

Topics:

00:00:00 – Introduction
00:01:43 – Python 3.13 Removes 20 Stdlib Modules
00:03:20 – Making the Global Interpreter Lock Optional
00:04:52 – What Is the Standard Library For?
00:06:14 – Python’s .__call__() Method: Creating Callable Instances
00:11:40 – The Power of Bit Manipulation
00:15:39 – Using Kivy for GUI Development
00:21:18 – Video Course Spotlight
00:22:43 – How Do You Deal With Large Python Code Bases?
00:40:50 – unimport: Remove Unused Import Statements in Your Code
00:42:36 – Update: rsync-time-machine.py
00:43:20 – memocast: Add links heard in podcasts into iOS reminders
00:47:42 – A request to listeners
00:48:20 – Thanks and goodbye

Show Links:

Python 3.13 Removes 20 Stdlib Modules – Core developers are busy working on PEP 594, removing dead batteries from Python 3.13. This long post in the discussion forum highlights what work has been completed so far.
Making the Global Interpreter Lock Optional – In the past, Sam Gross has outlined how to make the GIL optional in CPython. He presented at the Python Language Summit, providing updates on his progress and future plans for the project.
PEP 703 – Making the Global Interpreter Lock Optional in CPython | peps.python.org
PEP 703 - Discussion on Python.org
What Is the Standard Library For? – This post summarizes a conversation at the Python Language Summit proposing that guidelines be defined for when something should be added to the standard library.
Python’s .__call__() Method: Creating Callable Instances – In this tutorial, you’ll learn what a callable is in Python and how to create callable instances using the .__call__() special method in your custom classes. You’ll also code several examples of practical use cases for callable instances in Python.
The Power of Bit Manipulation – In this article, you learn about bit manipulation and how to solve problems efficiently using it in Python.
Using Kivy for GUI Development – Kivy is an open-source Python library for developing desktop and mobile GUI apps. It’s supported on Windows, Linux, macOS, Android, and iOS. This article introduces you to Kivy and teaches you how to build your first GUI with it.

Discussion:

How Do You Deal With Large Python Code Bases?

Projects:

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Volunteering, Organizing, and Finding a Python Community

Fri, 09 Jun 2023 12:00:00 +0000

Have you thought about getting more involved in the Python community? Are you interested in volunteering for an event or becoming an organizer? This week on the show, we speak with organizers from this year’s PyCascades conference about making connections, learning new skills, and rationing your time.

We have three guests to discuss working on PyCascades 2023 and how they got involved in volunteering. Conference Chair Ben Berry, a site reliability engineer based in Seattle, is currently working on a private platform-as-a-service. Diversity Chair Madison Swain-Bowden is a senior data engineer out of Seattle, currently working at Automattic on the Openverse team. Sponsorship Chair Michael van der Kamp is a back-end engineer at Coffee Meets Bagel.

We discuss finding other volunteers, maintaining motivation, and connecting with sponsors. Our guests also share their stories of overcoming challenges, connecting with community, and finding fulfillment through volunteering.

This week’s episode is brought to you by Proxify.

Course Spotlight: Using pandas to Make a Gradebook in Python

Topics:

00:00:00 – Introduction
00:02:34 – Conference Chair Ben Berry
00:03:58 – Diversity Chair Madison Swain-Bowden
00:06:04 – Sponsorship Chair Michael van der Kamp
00:08:28 – Scheduling of conferences around one another
00:10:41 – How did you get involved in the community?
00:20:01 – Sponsor: Proxify
00:20:57 – Workplace groups and guilds
00:27:41 – How community helps with job referrals
00:32:19 – New position and added responsibilities
00:41:24 – How would you pitch volunteering?
00:44:32 – Video Course Spotlight
00:45:45 – How do you avoid overdoing it?
00:52:46 – What are you excited about in the world of Python?
00:55:16 – What do you want to learn next?
00:59:22 – Thanks and goodbye

Show Links:

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Support the podcast & join our community of Pythonistas

Building Python CI With Docker & Applying for a Hacker Initiative Grant

Fri, 02 Jun 2023 12:00:00 +0000

Do you need a refresher on using Docker with Python? Would you like to learn how to configure a continuous integration pipeline with modern tools and Docker? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a Real Python tutorial from Bartosz Zaczyński about building continuous integration with Docker. Docker provides consistent environments for configuring, testing, and delivering Python applications. In this tutorial, you’ll get up to speed with current Docker and CI techniques.

We also speak with Bill Pollock from No Starch Press about Hacker Initiative. The public nonprofit gives back to and strengthens the hacking community. The 2023 grant cycle is currently open until August 15. We discuss the application process and projects from previous grant recipients.

We cover several other articles and projects from the Python community, including a news update and some resources on implementing metaclasses in Python, creating time machine-style backups, and scanning your project for vulnerabilities.

Course Spotlight: Metaclasses in Python

Metaclasses are an important but mysterious behind-the-scenes mechanism for instantiating classes in Python. In this video course, you’ll learn how Python’s metaclasses work in object-oriented programming.

Topics:

00:00:00 – Introduction
00:02:26 – PyPI Temporarily Suspended New Registrations
00:03:38 – PyPI was subpoenaed
00:04:54 – Python 3.12.0 beta 1 released
00:05:19 – Build Robust Continuous Integration With Docker and Friends
00:13:02 – Metaclasses in Python
00:20:03 – Pronouncing SQL - Is there life after SQL?
00:22:52 – Video Course Spotlight
00:24:25 – pyscan: Python dependency vulnerability scanner, written in Rust
00:26:28 – rsync-time-machine.py: Time Machine-Style Backups
00:29:20 – Bill Pollock and Hacker Initiative
00:34:18 – Previous grant recipients
00:36:58 – First Tech Challenge
00:43:18 – Large vs small nonprofits
00:45:48 – Applying for the 2023 grant cycle
00:49:01 – Underserved areas
00:51:45 – Thanks and goodbye

News:

PyPI Temporarily Suspended New Registrations – Due to a large volume of traffic from malicious users, PyPI temporarily suspended new account and project registrations on May 20. The suspension was lifted thirty hours later on May 21.
PyPI was subpoenaed - The Python Package Index – The PSF received three subpoenas from the US Department of Justice for PyPI user data in March and April of 2023.
Python Insider: Python 3.12.0 beta 1 released

Show Links:

Build Robust Continuous Integration With Docker and Friends – In this tutorial, you’ll use Docker and GitHub Actions to build a robust continuous integration pipeline for a multi-container web application consisting of Flask and Redis. Along the way, you’ll learn how to dockerize a Python web application.
Metaclasses in Python - Real Python Video Course – Metaclasses are an important but mysterious behind-the-scenes mechanism for instantiating classes in Python. In this video course, you’ll learn how Python’s metaclasses work in object-oriented programming.
Is there life after SQL? - YouTube

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Discussing Mojo & Improving Python Object-Oriented Programming

Fri, 26 May 2023 12:00:00 +0000

Would you like to speed up your Python machine-learning code dramatically? What if you only had to change a few keywords and add a couple of type hints on portions of your code? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a new programming language named Mojo, which is a superset of Python. It aims to fix Python’s performance and deployment problems. The project has many interesting ideas and a leader who has helped to shape modern compiler technology.

We also share a pair of Real Python tutorials from Leodanis Pozo Ramos about object-oriented programming in Python. The first one is a deep dive into the creation of classes. It’s an excellent refresher for anyone looking to hone their OOP skills in Python.

The second tutorial covers the SOLID principles, which are five well-established standards for improving your object-oriented design. These principles guide you to create object-oriented code that is more maintainable, extensible, scalable, and testable.

We cover several other articles and projects from the Python community, including a news update, showing warnings when running Django, tracking the progress of your Python program, and a Markdown browser for your terminal.

This week’s episode is brought to you by Koyeb.

Course Spotlight: Using k-Nearest Neighbors (kNN) in Python

Topics:

00:00:00 – Introduction
00:02:39 – PEP 713: Callable Modules
00:04:22 – PEP 712: “Converter” Parameter for dataclasses.field
00:06:49 – Python Classes: The Power of Object-Oriented Programming
00:16:44 – Sponsor: Koyeb
00:17:33 – SOLID Principles: Improve Object-Oriented Design in Python
00:22:00 – Mojo, a Superset of Python
00:34:31 – Mojo might be the biggest thing to happen in programming for decades
00:41:48 – Mojo – a new programming language for AI developers
00:44:03 – Video Course Spotlight
00:45:23 – Have Python Show Warnings When Running Django
00:47:52 – TQDM: Tracking the Progress of Your Python Program
00:50:34 – frogmouth: A Markdown Browser for Your Terminal
00:53:59 – Thanks and goodbye

News:

Show Links:

Python Classes: The Power of Object-Oriented Programming – In this tutorial, you’ll learn how to create and use full-featured classes in your Python code. Classes provide a great way to solve complex programming problems by approaching them through models that represent real-world objects.
SOLID Principles: Improve Object-Oriented Design in Python – In this tutorial, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that is more maintainable, extensible, scalable, and testable.
Mojo, a Superset of Python – Mojo is a new programming language, which is a superset of Python. It aims to fix Python’s performance and deployment problems.
Have Python Show Warnings When Running Django – How to show warnings when running Python, and Django, during local development. The easy, yet not well-known, way.

Discussion:

Projects:

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Virtual Environment Structure & Surveying the Packaging Ecosystem

Fri, 12 May 2023 12:00:00 +0000

How do Python virtual environments work under the hood? How does understanding these concepts help you with managing them for your projects? This week on the show, CPython core developer Brett Cannon returns to discuss his recent articles about virtual environments and the Python packaging landscape.

Brett talks about his recent article “How Virtual Environments Work.” He was researching the topic to solve an issue with a Linux Python distribution that doesn’t provide the tools to create virtual environments. We talk about how he solved the problem by creating a tiny library named microvenv.

We also take a look at the Python packaging ecosystem. Brett talks about the early days of Python, when these tools didn’t exist. He contrasts that with the current packaging solution explosion and how each one attempts to solve unique problems. We also discuss the Python Packaging User Survey and the plans for packaging summits at PyCon US. Note that we recorded this episode two weeks before PyCon US 2023.

Course Spotlight: Getting the Most Out of the Python Standard REPL

In this video course, you’ll learn how to use the Python standard REPL (Read-Eval-Print Loop) to run your code interactively. This tool will allow you to test new ideas, explore and experiment with new tools and libraries, refactor and debug your code, try out examples, and more.

Topics:

00:00:00 – Introduction
00:01:58 – Virtual environments
00:07:10 – PEP 704 & virtual environment workflows
00:26:57 – Experimenting with not including pip
00:34:44 – Video Course Spotlight
00:36:35 – What are you using for packaging?
00:43:32 – Python Packaging User Survey & the history of packaging
00:48:40 – Packing only gets complex when it’s not pure Python code
01:01:22 – Outcomes after the survey and the packaging summit
01:03:56 – What are you excited about in the world of Python?
01:05:28 – What’s something you want to learn next?
01:07:12 – How can people follow the work that you do?
01:08:14 – Thanks and goodbye

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Checking Project Dependencies & Python Dev Resource Collections

Fri, 05 May 2023 12:00:00 +0000

How can you ensure that you’ve appropriately declared your project’s required dependencies? How do you determine what dependencies are missing from a third-party project that you can’t run? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a new Python dependency checker called FawltyDeps. The tool helps you determine if you’ve declared too few or too many packages for your project.

Christopher has brought several developer resource collections: a list of assured open-source Python packages from Google, test databases with interesting datasets, and multiple Django third-party packages.

We cover several other articles and projects from the Python community, including a news update, advice on how to pitch yourself as a guest to a podcast, how to submit articles and projects to PyCoders Weekly, PyPI’s introduction of trusted publishers and organizations, a tool for tracking package history, a pixel art paint program written in Python, and a project for efficient string matching with regular expressions.

Course Spotlight: Publishing Python Packages to PyPI

Topics:

00:00:00 – Introduction
00:02:19 – PEP 684 – A Per-Interpreter GIL Accepted
00:03:59 – Python 3.10 Runtime Now Available in AWS Lambda
00:04:19 – PyCon US 2023 Follow Up
00:08:05 – Podcast Topic & Guest Submissions
00:13:45 – Submissions to PyCoders Weekly
00:16:49 – Google’s Assured OSS Python Packages
00:18:27 – Groovy Datasets for Test Databases
00:19:10 – Top 10 Django Third-Party Packages
00:20:27 – Video Course Spotlight
00:21:46 – FawltyDeps - A Dependency Checker for Your Python
00:26:58 – PyPI Introduces “Trusted Publishers”
00:28:27 – Introducing PyPI Organizations
00:29:02 – pypi-diff: PyPI Package History Tracking
00:30:29 – PyDPainter: A usable pixel art paint program written in Python
00:35:19 – trrex: Efficient String Matching With Regular Expressions
00:37:16 – Thanks and Goodbye

News:

Show Links:

PyCon US 2023 - Follow Up
The Real Python Podcast (podcast@realpython.com)
PyCoder’s Weekly - Submit a Link
Google’s Assured OSS Python Packages – Google publishes a list of the open-source packages that it uses and secures within its own software supply chain. The list is public, so you can take advantage of their assessment.
Groovy Datasets for Test Databases – When you experiment with a new-to-you data science skill, you need some sort of data to work with. Why be boring? This article talks about several available datasets that you can use when you practice your coding skills.
Top 10 Django Third-Party Packages – Will covers a list of his favorite third-party packages for Django. This includes old favorites, like Django REST framework, and lesser-known packages, like django-filter and django-environ.
FawltyDeps: A Dependency Checker for Your Python – FawltyDeps is a new tool to help you identify undeclared and unused dependencies in your Python code, making your projects leaner and more reproducible.
PyPI Introduces “Trusted Publishers” – PyPI package maintainers can adopt a new, more secure, OIDC-authenticated publishing method that doesn’t require sharing long-lived passwords or API tokens with external systems.
Introducing PyPI Organizations - The Python Package Index
pypi-diff: PyPI Package History Tracking

Projects

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Targeting WebAssembly Platforms & Distilling a Minimum Viable Python

Fri, 28 Apr 2023 12:00:00 +0000

Are you familiar with the different versions of WebAssembly? Could WASM be the “write once, run everywhere” solution that developers have searched for? Where does distributing Python applications fit in the narrative? This week on the show, we have CPython core developer Brett Cannon to discuss his recent articles about WebAssembly and MVPy.

Brett has completed his syntactic sugar series, which we discussed in a previous episode. He details the origin of the series and his process of unearthing a minimum viable version of Python. Brett shares how he updated his PyCon US talk on the subject after feedback from presenting it at PyCascades.

We also dig deep into WebAssembly, specifically WebAssembly System Interface (WASI). Brett explains the concept of a “platform target triple” and the importance of defining which system CPython is compiled for. We also discuss WebAssembly becoming a ubiquitous distribution system.

Course Spotlight: Python Basics: Building Systems With Classes

Topics:

00:00:00 – Introduction
00:02:05 – PyCascades 2023
00:02:56 – Using social media for polls and checking interest
00:06:02 – Completing the syntactic sugar blog series
00:15:29 – Minimum Viable Python (MVPy) and WebAssembly
00:19:29 – Other teams focusing on WebAssembly
00:21:31 – Sponsor: Courier
00:22:13 – Stack of technology
00:26:50 – WebAssembly and its platform targets
00:32:35 – WASI and connecting to a runtime
00:38:33 – Extension modules and dynamic libraries
00:47:29 – Overcoming road blocks and envisioning a new WASI assignment
00:51:51 – Video Course Spotlight
00:53:26 – PEP 11 & CPython platform support for WASI
01:03:11 – Machine-specific runtime
01:04:57 – Write once, run everywhere
01:13:14 – Talks and summits planned for PyCon 2023
01:18:00 – Thanks and goodbye

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Seeking Faster Text Processing & Python's .repr() vs .str()

Fri, 14 Apr 2023 12:00:00 +0000

What can you do if your text manipulation in Python is slowing you down? Are there faster alternatives using a compiled extension? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent article by Itamar Turner-Trauring called “Speeding Up Text Processing in Python (Is Hard).” The piece compares the performance of string-matching scenarios using several alternatives to pure Python that rely on compiled extensions.

We also discuss a recent Real Python tutorial by Stephen Gruppetta on when to use .__repr__() vs .__str__() in Python. We cover the use cases for these special methods and the intended audiences for the strings they produce.

We share several other articles and projects from the Python community, including a news update, an article on the functional power of Python’s reduce(), a call to ban 1+N in Django, a friendly project to fetch your data files, and a tool for tracking your work from the shell.

Course Spotlight: Unicode in Python: Working With Character Encodings

In this course, you’ll get a Python-centric introduction to character encodings and Unicode. Handling character encodings and numbering systems can at times seem painful and complicated, but this guide is here to help with easy-to-follow Python examples.

Topics:

00:00:00 – Introduction
00:02:11 – The Python Package Index Launches a Blog
00:03:11 – PEP 582 (Python Local Packages Directory) Rejected
00:05:00 – Django 4.2 Release Candidate 1 Released
00:05:34 – Want to Host DjangoCon Europe 2024?
00:06:23 – When Should You Use .__repr__() vs .__str__() in Python?
00:14:16 – Sponsor: Snyk
00:15:06 – Speeding Up Text Processing in Python (Is Hard)
00:22:21 – reduce() - The Power of a Single Python Function
00:30:27 – Video Course Spotlight
00:32:04 – Ban 1+N in Django
00:35:26 – Pooch - A Friend to Fetch Your Data Files
00:39:11 – workedon - Track Your Work From the Shell
00:41:53 – Thanks and Goodbye

News:

Show Links:

When Should You Use .__repr__() vs .__str__() in Python? – In this tutorial, you’ll learn the difference between the string representations returned by .__repr__() vs .__str__() and understand how to use them effectively in classes that you define.
Speeding Up Text Processing in Python (Is Hard) – If you need to speed up string parsing and formatting in Python, you have many choices. This article covers the uses of Cython, mypyc, Rust, and PyPy and considers how to choose between them.
reduce() - The Power of a Single Python Function – “While Python is not a pure functional programming language, you still can do a lot of functional programming in it. In fact, just one function - reduce() - can do most of it.” This article introduces you to reduce().
Ban 1+N in Django – The 1+N database anti-pattern is common: fetch some rows from the database then re-fetch specific rows to get all the items. An ORM can hide this away and make you fail to realize that it’s happening. This article discusses how to avoid this anti-pattern in Django. It also has an added meta-bonus: a link to the attempt to write the article with ChatGPT.

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Automate Processes and Distribute Python Tools With RPA and RCC

Fri, 07 Apr 2023 12:00:00 +0000

Are you exploring automation of your repetitive business tasks with Python? How are you going to share your helpful tools with co-workers? This week on the show, Sampo Ahokas from Robocorp is here to discuss robotic process automation (RPA) and distribution of these robots.

Sampo is a co-founder and VP of engineering at Robocorp. We talk about using Robot Framework, an open-source RPA tool, to develop bots that implement your existing Python skills. Sampo shares example projects and additional resources for new users.

We discuss the typical difficulties of sharing automation tools with a team and trying to avoid the dreaded “works on my machine” problem. Sampo describes how their group worked to develop a Conda-based tool for creating shareable packages and environments.

Course Spotlight: Manipulating ZIP Files With Python

In this video course, you’ll learn how to manipulate ZIP files using Python’s zipfile module from the standard library. Through hands-on examples, you’ll learn how to read, write, compress, and extract files from your ZIP files quickly.

Topics:

00:00:00 – Introduction
00:02:25 – What is robotic process automation (RPA)?
00:03:55 – What do you mean by automation?
00:05:56 – Additional examples of RPA
00:07:41 – What is the RPA platform?
00:10:06 – What is the Robot Framework?
00:12:42 – Robocorp portal
00:14:09 – Python integration
00:17:06 – Sponsor: REVSYS
00:17:56 – Distribution with RCC
00:20:24 – Why does the system use conda under the hood?
00:24:12 – What hurdles did you face creating RCC?
00:27:51 – Steps for the end user
00:30:52 – Making the project open source
00:35:20 – Video Course Spotlight
00:36:42 – Tips for someone starting with automation
00:42:17 – Integration with VSCode
00:44:18 – Intelligent document processing (IDP)
00:45:36 – What are you excited about in the world of Python?
00:47:46 – What do you want to learn next?
00:48:13 – How can people follow the project online?
00:48:46 – Thanks and goodbye

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Evaluating Python Packages & Celebrating 20 Years of PyCon US

Fri, 31 Mar 2023 12:00:00 +0000

Have you ever installed a Python package without knowing anything about it? What best practices should you employ to ensure the quality of your next package installation? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have Python Software Foundation executive director, Deb Nicholson, to share details about PyCon US 2023.

We cover a recent Real Python tutorial by Philipp Acsany on evaluating the quality of Python packages. The piece provides a tool kit for researching the traits, history, software license, and current condition of external Python packages. We also discuss the techniques that we personally use before selecting a package for our Python projects.

We share several other articles and projects from the Python community, with topics such as the underlying structure of virtual environments, the overhead of Python asyncio tasks, documentation for Python projects with Sphinx and Read the Docs, a project for creating argparse boilerplate, and a way to generate seemingly realistic fake numbers using Benford’s law.

Deb Nicholson is also here to talk about the 20th anniversary of PyCon US, hosted in Salt Lake City. We dig into the details of the upcoming conference, including keynote speakers, tutorials, scheduled talks, and improvements to the hybrid online experience.

Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs

Topics:

00:00:00 – Introduction
00:02:46 – How to Evaluate the Quality of Python Packages
00:11:35 – Overhead of Python asyncio Tasks
00:14:53 – Sponsor: Courier
00:15:37 – How Virtual Environments Work
00:23:48 – Documenting Python Projects With Sphinx and Read the Docs
00:28:29 – duckargs: Code Generator for argparse Boilerplate
00:30:46 – Video Course Spotlight
00:32:04 – Are Those Numbers Realistic or Fake? Try Using Benford’s Law
00:34:37 – Introduction for Deb Nicholson
00:36:33 – What is your role with PyCon US?
00:37:28 – Hybrid conference and dates
00:39:07 – Tutorials
00:40:30 – Education Summit and Typing Summit
00:42:06 – Keynote speakers
00:42:57 – Lightning talks, posters, and job fair
00:45:04 – 20th anniversary of PyCon US
00:46:56 – Resources for proposals and talks
00:49:22 – Previous podcast guests and talks
00:51:26 – Mentored sprints for diverse beginners
00:53:12 – PyLadies auction
00:54:29 – COVID policy
00:56:50 – What are you excited about in the world of Python?
00:58:07 – What do you want to learn next?
00:59:24 – How to follow the PSF and PyCon US?
00:59:55 – Thanks and goodbye

Show Links:

How to Evaluate the Quality of Python Packages – Just like you shouldn’t download any file from the Internet, you shouldn’t install third-party Python packages without evaluating them first. This tutorial will give you the tool set to evaluate the quality of external Python packages before you incorporate them into your Python projects.
Overhead of Python Asyncio Tasks – The Textual library uses a lot of asyncio tasks. In order to determine whether to spend time optimizing them, Will measured the cost of creating asyncio tasks. TLDR; optimize something else. This article also spawned a conversation on Hacker News.
How Virtual Environments Work – This article attempts to demystify virtual environments, specifically why they exist and how they work. It even delves into why Brett is heading down this alley and how running into challenges with cross-platform tools has prompted the creation of microvenv.
Documenting Python Projects With Sphinx and Read the Docs – In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read The Docs to automatically build and publish your code documentation.

Projects:

duckargs: Code Generator for argparse Boilerplate
Are Those Numbers Realistic or Fake? Try Using Benford’s Law – How can you tell whether a set of figures is trustworthy? It’s not always simple, but Benford’s Law gives you one way to find out. There’s even a Python Package to help you check: randalyze.

PyCon US 2023 Links:

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Lessons Learned From Four Years Programming With Python

Fri, 24 Mar 2023 12:00:00 +0000

What are the core lessons you’ve learned along your Python development journey? What are key takeaways you would share with new users of the language? This week on the show, Duarte Oliveira e Carmo is here to discuss his recent talk, “Four Years of Python.”

Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk.

We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year. We also dig into the four major themes of the talk. Along the way, we share a collection of resources to help you continue learning on your Python journey.

Course Spotlight: Building a URL Shortener With FastAPI and Python

Topics:

00:00:00 – Introduction
00:02:38 – Four years of Python
00:04:18 – Why did you create a blog?
00:06:19 – A singular vs wide focus for the blog
00:09:19 – Pitching the talk to conferences
00:13:02 – Resources for preparing your talk
00:16:03 – What was your programming and Python background?
00:19:00 – Sponsor: InfluxData
00:19:47 – Reading is better than Googling
00:26:23 – What are some of your favorite docs?
00:28:48 – Thoughts on GPT and Copilot
00:31:45 – Keep it stupid simple
00:36:07 – What’s extensible code?
00:38:29 – Video Course Spotlight
00:39:54 – Learning testing techniques & testing data science code
00:46:05 – Continuous learning
00:51:46 – What do you use for RSS?
00:53:06 – Resources for machine learning
00:57:20 – What are you excited about in the world of Python?
00:58:57 – What do you want to learn next?
01:00:55 – How can people follow the work you do?
01:01:20 – Thanks and goodbye

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Coding With namedtuple & Python's Dynamic Superpowers

Fri, 17 Mar 2023 12:00:00 +0000

Have you explored Python’s collections module? Within it, you’ll find a powerful factory function called namedtuple(), which provides multiple enhancements over the standard tuple for writing clearer and cleaner code. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his Real Python video course about writing more Pythonic code using namedtuple(). With namedtuple(), you can create immutable sequence types that allow you to access their values using descriptive field names and dot notation instead of clunky integer indices.

We also discuss metaprogramming and the unique advantages of Python’s dynamism. Christopher shares potential paths for this type of coding from web applications, testing, and AST techniques.

We share several other articles and projects from the Python community, including a news update, the Arrow revolution happening in pandas 2.0, a new PEP for inlined comprehensions, tips and techniques for modern Flask apps, a Python helper tool for building and running a REPL with custom commands, and a project to turn a pandas DataFrame into a Tableau-style UI.

Course Spotlight: Writing Clean, Pythonic Code With namedtuple

In this video course, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.

Topics:

00:00:00 – Introduction
00:02:17 – Python 3.12.0 alpha 6 released
00:02:40 – Django Developers Survey 2022 Results
00:03:12 – Writing Clean, Pythonic Code With namedtuple
00:07:40 – pandas 2.0 and the Arrow Revolution (Part I)
00:17:21 – Sponsor: RevSys
00:18:10 – PEP 709: Inlined Comprehensions
00:20:51 – 13 Tips and Techniques for Modern Flask Apps
00:25:54 – Video Course Spotlight
00:27:24 – Discussion: Python’s “Disappointing” Superpowers
00:47:54 – replbuilder: Python helper tool for building and running a REPL with custom commands
00:49:58 – pygwalker: Turn pandas Into a Tableau-Style UI
00:52:15 – Thanks and goodbye

News:

Show Links:

Writing Clean, Pythonic Code With namedtuple – In this video course, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.
pandas 2.0 and the Arrow Revolution (Part I) – This article details the changes in the pandas 2.0 release, with emphasis on the underlying adoption of Apache Arrow.
PEP 709: Inlined Comprehensions – Python Enhancement Proposal 709 covers a change to how comprehensions are handled. Currently, they’re compiled as nested functions. Benchmarking shows that treating list, dict, and set comprehensions as inline code can result in a 2x speedup on the comprehension.
13 Tips and Techniques for Modern Flask Apps – Flask is approaching its 13th birthday, and to celebrate, Phillip has written 13 tips for writing modern Flask apps. It covers dealing with JSON, environment-based configuration, auto-generated docs, and more.

Discussion:

Projects:

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Sharing Your Python App Across Platforms With BeeWare

Fri, 10 Mar 2023 12:00:00 +0000

Are you interested in deploying your Python project everywhere? This week on the show, Russell Keith-Magee, founder and maintainer of the BeeWare project, returns. Russell shares recent updates to Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.

We cover how Anaconda hired him last year to work full-time on the BeeWare project. He shares how this has helped him focus his efforts and move the project forward.

We also discuss his recent talk at DjangoCon US 2022 on how to turn your website into an app (and why maybe you shouldn’t). Russell details the problems of converting from the Web to a mobile platform. We also contrast WebAssembly System Interface (WASI) with the tools that his team works on.

Course Spotlight: Managing Attributes With Python’s property()

In this video course, you’ll learn how to create managed attributes, also known as properties, using Python’s property() in your custom classes.

Topics:

00:00:00 – Introduction
00:02:06 – BeeWare project update and open-source funding
00:06:47 – What are BeeWare and Briefcase?
00:08:19 – Toga GUI and contributions
00:10:47 – Pace of the project now
00:12:47 – PEP 517 and binary packages with C or Rust
00:17:29 – WASM and Briefcase for Web
00:22:22 – Sponsor: InfluxData
00:23:10 – How to turn your Website into an App - Talk
00:28:14 – Bridging libraries that access platform hardware
00:40:56 – Video Course Spotlight
00:42:27 – WASI - WebAssembly System Interface
00:48:18 – Do you need an app or a website?
00:54:23 – Getting started with BeeWare
01:00:06 – What to do first if interested in contributing?
01:02:33 – Channels for the project
01:04:55 – Upcoming conference talks
01:05:26 – What are you excited about in the world of Python?
01:06:42 – What do you want to learn next?
01:09:28 – How can people follow your work online?
01:10:10 – Thanks and goodbye

Show Links:

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Django Deployment Strategies & Preparing for PyCascades 2023

Fri, 03 Mar 2023 12:00:00 +0000

Have you decided how you’re going to deploy your Django project? Should you use a VPS or a PaaS? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have organizers from PyCascades to share details about this year’s hybrid in-person and virtual conference.

Christopher shares an article about selecting an appropriate Django project deployment strategy. The guide compares VPS (virtual private server) and PaaS (platform as a service) systems. He also covers hosting providers for each and highlights potential pitfalls.

We share several other articles and projects from the Python community, including a news update, what’s new in SQLAlchemy 2.0, how to flush the output of the Python print function, the dangers behind image resizing for machine learning, a project that visualizes pathfinding algorithms, and a runtime executor project.

We also have three special guests from PyCascades 2023 to dig into the details of the conference. Conference chair Eliza Sarobhasa is CTO at Women Who Drone and Leadership Fellow (Python Track) & Python Developer Advocate at Women Who Code. Sprints chair Chethana Gopinath is a Software Engineer at realtor.com and a Senior Lead at Women Who Code Python. Speaker Support Chair Jolene Wong is a Senior Software Engineer at Cisco based in Vancouver. We discuss hosting a hybrid conference, participating in open-source sprints, and finding a local Python community.

Course Spotlight: The Python print() Function: Go Beyond the Basics

In this step-by-step course, you’ll learn about the print() function in Python and discover some of its lesser-known features. Avoid common mistakes, take your “hello world” to the next level, and know when to use a better alternative.

Topics:

00:00:00 – Introduction
00:02:47 – Django 4.2 beta 1 released
00:03:04 – What’s New in SQLAlchemy 2.0?
00:06:42 – How to Flush the Output of the Python Print Function
00:14:30 – The Essential Django Deployment Guide
00:21:37 – Sponsor: Snyk
00:22:30 – The Dangers Behind Image Resizing
00:29:40 – Pathfinding-Visualizer: Visualize Pathfinding With Pygame
00:32:40 – rtx: Runtime Executor (asdf Rust Clone)
00:36:19 – Video Course Spotlight
00:37:31 – PyCascades 2023 Details
00:38:53 – Hybrid conference
00:41:11 – How did Chethana get involved?
00:42:30 – Open-source sprints
00:45:19 – How did Jolene get involved?
00:46:31 – How did Eliza get involved?
00:50:21 – Venue details
00:52:32 – Scheduled talks
00:56:13 – Conference sponsors
00:57:48 – Advice for attendees
01:00:46 – Tickets and virtual platform
01:03:01 – What are you excited about in the world of Python?
01:06:13 – Thanks and goodbye

News:

Show Links:

What’s New in SQLAlchemy 2.0? – SQLAlchemy 2.0 was launched in January. This article reviews the latest changes, whether it is worth the upgrade, and how to go about it.
How to Flush the Output of the Python Print Function – In this tutorial, you’ll learn how to flush the output of Python’s print function. You’ll explore output stream buffering in Python using code examples and learn that output streams are block-buffered by default, and that print() with its default arguments executes line-buffered when interactive.
The Essential Django Deployment Guide – Going from “it works on my machine” to deploying to the public can be a daunting task. This guide details the choices between VPS and PaaS systems, how to choose, what the options are, and what you need to know to get your Django code live.
The Dangers Behind Image Resizing – When training an ML model on image data you likely want smaller, consistently sized images. That means image processing in your pipeline, but the expectation that image resizing is the same across libraries can cause unforeseen problems.

Projects:

PyCascades Links:

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Using NumPy and Linear Algebra for Faster Python Code

Fri, 24 Feb 2023 12:00:00 +0000

Are you still using loops and lists to process your data in Python? Have you heard of a Python library with optimized data structures and built-in operations that can speed up your data science code? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to share secrets for harnessing linear algebra and NumPy for your projects.

Jodie details how most people begin their data science journey using loops to iterate over values and apply operations sequentially. We talk about how loops are friendly for beginners, being clear to read and easy to debug, but unfortunately don’t scale well, especially with large amounts of data.

Jodie shares some of the basics of linear algebra and how to organize data into vectors. We talk about how the NumPy library leverages those concepts to improve data processing. We discuss how the library includes operations for vector and matrix addition and subtraction, and why these operations are more efficient than loops. We also cover how NumPy stores arrays in memory and when working with them is faster vs when it’s not.

Course Spotlight: Data Cleaning With pandas and NumPy

Topics:

00:00:00 – Introduction
00:02:35 – Vectorize all the things! - PyCon UK 2022 Talk
00:06:39 – Becoming familiar with linear algebra
00:09:05 – Beginners start with loops
00:11:25 – Starting with basic linear algebra
00:12:25 – The basic unit of a vector
00:18:06 – NumPy representing vectors in Python
00:23:25 – Sponsor: InfluxDB
00:24:13 – Block management
00:25:54 – Replacing a loop with vector-based operations
00:34:06 – NumPy broadcasting
00:38:52 – Approximating nearest neighbors
00:43:49 – Video Course Spotlight
00:45:15 – Solving the problem
00:46:44 – Getting rid of nested loops
00:48:54 – A peek under the hood
00:53:28 – How arrays vs lists are stored in memory
01:00:24 – Considering a GPU
01:03:37 – Real Python resources on the subject
01:04:08 – Upcoming talks and conferences
01:07:31 – Thanks and goodbye

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Creating a Python Wordle Clone & Testing Environments With Nox

Fri, 17 Feb 2023 12:00:00 +0000

Would you like to practice your Python skills while building a challenging word game? Have you been wanting to learn more about creating command-line interfaces and making them colorful and interactive? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent Real Python step-by-step project about creating a clone of Wordle. In the project, you’ll practice building a terminal application, validating user input, and refactoring code into functions.

Christopher shares an article that compares two popular testing tools, Nox and Tox. He discusses how each framework approaches test environment configuration and why the author leans toward using Nox’s Python decorator–based format.

We share several other articles and projects from the Python community, including a news update, a guide to trying out code and ideas quickly with the Python REPL, a PEP about requiring virtual environments by default, a discussion about lessons learned in twenty years as a software engineer, a project for a spreadsheet GUI inside of JupyterLab notebooks, and adding C-style for loops to Python.

Course Spotlight: Getters and Setters in Python

In this video course, you’ll learn what getter and setter methods are, how Python properties are preferred over getters and setters when dealing with attribute access and mutation, and when to use getter and setter methods instead of properties in Python.

Topics:

00:00:00 – Introduction
00:02:21 – Django Security Releases Issued
00:02:43 – PSF Is Hiring a Security Developer-in-Residence
00:03:44 – Python 3.11.2, Python 3.10.10 and 3.12.0 alpha 5 are available
00:04:02 – Build a Wordle Clone With Python and Rich
00:10:05 – Why I Like Nox
00:16:44 – Sponsor: Anaconda Cloud
00:17:26 – PEP 704: Require Virtual Environments by Default
00:27:17 – The Python Standard REPL: Try Out Code and Ideas Quickly
00:33:08 – Video Course Spotlight
00:34:35 – 20 Things I’ve Learned in My 20 Years as a Software Engineer
00:47:50 – Mito: A Spreadsheet Inside Your JupyterLab Notebooks
00:51:37 – How I Added C-Style for-Loops to Python
00:58:08 – Thanks and goodbye

News:

Show Links:

Build a Wordle Clone With Python and Rich – In this step-by-step project, you’ll build your own Wordle clone with Python. Your game will run in the terminal, and you’ll use Rich to ensure your word-guessing app looks good. Learn how to build a command-line application from scratch and then challenge your friends to a wordly competition!
Why I Like Nox – Both Nox and Tox are multi-environment testing tools. This opinion piece by Hynek compares and contrasts them and explains why he is increasingly using Nox.
PEP 704: Require Virtual Environments by Default
The Python Standard REPL: Try Out Code and Ideas Quickly – In this tutorial, you’ll learn how to use the Python standard REPL (Read-Eval-Print Loop) to run your code interactively. This tool will allow you to test new ideas, explore and experiment with new tools and libraries, refactor and debug your code, try out examples, and more.

Discussion

20 Things I’ve Learned in My 20 Years as a Software Engineer – Justin writes a list of things he’s learned over his past twenty years in development. He starts by stating how context is important and that his lessons are from small teams that emphasize productivity and are tool agnostic.
The 10x Programmer Myth - Simple Thread

Projects:

Mito: A Spreadsheet Inside Your JupyterLab Notebooks
How I Added C-Style for-Loops to Python – Ever wanted a C-style for loop in Python? No? Well, you can have one anyway. See how Tushar implemented with for (i := var(0), i < 10, i + 2):

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Wrangling Business Process Models With Python and SpiffWorkflow

Fri, 10 Feb 2023 12:00:00 +0000

Can you describe your business processes with flowcharts? What if you could define the steps in a standard notation and implement the workflows in pure Python? This week on the show, Dan Funk from Sartography is here to discuss SpiffWorkflow.

SpiffWorkflow is a Python tool for translating Business Process Model and Notation (BPMN) diagrams into a workflow engine. You can manipulate this visual chain of events to suit your team’s business requirements. Individual events in the workflow can contain blocks or scripts of Python code to be executed.

We discuss the concept of low-code software tools. Dan also talks about how SpiffWorkflow aims at getting non-developers within an organization involved in development.

Course Spotlight: Cool New Features in Python 3.11 – Real Python

In this video course, you’ll explore what Python 3.11 brings to the table. You’ll learn how Python 3.11 is the fastest and most user-friendly version of CPython yet, and learn about improvements to the typing system and to the asynchronous features of Python.

Topics:

00:00:00 – Introduction
00:02:14 – What is SpiffWorkflow?
00:03:12 – What is BPMN?
00:06:29 – What did you need to add to the project?
00:07:12 – What are the components of a diagram?
00:12:42 – Examples of workflow
00:13:54 – Sponsor: TelemetryHub
00:14:29 – What types of industries use BPMN?
00:18:02 – Decision Model and Notation (DMN)
00:19:34 – What is low-code?
00:27:02 – How could someone get involved?
00:28:02 – How do you host a workflow?
00:29:43 – Video Course Spotlight
00:31:05 – What has the project taught you as a developer?
00:37:29 – Empowering more members of the organization
00:42:07 – Project direction for the next year
00:42:51 – Where to start with SpiffWorkflow?
00:43:15 – What are you excited about in the world of Python?
00:45:59 – What do you want to learn next?
00:51:06 – Thanks and goodbye

Show Links:

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Create Interactive Maps & Geospatial Data Visualizations With Python

Fri, 03 Feb 2023 12:00:00 +0000

Would you like to quickly add data to a map with Python? Have you wanted to create beautiful interactive maps and export them as a stand-alone static web page? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent Real Python tutorial about using Python Folium to create geospatial data visualizations. Folium harnesses the power of the JavaScript library Leaflet. The project shares how to combine this graphical power with Python’s data-wrangling strength.

Christopher shares a recent Python Enhancement Proposal (PEP) about the Global Interpreter Lock (GIL) in CPython. The PEP proposes a change to the build process that implements a flag for optionally building a GIL-less interpreter.

We share several other articles and projects from the Python community, including a news update, a YAML document from hell, a set of logging practices to follow, a discussion about the discourse surrounding the recent Python packaging user survey, a modern Python UI library based on Tkinter, and a lightweight tool kit for bounding boxes.

Course Spotlight: Everyday Project Packaging With pyproject.toml

Topics:

00:00:00 – Introduction
00:02:33 – Bleach 6.0.0 release and deprecation
00:05:03 – Python Folium: Create Web Maps From Your Data
00:10:37 – PEP 703: Making the GIL Optional in CPython
00:15:36 – Sponsor: Influxdata
00:16:24 – The YAML Document From Hell
00:27:53 – Logging Practices I Follow
00:32:08 – How to improve Python packaging
00:36:55 – Video Course Spotlight
00:38:25 – Thoughts on the Python packaging ecosystem
00:58:16 – CustomTkinter: Python UI library Based on Tkinter
01:00:16 – pybboxes: Lightweight Tool Kit for Bounding Boxes
01:01:36 – Thanks and goodbye

News:

Bleach 6.0.0 release and deprecation - Will’s Blog

Show Links:

Python Folium: Create Web Maps From Your Data – You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this tutorial, you’ll create and style a choropleth world map that shows the ecological footprint per country.
PEP 703: Making the GIL Optional in CPython – This PEP proposes changes to the CPython build process that would allow you to build a GIL-less interpreter. This kind of interpreter would not be ABI compatible with the GIL-based one, and the programmer would become responsible for some locking situations in C-extensions. If implemented, this would lead the way to being able to operate without the GIL in cases where backward-compatibility issues aren’t important.
The yaml Document From Hell – As a data format, YAML is extremely complicated and it has many footguns. In this post, Ruud explains some of those pitfalls by means of an example and suggests a few simpler and safer YAML alternatives.
Logging Practices I Follow – “No matter what kind of software you’re developing, you most definitely leverage logging to some extent, probably every single day.” This article outlines good cross-language logging practices, making it easier to find bugs and understand what has happened in your software.

Discussion:

Projects:

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Orchestrating Large and Small Projects With Apache Airflow

Fri, 27 Jan 2023 12:00:00 +0000

Have you worked on a project that needed an orchestration tool? How do you define the workflow of an entire data pipeline or a messaging system with Python? This week on the show, Calvin Hendryx-Parker is back to talk about using Apache Airflow and orchestrating Python projects.

Calvin is the co-founder and CTO of Six Feet Up and a Python Web Conference co-organizer. He’s recently been working on a massive project that requires thousands of jobs involving transferring and transforming data. Through his research into orchestration systems, he found Apache Airflow.

Airflow is an open-source tool to define, schedule, and monitor workflows. The platform is pure Python and integrates with a wide variety of services. We discuss how workflows are defined by creating directed acyclic graphs (DAG).

Calvin talks about how a recent project outgrew the system and how his team built a clever solution using Python. We also discuss the upcoming Python Web Conference and what virtual attendees can expect.

Course Spotlight: Python Basics: Object-Oriented Programming

In this video course, you’ll get to know OOP, or object-oriented programming. You’ll learn how to create a class, use classes to create new objects, and instantiate classes with attributes.

Topics:

00:00:00 – Introduction
00:02:24 – Describing the large data pipeline
00:04:38 – What format was the data in?
00:06:04 – Was the format of the data changed for storage?
00:09:34 – Data engineering and describing sources and targets
00:11:29 – Apache Airflow orchestration and hitting limitations
00:18:12 – Sponsor: CData Software
00:18:54 – DAG: Directed acyclic graphs
00:22:29 – Streaming data and other tool choices
00:25:38 – Overcoming DAG Factory limitations
00:31:49 – Another industry example for Airflow
00:34:24 – Finding solutions as a consultancy
00:35:12 – Is there a minimum-size project for Airflow?
00:37:37 – Django under the hood
00:38:31 – Video Course Spotlight
00:39:58 – The Python Web Conference 2023
00:44:24 – Do you have any upcoming conference talks?
00:45:53 – How can people follow your work online?
00:46:52 – IndyPy talk by Mariatta Wijaya
00:48:01 – What are you excited about in the world of Python?
00:51:45 – What do you want to learn next?
00:53:22 – Thanks and goodbye

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Exploring Python With bpython & Formalizing f-String Grammar

Fri, 20 Jan 2023 12:00:00 +0000

Have you used the Python Read-Eval-Print Loop (REPL) to explore the language and learn about how it operates? Would it help if it provided syntax highlighting, definitions, and code completion and behaved more like an IDE? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the drop-in REPL replacement bpython. bpython enhances the interactivity of a Python REPL session. It’s also a powerful teaching tool for instructors and students to experiment with and explore Python code.

Christopher shares a recent Python Enhancement Proposal (PEP) about formalizing the grammar for f-strings. The PEP describes a reduction in the underlying parser code complexity and provides for future features like comments in multiline f-strings.

We share several other articles and projects from the Python community, including a news roundup, a collection of surveys to classify Python virtual environment workflows, a course about context managers and Python’s with statement, a discussion about microfeatures that we would like to see adopted in Python, a Python terminal music player, and an infinite array powered by AI.

Course Spotlight: Context Managers and Python’s with Statement

In this video course, you’ll learn what the Python with statement is and how to use it with existing context managers. You’ll also learn how to create your own context managers.

Topics:

00:00:00 – Introduction
00:02:21 – Pillow 9.4.0 Released
00:02:47 – Django Bugfix Release: 4.1.5
00:02:56 – Plone 6.0 Released
00:03:16 – PyCon Italia 2023
00:03:54 – Discover bpython: A Python REPL With IDE-Like Features
00:13:55 – PEP 701: Syntactic Formalization of f-Strings
00:17:07 – Sponsor: Influx Data
00:17:57 – Classifying Python Virtual Environment Workflows
00:30:26 – Context Managers and Python’s with Statement
00:36:32 – Video Course Spotlight
00:37:47 – Microfeatures I’d Like to See in More Languages
00:49:34 – Python Terminal Music Player
00:51:19 – Infinite AI Array
00:55:05 – Thanks and goodbye

News:

Show Links:

Discover bpython: A Python REPL With IDE-Like Features – In this tutorial, you’ll learn about bpython, an alternative Python REPL that brings code suggestions and many other IDE-like features to the terminal. Once you discover how much bpython can improve your productivity, you’ll never want to return to using the vanilla Python REPL again.
PEP 701: Syntactic Formalization of f-Strings – This Python Enhancement Proposal describes the formalization of a grammar for f-strings, allowing a reduction in the underlying parser code complexity and providing future features like comments in multiline f-strings.
Classifying Python Virtual Environment Workflows – This article discusses the various ways of creating and managing Python virtual environments, including what kinds of tools you could use. It categorizes the different styles and describes how the choices that you make affect your workflow.
Context Managers and Python’s with Statement – In this video course, you’ll learn what the Python with statement is and how to use it with existing context managers. You’ll also learn how to create your own context managers.
Microfeatures I’d Like to See in More Languages – Some language features are intrinsic to the language. Others are syntactic sugar that other programming languages could easily borrow. This opinion piece from Hillel highlights some features that the mainstream should steal from more obscure languages. Two Python features that he’d like to see in more languages are chained evaluations (2 <= x < 10) and numbers with separators (1000000 == 1_000_000).

Projects:

Python Terminal Music Player
Infinite AI Array – Learn about an insane library containing special lists and dicts so that any missed calls automatically go to GPT3 and add a predictive value in its place.

Additional Links:

Christopher Trudeau’s - Act function for virtual environments

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Speeding Up Your DataFrames With Polars

Fri, 13 Jan 2023 12:00:00 +0000

How can you get more performance from your existing data science infrastructure? What if a DataFrame library could take advantage of your machine’s available cores and provide built-in methods for handling larger-than-RAM datasets? This week on the show, Liam Brannigan is here to discuss Polars.

Liam is an experienced data scientist working in finance, technology, and environmental analysis. He’s recently started contributing to the documentation for Polars and developing a training course for the library.

We talk about the library’s overall speed and lack of additional dependencies. Liam explains the advantages of lazy vs eager mode and which to choose when performing data exploration or attempting to load a dataset larger than your RAM.

We also discuss potential barriers to switching to Polars from a pandas workflow. Across our conversation, we explore several other libraries and technologies, including Apache Arrow, DuckDB, query optimization, and the “rustification” of Python tools.

Course Spotlight: Graph Your Data With Python and ggplot

Show Topics:

00:00:00 – Introduction
00:02:06 – Liam’s background and intro to Polars
00:03:37 – Hurdles to switching to Polars
00:05:23 – Creating training resources
00:08:15 – No index
00:09:46 – Data science 2025 predictions
00:12:02 – Contributions to Polars
00:15:07 – Eager vs lazy mode & query optimization
00:19:25 – Sponsor: Anaconda Nucleus
00:20:00 – Apache Arrow and parquet
00:24:43 – DuckDB and column orientation
00:29:27 – The “rustification” of libraries
00:34:49 – Video Course Spotlight
00:36:16 – GPUs and memory requirements
00:45:49 – No additional library requirements
00:47:37 – Development of the ecosystem
00:51:33 – Chaining operations
00:53:39 – How can people follow your work?
00:54:51 – What are you excited about in the world of Python?
00:56:09 – What do you want to learn next?
00:56:58 – Thanks and goodbye

Show Links:

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Surveying Comprehension Constructs & Python Parallelism Infighting

Fri, 06 Jan 2023 12:00:00 +0000

Have you embraced the use of comprehensions in your Python journey? Are you familiar with all the varieties of comprehension constructs? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article that surveys Python’s comprehensions and generators. This overview includes code snippets and the fundamentals of creating list, set, and dictionary comprehensions. We weigh the advantages of using a comprehension versus the more familiar for loops that they replace.

Christopher shares an article about how there may be infighting between the parallelism in your Python code and the parallelism within the libraries that you’re using. These complex system interactions can cause processing slowdowns and hard-to-trace bottlenecks.

We share several other articles and projects from the Python community, including a news roundup, a Python linter comparison, an overview of multiprocessing race conditions in Python, a discussion covering import statement styles, a project for WASM-powered Jupyter tools running in the browser, and a collection of easter eggs and jokes hidden inside Python itself.

Course Spotlight: Understanding Python List Comprehensions

Python list comprehensions make it easy to create lists while performing sophisticated filtering, mapping, and conditional logic on their members. In this course, you’ll learn when to use list comprehensions in Python and how to create them effectively.

Topics:

00:00:00 – Introduction
00:02:41 – PyPy v7.3.10 Release
00:03:17 – Django Bugfix Release: 4.1.4
00:03:28 – Python 3.11.1, 3.10.9, 3.9.16, 3.8.16, 3.7.16 Released
00:03:56 – Python Linter Comparison 2022
00:11:51 – Who Controls Parallelism? A Disagreement That Leads to Slower Code
00:15:58 – Sponsor: InfluxDB
00:16:47 – A Crash Course in Comprehensions and Generators
00:24:23 – Multiprocessing Race Conditions in Python
00:27:46 – Video Course Spotlight
00:28:55 – What Style of import Statement Do You Use?
00:36:54 – jupyterlite: WASM Powered Jupyter Running in the Browser
00:40:35 – python-easter-eggs: Easter Eggs and Hidden Jokes in Python
00:43:18 – PyCoder’s Weekly: Submit a Link
00:43:46 – Thanks and goodbye

News:

Show Links:

Python Linter Comparison 2022 – There are many linter choices for Python. This article covers a lot of them: Pylint, Pyflakes, Flake8, autopep8, Bandit, Prospector, Pylama, Pyroma, Black, Mypy, Radon, and mccabe.
Who Controls Parallelism? A Disagreement That Leads to Slower Code – In complex systems, there may be a fight between the parallelism in your code vs the parallelism in the libraries that you’re using. This fight can cause things to slow down. This article shows some examples and explores what you can do about the issue.
A Crash Course in Comprehensions and Generators – A great collection of code snippets that showcase the power and flexibility of list comprehensions, generators, and related constructs.
Multiprocessing Race Conditions in Python – A race condition happens when parallel tasks attempt to execute code at the same time and the results are dependent on order of execution. Finding race conditions can be challenging. This article gives some hints as to how to find the different kinds of race conditions when coding with the multiprocessing module.

Discussion:

Projects:

Additional Links:

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2022 Real Python Tutorial & Video Course Wrap Up

Fri, 23 Dec 2022 12:00:00 +0000

It’s been another year of changes at Real Python! The Real Python team has written, edited, curated, illustrated, and produced a mountain of Python material this year. We added some new members to the team, updated the site’s features, and created new styles of tutorials and video courses.

Three members of the Real Python team join us this week, Kate Finegan, Geir Arne Hjelle, and Leodanis Pozo Ramos. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, and video courses that showcase what our team created this year.

Kate and Geir Arne help to shepherd articles through the multi-stage editing process. Along with the rest of the team, they make sure these resources impart crucial Python knowledge and provide a thorough didactic experience. Leodanis’ name has been featured many times on this podcast, and it was great to talk to him about writing tutorials and diving deep into the Pythonic details.

We hope you enjoy this review! Programming note, there won’t be an episode next week, but we’ll be back in January and look forward to bringing you a year full of great guests, articles, and topics.

Course Spotlight: Building Python Project Documentation With MkDocs

Topics:

00:00:00 – Introduction
00:03:02 – Geir Arne and RP content direction
00:04:31 – Kate Finegan and editing tutorials
00:07:35 – Leodanis Pozo Ramos and writing tutorials
00:09:14 – Changes for Real Python in 2022
00:18:56 – Your Python Coding Environment on Windows
00:23:32 – Sponsor: TelemetryHub
00:24:16 – Why Is It Important to Close Files in Python?
00:28:40 – Python and TOML: New Best Friends
00:33:47 – Sneaky REST APIs With Django Ninja
00:36:24 – Manage Your To-Do Lists Using Python and Django
00:39:52 – Python Constants: Improve Your Code’s Maintainability
00:42:59 – Build Your Python Project Documentation With MkDocs
00:49:28 – Building a URL Shortener With FastAPI and Python
00:51:24 – Video Course Spotlight
00:52:40 – Image Processing With the Python Pillow Library
00:57:20 – Draw the Mandelbrot Set in Python
01:05:01 – Using Python’s pip to Manage Your Projects’ Dependencies
01:11:39 – Exploring Scopes and Closures in Python
01:14:49 – Thanks and goodbye

Show Links:

Additional Links:

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Start Using a Build System & Continuous Integration in Python

Fri, 16 Dec 2022 12:00:00 +0000

What advantages can a build system provide for a Python developer? What new skills are required when working with a team of developers? This week on the show, Benjy Weinberger from Toolchain is here to discuss the Pants build system and getting started with continuous integration (CI).

Benjy is one of the core developers of the Pants build system. He talks about the software tools and processes that a build system simplifies. We discuss how an individual developer can take advantage of continuous integration. We also cover some of the expectations when moving into professional software development.

Have you learned about or started to use tools like linters, code formatters, import sorters, type checkers, and packaging systems? A build system is designed to combine all of those tools into a simplified, one-step process to share your best code.

Benjy explains concepts like implementing fine-grained invalidation, moving to a monorepo, and using a build system for data science projects. He also shares his tips for getting started with Pants and finding help within the community.

Course Spotlight: Testing Your Code With pytest

Topics:

00:00:00 – Introduction
00:03:19 – Working on Pants
00:05:24 – Background on Toolchain
00:08:26 – Individual developer using CI
00:11:04 – When did you start using these types of tools?
00:14:30 – Was the organization open to the development of CI tools?
00:15:45 – Having a foundation with Git
00:17:11 – Moving toward workflows
00:23:30 – Sponsor: InfluxDB
00:24:20 – What’s fine-grained invalidation?
00:29:32 – Setting up test coverage
00:33:07 – Moving into packaging and deployment
00:37:22 – Advantages of a monorepo
00:42:10 – Video Course Spotlight
00:43:36 – Reasons for deeper Python integration
00:47:40 – Using the build system with data science projects
00:52:21 – Getting started with Pants
00:55:47 – What are you excited about in the world of Python?
00:57:12 – What do you want to learn next?
00:58:41 – How can people follow your work online?
00:59:27 – Thanks and goodbye

Show Links:

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Package Python Code With pyproject.toml & Listing Files With pathlib

Fri, 09 Dec 2022 12:00:00 +0000

How do you start packaging your code with pyproject.toml? Would you like to join a conversation that gently walks you through setting up your Python projects to share? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent code conversation featuring Real Python team members Ian Currie and Geir Arne Hjelle. The video dives into the officially sanctioned way to configure your project using a pyproject.toml file. We cover how this relatively new approach will help you package your code for use on your system or for sharing with others.

Christopher shares a Real Python tutorial about using pathlib to get a list of all the files within a directory. We’re both fans of pathlib and how it simplifies working with file paths. The tutorial digs into methods to recursively list all directory contents or create a conditional listing.

We share several other articles and projects from the Python community, including an explanation of Python bytecode, an argument for always using [closed, open) intervals, a discussion about building the monolith before microservices, a way to parse natural language time and date expressions, and a project for posting on Mastodon.

Course Spotlight: Using Python’s pathlib Module

In this video course, you’ll learn how to effectively work with file system paths in Python 3 using the pathlib module in the standard library.

Topics:

00:00:00 – Introduction
00:02:30 – Always Use [closed, open) Intervals
00:07:05 – Everyday Project Packaging With pyproject.toml
00:15:38 – Sponsor: InfluxDB
00:16:27 – How to Get a List of All Files in a Directory With Python
00:20:37 – Python Bytecode Explained
00:29:39 – Video Course Spotlight
00:30:48 – Build the Modular Monolith First
00:44:34 – toot - PyPI
00:49:58 – quickadd: Parse Natural Language Time and Date Expressions
00:53:09 – Thanks and goodbye

Show Links:

Always Use [closed, open) Intervals – “Intervals or ranges pop-up everywhere in the programming world. The classic example is picking a start and end date, like you would when booking an AirBnB or a flight. Have you ever wondered why they are always implemented as [closed, open) as opposed to [closed, closed]?”
Everyday Project Packaging With pyproject.toml – In this Code Conversation video course, you’ll learn how to package your everyday projects with pyproject.toml. Playing on the same team as the import system means you can call your project from anywhere, ensure consistent imports, and have one file that’ll work for many build systems.
How to Get a List of All Files in a Directory With Python – In this tutorial, you’ll be examining a couple of methods to get a list of files and folders in a directory with Python. You’ll also use both methods to recursively list directory contents. Finally, you’ll examine a situation that pits one method against the other.
Python Bytecode Explained – When a Python program is run, the interpreter first parses your code and checks for syntax errors, then it translates it into bytecode instructions. This article explains some of the features of Python bytecode.

Discussion:

Build the Modular Monolith First – “Even talking about building a monolith today, is a bit taboo. It is all about microservices at the moment, and has been for a few years. But they aren’t a silver bullet.” Coding samples in the article aren’t Python, but the architectural advice is cross-language.
Microservices and the First Law of Distributed Objects
“I’m convinced that one of the biggest architectural mistakes of the past decade was going full microservice” Jason Warner - Twitter

Projects:

Additional Links:

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Preparing Data to Measure True Machine Learning Model Performance

Fri, 02 Dec 2022 12:00:00 +0000

How do you prepare a dataset for machine learning (ML)? How do you go beyond cleaning the data and move toward measuring how the model performs? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to talk about strategies for better ML model performance.

Jodie starts by defining some terms for the conversation. We talk about targets, features, and supervised learning.

We discuss three common ways that data can alter model performance and which Python tools can help spot and avoid them. Jodie shares personal experiences of working through these pitfalls. We also share a healthy collection of resources to explore and learn more.

Course Spotlight: Combining Data in pandas With concat() and merge()

In this video course, you’ll learn two techniques for combining data in pandas: merge() and concat(). Combining Series and DataFrame objects in pandas is a powerful way to gain new insights into your data.

Topics:

00:00:00 – Introduction
00:01:46 – Recent conference talks
00:03:24 – How to prepare your data for model performance
00:04:24 – Vocabulary: target, features, and supervised learning
00:06:28 – The curse of dimensionality
00:08:57 – Overfitting
00:11:08 – Underfitting
00:12:11 – Splitting the dataset
00:13:39 – K-fold cross validation
00:18:30 – Data leakage
00:21:36 – Checking for duplicates
00:26:23 – Applying transformations only after splitting data
00:31:16 – Imbalanced data
00:36:36 – Using ML to balance data
00:41:05 – Informing your model of the imbalance
00:42:56 – Video Course Spotlight
00:44:20 – Accuracy used as a measure
00:49:05 – Scikit-learn method classification_table
00:50:43 – Jet Brains blog post and conference talk
00:52:18 – How can people follow your work online?
00:54:39 – Upcoming webinars
00:56:20 – Thanks and goodbye

Show Links:

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Building Python REST APIs With Flask & Structuring Pull Requests

Fri, 25 Nov 2022 12:00:00 +0000

How do you build a REST API using the Flask web framework? How can you quickly add endpoints while automatically generating documentation? This week on the show, Real Python author Philipp Acsany is here to discuss his tutorial series “Python REST APIs With Flask, Connexion, and SQLAlchemy.” Christopher Trudeau is also here with another batch of PyCoder’s Weekly articles and projects.

Philipp talks about updating a set of tutorials to use current libraries and best practices. The series takes you through building the base Flask project, defining endpoints, creating documentation, adding a persistent database, and implementing models with SQLAlchemy.

Christopher shares an article about contributing to an existing internal or open-source project by properly preparing pull requests. The article is titled “Ten Tasty Ingredients for a Delicious Pull Request”.

We share several other articles and projects from the Python community, including more suspicious PyPI packages using new tactics, method chaining in pandas, tools to find syntax errors without stopping, a library for searching text in videos using optical character recognition (OCR), a project for visualizing CPython’s specializing adaptive interpreter, and a library for building CLI applications based on type hints.

Course Spotlight: The Pandas DataFrame: Working With Data Efficiently

Topics:

00:00:00 – Introduction
00:03:09 – Philipp’s background
00:05:37 – Python REST APIs With Flask, Connexion, and SQLAlchemy
00:14:35 – Ten Tasty Ingredients for a Delicious Pull Request
00:24:25 – Sponsor: InfluxDB
00:25:13 – Method Chaining in Pandas: Bad Form or a Recipe for Success?
00:31:35 – More Suspicious PyPI Packages
00:35:48 – Video Course Spotlight
00:37:01 – What Tools Find Syntax Errors Without Stopping?
00:47:29 – Perform OCR upon entire videos
00:49:49 – Visualize CPython 3.11’s Specializing, Adaptive Interpreter
00:54:08 – Typer, build great CLIs
00:56:29 – Thanks and goodbye

Show Links:

About Philipp Acsany – Real Python
Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1
Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 2
Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 3
Ten Tasty Ingredients for a Delicious Pull Request – LB is a core team member of the open-source project Wagtail and, as such, has a lot of experience dealing with community contributions. This article talks about how to be a good contributor, whether for your next open-source software (OSS) PR or within your own organization.
Method Chaining in Pandas: Bad Form or a Recipe for Success? – Python trainer Matt Harrison has been creating a bit of a stir. Some of his pandas examples have elicited criticism from different folks in the Twitterverse. Dave Amos interviews Matt to discuss the pros and cons of his approach.
More Suspicious PyPI Packages – Researchers at Phylum have come across over a dozen new malicious uploads to PyPI. Many of them are copied and pasted versions of legitimate packages that have been renamed and had malicious code inserted. This detailed article shows some of the tactics used by the bad actors.

Discussion:

What Tools Find Syntax Errors Without Stopping?

Projects:

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Moving Projects Away From Passwords With WebAuthn and Python

Fri, 18 Nov 2022 12:00:00 +0000

What if you didn’t have to worry about managing user passwords as a Python developer? That’s where the WebAuthn protocol and new hardware standards are heading. This week on the show, Dan Moore from FusionAuth returns to discuss a password-less future.

WebAuthn is a way to authenticate users using biometric, secure authentication methods. Dan dives into passkeys, ceremonies, authenticators, and hardware standards. We also cover several projects and libraries that can help you get started with WebAuthn in Python.

Course Spotlight: Refactoring: Prepare Your Code to Get Help

In this Code Conversation video course, you’ll explore the steps you can take to get help when you’re stuck while coding. You’ll investigate how to clean up your code to focus on the question you have. Along the way, you’ll learn how to handle errors and use custom exceptions.

Topics:

00:00:00 – Introduction
00:01:36 – Dan’s WebAuthn article
00:03:26 – FIDO and WebAuthn
00:05:53 – What’s a YubiKey?
00:07:57 – Phones with biometric systems
00:12:03 – Sponsor: CData Software
00:12:45 – Similarities to HTTPS
00:16:13 – A password-less future
00:24:31 – Where’s it being used?
00:30:53 – Video Course Spotlight
00:32:26 – Python WebAuthn projects and packages
00:34:52 – Does a developer need to set up additional auth methods?
00:37:31 – How are the third-party auth services implementing this?
00:39:50 – What are you excited about in the world of Python?
00:41:24 – What do you want to learn next?
00:43:20 – Thanks and goodbye

Show Links:

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Creating Tic-Tac-Toe With an AI Player & Shortcuts for Python Decorators

Fri, 11 Nov 2022 12:00:00 +0000

How do you create a computer opponent for a simple game within Python? Would you also like to learn how to adapt the game to run in a web browser or graphical user interface (GUI)? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent Real Python step-by-step project for creating a tic-tac-toe game engine. He talks about how to build the game engine and adapt it for different front ends. The tutorial also shows how to implement an unbeatable computer player using the minimax algorithm.

We discuss an article about how to avoid repeating yourself when creating decorators with multiple parameters. We talk about how you can stop copying and pasting code several times by assigning the decorator to a new variable.

We share several other articles and projects from the Python community, including a news roundup, a deep dive into Python’s doctest, several Python command line tricks, type annotations via automated refactoring, a new way to draw boxes in the terminal, a collection of projects for beginners with source code, a minimalist PDF creation library, and a tool for sensible logging in Python.

Course Spotlight: Python Decorators 101

Topics:

00:00:00 – Introduction
00:02:25 – Python 3.12.0 Alpha 1 Released
00:02:45 – PyCon US 2023 Call for Proposals
00:03:27 – Python’s doctest: Document and Test Your Code at Once
00:13:59 – Build a Tic-Tac-Toe Game Engine With an AI Player in Python
00:22:12 – Sponsor: InfluxDB
00:22:59 – Python Command Line Tricks
00:30:50 – Type Annotation via Automated Refactoring
00:34:29 – A New (?) Way of Drawing Boxes in the Terminal
00:37:10 – Decorator Shortcuts
00:39:36 – Video Course Spotlight
00:41:02 – 190 Python Projects With Source Code
00:47:58 – fpdf2: Minimalist PDF Creation Library
00:50:04 – Simple, Sane, and Sensible Logging in Python
00:53:05 – Thanks and goodbye

News:

Topics:

Python’s doctest: Document and Test Your Code at Once – In this tutorial, you’ll learn how to add usage examples to your code’s documentation and docstrings and how to use these examples to test your code. To run your usage examples as automated tests, you’ll use Python’s doctest module from the standard library.
Build a Tic-Tac-Toe Game Engine With an AI Player in Python – In this step-by-step tutorial, you’ll build a universal game engine in Python with tic-tac-toe rules and two computer players, including an unbeatable AI player using the minimax algorithm. You’ll also create a text-based graphical front end for your library and explore two alternative front ends.
Python Command Line Tricks – Using python -m you can do all sorts of things from the command line, including starting a webserver, opening a browser, parsing JSON, compressing files, and much more.
Type Annotation via Automated Refactoring – Jimmy’s team at Carta decided they wanted to add type annotations to their large codebase, but doing so manually would’ve taken a very long time. This post shows you how they built automated refactoring tools to add type annotations to their code.
A New (?) Way of Drawing Boxes in the Terminal – With clever use of some of the Unicode border characters, you can build a better box around your text, without any color bleeding.
Decorator Shortcuts – “When using many decorators in code, there’s a shortcut you can use if you find yourself repeating them. They can be assigned to a variable just like any other Python expression.”

Discussion:

190 Python Projects With Source Code

Projects:

fpdf2: Minimalist PDF Creation Library
Simple, Sane, and Sensible Logging in Python – Get started with logging in Python or deploy advanced, flexible loggers without the boilerplate code. Learn all about log2d, a third-party wrapper for the Python logging library.

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Exploring the New Features of Python 3.11

Fri, 04 Nov 2022 12:00:00 +0000

Python 3.11 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to talk about the new version. Geir Arne wrote a series of preview tutorials earlier this year, and his annual piece, titled “Python 3.11: Cool New Features for You to Try,” was published on October 24. Christopher’s video course came out the next day, covering the topics from the tutorial with visual examples of Python 3.11 in action.

Geir Arne and Christopher collaborated to create code examples for the new features. We discuss better error messages, faster code execution, task and exception groups, typing features, and native TOML support.

We dive into the updates and offer advice about ways to incorporate them into your projects. We also consider when you should start running Python 3.11.

Course Spotlight: Cool New Features in Python 3.11 – Real Python

Topics:

00:00:00 – Introduction
00:02:19 – Preview series
00:03:50 – Faster CPython project
00:07:10 – Specializing adaptive interpreter
00:11:24 – Other performance stuff
00:16:07 – Sponsor: Deepgram
00:16:51 – Improved tracebacks
00:21:49 – Exception groups and notes
00:27:22 – Self type and additional type hints
00:36:14 – Video Course Spotlight
00:37:27 – asyncio and task groups
00:41:25 – TOML and tomllib
00:46:21 – ISO date parsing
00:50:09 – Negative zeros
00:53:38 – Dead battery deprecations
00:56:04 – Advice on upgrading
01:01:01 – Thanks and goodbye

Show Links:

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Fostering an Internal Python Community & Managing the 3.11 Release

Fri, 21 Oct 2022 12:00:00 +0000

Does your company have a plan for growing an internal Python community? What are the attributes to look for when bringing someone into your department? This week on the show, Pablo Galindo Salgado returns to talk about building community through the Python Guild at Bloomberg and managing the release of Python 3.11.

Pablo describes how the Python Guild started and currently operates inside Bloomberg. We talk about how it fosters community and acts as a way to promote internally developed tools across disparate teams. We also discuss how work groups use it to find new internal candidates for their teams.

Pablo talks about his role as release manager for Python 3.10 and 3.11. He shares the intense journey the team has had this year in preparing for the release of 3.11. He details updating testing strategies to work with the new specializing adaptive interpreter.

Course Spotlight: Python Basics: Strings and String Methods

In Python, collections of text are called strings. In this course, you’ll learn about this fundamental data type and the string methods that you can use to manipulate strings. Along the way, you’ll learn ways to work with strings of numbers, and how to format strings for printing.

Topics:

00:00:00 – Introduction
00:02:13 – Python Guild inside of Bloomberg
00:13:31 – Finding candidates for the guild from other areas
00:19:11 – Sponsor: Platform.sh
00:19:47 – Considering eagerness to learn and excitement
00:29:44 – Structuring the guild into work groups
00:33:43 – How are things going as release manager?
00:38:25 – Testing for adaptive interpreters
00:44:02 – Working toward the feature freeze
00:50:39 – Changing the parser went smoothly
00:54:34 – Video Course Spotlight
00:55:55 – Where do you find the time?
00:59:51 – How’s the sweep picking coming along?
01:00:33 – What are you excited about in the world of Python?
01:01:29 – What do you want to learn next?
01:07:18 – How can people follow the work you do?
01:08:20 – Thanks and goodbye

Show Links:

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Using an Ellipsis in Python & Goals for CPython 3.12

Fri, 14 Oct 2022 12:00:00 +0000

Where should you use an ellipsis in Python? How does it behave as a placeholder in a script, project, or stub file? What are the next goals for the Faster CPython project? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python article that covers when you should use an ellipsis in Python. We discuss the similarities with the pass keyword and how it’s used for type hints within stub files.

Christopher shares resources covering the goals of the Faster CPython project. We’re on the cusp of the release of Python 3.11, but the project keeps moving forward as they look at ways to continue speeding up Python.

We share several other articles and projects from the Python community, including a news roundup, alternatives for hosting Python-based applications, ways to create custom Python strings, a discussion about aging programmers, a structural diff that understands syntax, and a project for refurbishing and modernizing Python codebases.

Course Spotlight: Providing Multiple Constructors in Your Python Classes

In this video course, you’ll learn how to provide multiple constructors in your Python classes. To this end, you’ll learn different techniques, such as checking argument types, using default argument values, writing class methods, and implementing single-dispatch methods.

Topics:

00:00:00 – Introduction
00:02:26 – Django security releases issued
00:02:44 – PEP 698: Override Decorator for Static Typing
00:03:37 – Heroku Alternatives for Python-Based Applications
00:14:34 – Python 3.12 Goals: Faster-CPython Ideas Wiki
00:20:29 – Sponsor: InfluxDB
00:21:19 – When Do You Use an Ellipsis in Python?
00:28:18 – Custom Python Strings: Inheriting From str vs UserString
00:32:52 – Aging Programmer
00:46:32 – Video Course Spotlight
00:47:48 – difftastic: A Structural Diff That Understands Syntax
00:50:44 – refurb: Refurbish and Modernize Python Codebases
00:55:44 – Thanks and goodbye

News:

Django security releases issued: 4.1.2, 4.0.8, and 3.2.16 | Weblog | Django
PEP 698: Override Decorator for Static Typing – This Python Enhancement Proposal describes the use of a new decorator, @override, which would be used as a type hint for methods in a subclass that override a parent’s method. This type hint would introduce a level of safety if the parent method is refactored without corresponding changes to the child method.

Show Links:

Heroku Alternatives for Python-Based Applications – Learn about alternatives to Heroku and their pros and cons. Platforms discussed include Digital Ocean, Google App Engine, AWS, Azure, PythonAnywhere, and half a dozen more.
Python 3.12 Goals: Faster-CPython Ideas Wiki – A summary of the goals for the Faster CPython initiative within the Python 3.12 release. Includes trace optimizations, shrinking object sizes, improving memory management overhead, and more. See also the associated Workflow for 3.12 cycle checklist.
When Do You Use an Ellipsis in Python? – You may have seen three dots in Python scripts. Although this syntax may look odd, using an ellipsis is valid Python code. In this tutorial, you’ll learn when Python’s Ellipsis constant can come in handy for you.
Custom Python Strings: Inheriting From str vs UserString – In this tutorial, you’ll learn how to create custom string-like classes in Python by inheriting from the built-in str class or by subclassing UserString from the collections module.

Discussion:

Aging Programmer

Projects:

Additional Links:

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Using a Memory Profiler in Python & What It Can Teach You

Fri, 07 Oct 2022 12:00:00 +0000

Have you used a memory profiler to gauge the performance of your Python application? Maybe you’re using it to troubleshoot memory issues when loading a large data science project. What could running a profiler show you about a codebase you’re learning? This week on the show, Pablo Galindo Salgado returns to talk about Memray, a powerful tracing memory profiler.

Pablo developed Memray while working at Bloomberg to track memory allocations beyond Python code into native extensions and the interpreter itself. It’s a compelling tool that provides fine-grain reports to help you understand where memory is used.

Pablo shares the reporting that Memray provides, including live mode, flame graphs, and a pytest plug-in. We also discuss how a tracing memory profiler can help you understand a new codebase.

He walks through how he developed the first prototype internally and eventually moved the project into open source. This is the first part of my conversation with Pablo. In a couple of weeks, you’ll get the second part, where we talk about Python guilds inside large companies and his work as the release manager for Python 3.10 and 3.11.

Course Spotlight: SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files

Topics:

00:00:00 – Introduction
00:02:48 – When should you use a memory profiler?
00:05:13 – Fine-grain reporting
00:13:17 – Sampling profiler vs tracing profiler
00:19:46 – Sponsor: Deepgram
00:20:31 – What is a flame graph?
00:30:36 – Using Rich for terminal reporters
00:40:08 – Currently only Linux and macOS
00:41:13 – pytest plug-in
00:42:03 – Showing native allocation details
00:44:20 – Video Course Spotlight
00:45:52 – Using a profiler to learn a codebase
00:54:39 – Moving from internal project to open source
01:02:17 – Thanks and goodbye

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Explaining Access Control Using Python & Cautiously Handling Pickles

Fri, 30 Sep 2022 12:00:00 +0000

Have you ever used code to help explain a topic? How can Python scripts be used to understand the intricacies of access control? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher talks about an article that explores the evolution of access control by reimplementing the concepts with Python scripts. The experiment moves across the various access forms, starting with control lists, roles, and attributes, then ending with purpose-based access control (PBAC).

We also cover a post about how to create dangerous pickles. We discuss where malicious code can hide within the serialization process and how decompiling code can be an education tool.

We share several other articles and projects from the Python community, including command line interface (CLI) creation with argparse, HTML and CSS for Python developers, a Python packaging user survey, a visual Python Tkinter GUI creator, a PyScript-based data visualization cookbook, and a project for writing functional test helpers in Django.

Course Spotlight: Serializing Objects With the Python pickle Module

In this course, you’ll learn how you can use the Python pickle module to convert your objects into a stream of bytes that can be saved to a disk or sent over a network. You’ll also learn the security implications of using this process on objects from an untrusted source.

Topics:

00:00:00 – Introduction
00:02:19 – Python 3.11.0rc2 is now available
00:03:45 – HTML and CSS for Python Developers
00:08:34 – Evolution of Access Control Explained Through Python
00:17:14 – Sponsor: InfluxDB
00:18:03 – Dangerous Pickles
00:28:08 – Building Command Line Interfaces With argparse
00:34:27 – Video Course Spotlight
00:35:45 – PyPI.org is running a survey
00:49:01 – Visual Python Tkinter GUI Creator
00:50:33 – Python Data Visualization Cookbook
00:52:06 – django-functest: Helpers for Functional Tests in Django
00:57:55 – Thanks and goodbye

Show Links:

Python Insider: Python 3.11.0rc2 is now available
HTML and CSS for Python Developers – There’s no way around HTML and CSS when you want to build web apps. Even if you’re not aiming to become a web developer, knowing the basics of HTML and CSS will help you understand the Web better. In this tutorial, you’ll get an introduction to HTML and CSS for Python programmers.
Evolution of Access Control Explained Through Python – Sometimes, writing code can help you explore and understand concepts. This article shows a history of access controls in software, using Python scripts to reimplement the ideas.
Dangerous Pickles – A light introduction to the Python pickle protocol, the Pickle Machine, and the construction of malicious pickles. Learn why your code shouldn’t trust arbitrary serialized objects, and discover the dangers of pickle-bombs.
Building Command Line Interfaces With argparse – In this step-by-step Python video course, you’ll learn how to take your command line Python scripts to the next level by adding a convenient command line interface that you can write with argparse.

Discussion:

Projects:

Additional Links:

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Python as an Efficiency Tool for Non-Developers

Fri, 23 Sep 2022 12:00:00 +0000

Are you interested in using Python in an industry outside of software development? Would adding a few custom software tools increase efficiency and make your coworkers’ jobs easier? This week on the show, Josh Burnett talks about using Python as a mechanical engineer.

I met Josh at PyCon US 2022 in Salt Lake City, which he attended for the first time with several coworkers. He suggested we do an episode to shed some light on ways that Python is being used professionally by people who aren’t primarily programming for a living.

Josh works as a mechanical engineer for an equipment manufacturer, where he needs to perform repetitive tasks and generate copious logs. He explains how he moved his team away from MATLAB and toward Python.

We discuss his progression from writing scripts to developing packages and eventually hosting his work on PyPI. He also shares his explorations with CircuitPython for personal and professional projects.

Course Spotlight: Building Python Project Documentation With MkDocs

Topics:

00:00:00 – Introduction
00:02:10 – Meeting at PyCon US 2022
00:04:32 – Bringing members of team to PyCon
00:05:30 – What are next generation instrumentation platforms?
00:06:35 – What is the lifespan of a platform?
00:09:26 – Has interconnectivity affected upgrades?
00:10:57 – Programming and Python background
00:12:13 – Introduction to MATLAB at university
00:15:18 – Moving away from MATLAB to Python
00:19:39 – How was your transition from Python 2 to 3?
00:21:19 – Debugging methods and logging
00:22:27 – Why did you choose Python?
00:24:26 – Sponsor: Deepgram
00:25:12 – Promoting more use of Python in the organization
00:33:07 – Selling the idea of Python training in the organization
00:37:16 – Moving from scripts to building packages
00:43:48 – From personal project to critical package on PyPI
00:44:29 – Using PyPI or in-house package repository
00:46:27 – Experience with modern packaging tools
00:48:16 – Video Course Spotlight
00:49:32 – Using CircuitPython for personal and work projects
00:56:09 – Use of 3D printing and machining
00:57:33 – Josh’s projects on PyPI
01:02:57 – What are you excited about in the world of Python?
01:05:23 – What do you want to learn next?
01:08:46 – How can people follow your work?
01:09:07 – Thanks and goodbye

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Improve Matplotlib With Style Sheets & Python Async for the Web

Fri, 16 Sep 2022 12:00:00 +0000

Have you thought the standard output from Matplotlib is a bit generic looking? Would you like a quick way to add style and consistency to your data visualizations? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We cover an article about the magic of creating style sheets for Matplotlib. You can quickly customize plots and graphs with a single line of code. We share additional resources for you to try out new styles and learn what parameters are customizable.

Christopher covers an article about using async for web development in Python. The creation of Python generators inspired the development of async functionality. He discusses recent changes and async additions within Python web frameworks.

We cover several other articles and projects from the Python community, including how to install a pre-release version of Python, cache in Python with lru_cache, and get better at debugging, along with suggestions of libraries that deserve attention, a Python library for creating mathematical animations, and an extremely fast Python linter that’s written in Rust.

Course Spotlight: Python Debugging With pdb

Topics:

00:00:00 – Introduction
00:02:21 – Python releases 3.10.7, 3.9.14, 3.8.14, and 3.7.14 are now available
00:03:51 – How Can You Install a Pre-Release Version of Python?
00:08:13 – Understanding async Python for the Web
00:17:11 – The Magic of Matplotlib Style Sheets
00:24:20 – Sponsor: Platform.sh
00:24:56 – Caching in Python With lru_cache
00:29:41 – Some Ways to Get Better at Debugging
00:38:14 – Video Course Spotlight
00:39:26 – Suggest a Lesser Known Library Deserving Attention
00:44:52 – ruff: An Extremely Fast Python Linter, Written in Rust
00:48:19 – Manim: Python Library for Creating Mathematical Animations
00:51:50 – Thanks and goodbye

Show Links:

Python releases 3.10.7, 3.9.14, 3.8.14, and 3.7.14 are now available
How Can You Install a Pre-Release Version of Python? – If you want to have a peek at what’s coming in the next stable version of Python, then you can install a pre-release version. In this tutorial, you’ll learn how to access the latest Python versions and help test them.
Understanding async Python for the Web – “Recently Django 4.1 was released, and the thing most people seem interested in is the expanded async support. The Python web ecosystem has been seeing new frameworks pop up which are fully async, or support going fully async, from the start.” Learn more about async and its use in web frameworks.
The Magic of Matplotlib Stylesheets – With a single line of code, you can integrate a style sheet with your Matplotlib visualization. In this tutorial, you’ll learn how to make your very own custom reusable style sheet.
Caching in Python With lru_cache – Caching is an essential optimization technique. In this video course, you’ll learn how to use Python’s @lru_cache decorator to cache the results of your functions using the LRU cache strategy. This is a powerful technique you can use to leverage the power of caching in your implementations.
Some Ways to Get Better at Debugging – This is a short summary of a couple of academic papers on how to improve your debugging skills. The suggestions are to learn the codebase, learn the system, learn your tools, learn strategies, and gain experience.

Discussions:

Suggest a Lesser Known Library Deserving Attention

Projects:

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Exploring Recursion in Python With Al Sweigart

Fri, 09 Sep 2022 12:00:00 +0000

Have you wanted to understand recursion and how to use it in Python? Are you familiar with the call stack and how it relates to tracebacks? This week on the show, Al Sweigart talks about his new book, “The Recursive Book of Recursion.”

Recursion is one of those concepts held as a tenet of high-level computer science priesthood. Al explains the fundamentals of writing recursive functions and a critical missing piece in understanding how they operate, the call stack. After completing his research, he concluded that it’s a technique that you should understand but rarely use.

He also shares the few cases where recursion is an appropriate solution. Along the way, we talk about directed acyclic graphs, solving mazes, exploring file trees, and creating fractal images.

Course Spotlight: Caching in Python With lru_cache

Caching is an essential optimization technique. In this video course, you’ll learn how to use Python’s @lru_cache decorator to cache the results of your functions using the LRU cache strategy. This is a powerful technique you can use to leverage the power of caching in your implementations.

Topics:

00:00:00 – Introduction
00:01:55 – The Recursive Book of Recursion
00:02:55 – A Beginner’s Guide to Recursion - YouTube
00:05:41 – What is recursion?
00:10:17 – Understanding the call stack
00:12:15 – Languages moving from GOTO statements to functions and a stack
00:21:11 – A common recursion example of factorials
00:26:00 – Fibonacci sequence and memoization
00:30:25 – Cautionary advice on applying recursion
00:32:55 – What is recursion useful for?
00:39:56 – Video Course Spotlight
00:41:14 – Recursion and directed acyclic graphs
00:45:46 – Book examples
00:49:50 – Thoughts on tail recursion
00:54:34 – How has the scope of the book evolved?
01:00:34 – Creating examples in two languages
01:02:37 – Upcoming projects
01:05:19 – Examples of the projects in the book
01:10:30 – What are you excited about in the world of Python?
01:14:50 – What do you want to learn next?
01:19:06 – How can people follow your work?
01:19:48 – Thanks and goodbye

Show Links:

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Creating a Python Code Completer & More Abstract Syntax Tree Projects

Fri, 02 Sep 2022 12:00:00 +0000

How does a code completion tool work? What is an Abstract Syntax Tree, and how is it created in Python? How does an AST help you write programs and projects that inspect and modify your Python code? This week on the show, Meredydd Luff, co-founder of Anvil, shares his PyCon talk, “Building a Python Code Completer.”

Meredydd talks about his experience building a code completion engine for the Anvil platform. The conversation leads us to discuss how Python parses the code that you type. We examine tokenization, abstract syntax trees, and how parsing has changed in Python.

We cover additional projects that you can explore once you have a tool that inspects the Python code you’re writing. Join us as we dive into multiple rabbit holes of research and exploration.

Course Spotlight: Python Assignment Expressions and Using the Walrus Operator

In this course, you’ll learn about assignment expressions and the walrus operator. The biggest change in Python 3.8 was the inclusion of the := operator, which you can use to assign variables in the middle of expressions. You’ll see several examples of how to take advantage of this feature.

Topics:

00:00:00 – Introduction
00:02:11 – Building a Python Code Completer - Talk
00:08:48 – How is recursion being used?
00:10:28 – Abstract Syntax Trees, Parsers, and Tokens
00:14:58 – Sponsor: CData Software
00:15:39 – Is parsing into tokens a separate step?
00:21:45 – What makes the new PEG parser unique?
00:25:32 – Using the AST doesn’t have to be scary.
00:27:33 – More on the PEG parser
00:29:54 – Keeping scope and holding completions
00:39:43 – Video Course Spotlight
00:41:15 – Was this your first expirement in creating a code completer?
00:46:46 – How can adding type hints assist a completer?
00:50:53 – Projects you can build using AST
00:53:02 – Linter project
00:54:15 – Automatic code formatter
00:54:59 – Code testing and the pytest project
00:56:22 – Security tools
00:57:11 – Friendlier tracebacks
00:59:57 – Code completion vs GitHub Copilot
01:04:14 – Code completion in a text editor vs IDE
01:08:53 – What are you excited about in the world of Python?
01:09:54 – What do you want to learn next?
01:11:20 – How can people follow your work?
01:12:31 – Thanks and goodbye

Show Links:

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Configuring a Coding Environment on Windows & Using TOML With Python

Fri, 19 Aug 2022 12:00:00 +0000

Have you attempted to set up a Python development environment on Windows before? Would it be helpful to have an easy-to-follow guide to get you started? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python tutorial that covers configuring a Windows coding environment. The guide contains valuable suggestions, best practices, and powerful coding tools. It also covers how to use a package manager, the new Windows Terminal, PowerShell Core, and a program to manage multiple versions of Python.

Christopher covers another Real Python tutorial about using TOML in Python. TOML is a configuration format for building and distributing your own packages. We discuss how TOML parsing will be added to Python’s standard library in version 3.11.

We cover several other articles and projects from the Python community, on topics including technical writing for developers, a news round-up, a farewell to obsolete Python libraries, uncommon uses of Python in commonly used libraries, a prettier ls, and a project for advanced hot reloading in Python.

Course Spotlight: Python Basics: Finding and Fixing Code Bugs

In this Python Basics video course, you’ll learn how to identify and fix logic errors, or bugs, in your Python code. You’ll use the built-in debugging tools in Python’s Integrated Development and Learning Environment to practice locating and resolving bugs in an example function.

Topics:

00:00:00 – Introduction
00:02:13 – Python 3.10.6 Released
00:02:41 – Python 3.11.0rc1 Released
00:03:13 – Django 4.1 Released
00:04:07 – 10 malicious Python packages exposed in latest repository attack
00:05:12 – Protestware: Why Developers Sabotage Their Own Code
00:06:41 – Python and TOML: New Best Friends
00:16:19 – Say Goodbye to These Obsolete Python Libraries
00:25:51 – Video Course Spotlight
00:27:26 – Uncommon Uses of Python in Commonly Used Libraries
00:37:56 – Your Python Coding Environment on Windows: Setup Guide
00:48:20 – Technical Writing for Developers
00:55:24 – reloadium: Advanced Hot Reloading for Python
00:58:07 – pls: A Prettier ‘ls’
01:00:56 – Thanks and goodbye

News:

Topic Links:

Python and TOML: New Best Friends – TOML is a configuration file format that’s becoming increasingly popular in the Python community. In this tutorial, you’ll learn the syntax of TOML and explore how you can work with TOML files in your own projects.
Say Goodbye to These Obsolete Python Libraries – It’s time to say goodbye to os.path, random, pytz, namedtuple and many more obsolete Python libraries. Start using the latest and greatest ones instead.
Uncommon Uses of Python in Commonly Used Libraries – To learn more about writing maintainable Python, Eugene has been reading code from some of the more popular Python libraries. This blog post talks about some of the coding patterns he has encountered along the way.
Your Python Coding Environment on Windows: Setup Guide – With this opinionated guide to setting up a basic, fully featured, and flexible setup for Python coding and contributing to open-source projects when working from Windows, you’ll go from a fresh install to ready to contribute, and even check out a PowerShell script to automate much of the process.

Discussion:

Technical Writing for Developers – “The way we write about and around code is arguably as important as the code itself.” This article outlines how programming and writing come together to take your developer skills to the next level.

Projects:

Additional Links:

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Moving NLP Forward With Transformer Models and Attention

Fri, 12 Aug 2022 12:00:00 +0000

What’s the big breakthrough for Natural Language Processing (NLP) that has dramatically advanced machine learning into deep learning? What makes these transformer models unique, and what defines “attention?” This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, continues our talk about how machine learning (ML) models understand and generate text.

This episode is a continuation of the conversation in episode #119. Jodie builds on the concepts of bag-of-words, word2vec, and simple embedding models. We talk about the breakthrough mechanism called “attention,” which allows for parallelization in building models.

We also discuss the two major transformer models, BERT and GPT3. Jodie continues to share multiple resources to help you continue exploring modeling and NLP with Python.

Course Spotlight: Building a Neural Network & Making Predictions With Python AI

In this step-by-step course, you’ll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You’ll learn how to train your neural network and make predictions based on a given dataset.

Topics:

00:00:00 – Introduction
00:02:20 – Where we left off with word2vec…
00:03:35 – Example of losing context
00:06:50 – Working at scale and adding attention
00:12:34 – Multiple levels of training for the model
00:14:10 – Attention is the basis for transformer models
00:15:07 – BERT (Bidirectional Encoder Representations from Transformers)
00:16:29 – GPT (Generative Pre-trained Transformer)
00:19:08 – Video Course Spotlight
00:20:08 – How far have we moved forward?
00:20:41 – Access to GPT-2 via Hugging Face
00:23:56 – How to access and use these models?
00:30:42 – Cost of training GPT-3
00:35:01 – Resources to practice and learn with BERT
00:38:19 – GPT-3 and GitHub Copilot
00:44:35 – DALL-E is a transformer
00:46:13 – Help yourself to the show notes!
00:49:19 – How can people follow your work?
00:50:03 – Thanks and goodbye

Show Links:

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Inspiring Young People to Learn Python With Mission Encodeable

Fri, 05 Aug 2022 12:00:00 +0000

Is there someone in your life you’d like to inspire to learn Python? Mission Encodeable is a website designed to teach people to code, built by two high-school students. This week on the show, Anna and Harry Wake talk about creating their site and motivating people to start coding.

We discuss why they decided to build the site. Anna and Harry initially felt that the site would be for other students but soon realized it could be helpful for anyone interested in starting to code in Python. We cover the project-based approach and how they implemented the interactive browser-based tool replit.com.

We talk about learning Python in the classroom and how they found additional books and tutorials to supplement their coding education. Anna and Harry also created a resource hub to help teachers take advantage of the site.

Course Spotlight: Rock, Paper, Scissors With Python: A Command Line Game

In this course, you’ll learn to program rock paper scissors in Python from scratch. You’ll learn how to take in user input, make the computer choose a random action, determine a winner, and split your code into functions.

Topics:

00:00:00 – Introduction
00:02:17 – Personal backgrounds
00:02:51 – What’s the goal for the site?
00:03:54 – How did you come up with the idea?
00:05:08 – Where have you shared it?
00:06:39 – Projects for each level
00:09:28 – How has the response been?
00:10:10 – Using replit
00:12:56 – Sponsor: CData Software
00:13:37 – Design of the site and other tools to create it
00:15:49 – Learning Python and classes at school
00:17:41 – Did remote school inspire more online exploration?
00:19:16 – Myths of how kids learn programming
00:23:32 – More about projects
00:27:57 – Video Course Spotlight
00:29:27 – What other areas of Python do you want to explore?
00:33:08 – Teachers using the site
00:37:11 – What other resources have you used to learn Python?
00:38:52 – What are you excited about in the world of Python?
00:40:01 – What do you want to learn next?
00:42:06 – Thanks and goodbye

Show Links:

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Natural Language Processing and How ML Models Understand Text

Fri, 29 Jul 2022 12:00:00 +0000

How do you process and classify text documents in Python? What are the fundamental techniques and building blocks for Natural Language Processing (NLP)? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, talks about how machine learning (ML) models understand text.

Jodie explains how ML models require data in a structured format, which involves transforming text documents into columns and rows. She covers the most straightforward approach, called binary vectorization. We discuss the bag-of-words method and the tools of stemming, lemmatization, and count vectorization.

We jump into word embedding models next. Jodie talks about WordNet, Natural Language Toolkit (NLTK), word2vec, and Gensim. Our conversation lays a foundation for starting with text classification, implementing sentiment analysis, and building projects using these tools. Jodie also shares multiple resources to help you continue exploring NLP and modeling.

Course Spotlight: Learn Text Classification With Python and Keras

Topics:

00:00:00 – Introduction
00:02:47 – Exploring the topic
00:06:00 – Perceived sentience of LaMDA
00:10:24 – How do we get started?
00:11:16 – What are classification and sentiment analysis?
00:13:03 – Transforming text in rows and columns
00:14:47 – Sponsor: Snyk
00:15:27 – Bag-of-words approach
00:19:12 – Stemming and lemmatization
00:22:05 – Capturing N-grams
00:25:34 – Count vectorization
00:27:14 – Stop words
00:28:46 – Text Frequency / Inverse Document Frequency (TFIDF) vectorization
00:32:28 – Potential projects for bag-of-words techniques
00:34:07 – Video Course Spotlight
00:35:20 – WordNet and NLTK package
00:37:27 – Word embeddings and word2vec
00:45:30 – Previous training and too many dimensions
00:50:07 – How to use word2vec and Gensim?
00:51:26 – What types of projects for word2vec and Gensim?
00:54:41 – Getting into GPT and BERT in another episode
00:56:11 – How to follow Jodie’s work?
00:57:36 – Thanks and goodbye

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Creating Documentation With MkDocs & When to Use a Python dict

Fri, 22 Jul 2022 12:00:00 +0000

How do you start building your project documentation? What if you had a tool that could do the heavy lifting and automatically write large portions directly from your code? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python step-by-step project from Martin Breuss about MkDocs. The project walks you through generating nice-looking and modern documentation from Markdown files and your existing code’s docstrings. The final step is to deploy your freshly generated documentation to a GitHub repository.

Christopher talks about a pair of articles arguing for and against using Python dictionaries. The first article, “Just Use Dictionaries,” pushes to keep things simple, while the second article, “Don’t Let Dicts Spoil Your Code,” contends that complex projects require something more specific.

We cover several other articles and projects from the Python community, including discussing the recent beta release of Python 3.11, 2FA for PyPI, procedural music composition with arvo, building a tic-tac-toe game with Python and Tkinter, common issues encountered while coding in Python, a type-safe library to generate SVG files, and a lightweight static analysis tool for your projects.

Course Spotlight: Dictionaries and Arrays: Selecting the Ideal Data Structure

In this course, you’ll learn about two of Python’s data structures: dictionaries and arrays. You’ll look at multiple types and classes for both of these and learn which implementations are best for your specific use cases.

Topics:

00:00:00 – Introduction
00:02:39 – Python 3.11 Release May Be Delayed
00:03:39 – The cursed release of Python 3.11.0b4 is now available
00:05:01 – PyPI 2FA Security Key Giveaway
00:08:01 – Build Your Python Project Documentation With MkDocs
00:14:12 – Don’t Let Dicts Spoil Your Code
00:16:22 – Just Use Dictionaries
00:20:12 – Sponsor: Snyk.io
00:20:51 – Procedural Music Composition With arvo
00:29:10 – Build a Tic-Tac-Toe Game With Python and Tkinter
00:33:59 – Video Course Spotlight
00:35:35 – Most Common Issue You Have Coding With Python?
00:45:00 – svg.py: Type-Safe Library to Generate SVG Files
00:48:27 – semgrep: Lightweight Static Analysis for Many Languages
00:53:46 – Thanks and goodbye

News:

Topic Links:

Build Your Python Project Documentation With MkDocs – In this tutorial, you’ll learn how to build professional documentation for a Python package using MkDocs and mkdocstrings. These tools allow you to generate nice-looking and modern documentation from Markdown files and, more importantly, from your code’s docstrings.
Don’t Let Dicts Spoil Your Code – The dict is the go-to data structure for Python programmers, but its loose relationship to the data can be problematic in large data streams. Learn more about why and when you might choose a different data structure.
Just Use Dictionaries – Using simple data structures is an important part of keeping it simple, and Python is all about simplicity. Less code means fewer problems. Just use dictionaries. You probably don’t need classes.
Procedural Music Composition With arvo – By using the music21 and avro libraries, you can create musical scores programmatically. This article runs you through which libraries you need and how you can compose your own music.
Build a Tic-Tac-Toe Game With Python and Tkinter – In this step-by-step project, you’ll learn how to create a tic-tac-toe game using Python and the Tkinter GUI framework. Tkinter is cross-platform and is available in the Python standard library. Creating a game in Python is a great and fun way to learn something new and exciting!

Discussion:

Most Common Issue You Have Coding With Python?

Projects:

Additional Links:

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Measuring Python Code Quality, Simplicity, and Maintainability

Fri, 15 Jul 2022 12:00:00 +0000

How maintainable is your Python code? Is it possible to hold the code for your functions in your head? When is it appropriate to use measurements in a code review? This week on the show, Reka Horvath and Ben Martineau from Sourcery are here to discuss their recent PyCon talk, “Actionable insights vs ranking: How to use and how NOT to use code quality metrics.”

Reka and Ben share their thoughts on how metrics can provide insights into your Python code. We discuss four measurements of code complexity and what factors into each.

We also talk about deciding whether to refactor or rewrite code. Ben and Reka share their experience in code review situations and the importance of shifting the conversation from subjective opinions toward objective measurements.

Course Spotlight: Writing Idiomatic Python

What are the programming idioms unique to Python? This course is a short overview for people coming from other languages and an introduction for beginners to the idiomatic practices within Python. You’ll cover truth values, looping, DRY principles, and the Zen of Python.

Topics:

00:00:00 – Introduction
00:01:54 – Reka’s tutorials on Real Python
00:02:45 – PyCon US 2022 Talk
00:06:01 – Code reviews and metrics
00:09:42 – Trying to make things more objective
00:13:24 – Sponsor: CData Software
00:14:07 – Measuring WTFs/min and magical code
00:16:39 – Pythonic, idiomatic, and clean code
00:21:17 – Sourcery.ai and refactoring
00:24:04 – Four metrics to measure
00:24:56 – Function length
00:30:05 – Cyclomatic complexity
00:36:49 – Video Course Spotlight
00:38:21 – Cognitive complexity
00:44:38 – Working memory
00:51:49 – Suggestions on how to use the metrics
00:58:34 – Generating measurements
01:01:36 – What are you excited about in the world of Python?
01:03:30 – What do you want to learn next?
01:05:31 – Thanks and goodbye

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Exploring Functional Programming in Python With Bruce Eckel

Fri, 01 Jul 2022 12:00:00 +0000

Would you like to explore the functional programming side of Python? What are the advantages of this approach, and what tools are built into the language? This week on the show, author Bruce Eckel talks about functional programming in Python.

Bruce is the author of several programming books, including Thinking in Java, Thinking in C++, Thinking in Python, Atomic Scala, and most recently, Atomic Kotlin. He’s been an explorer of programming languages over his career. Functional programming—with its lack of side effects, its transparency, and its potential for parallelization—has recently caught Bruce’s attention.

Bruce’s talk “Making Data Classes Work for You” at PyCon US 2022 explored the idea of the invariance of objects.

We also discuss his next book project, the Python community, and his affection for “un-conferences”. Bruce is hosting the upcoming Summer Tech Forum in Crested Butte, Colorado, this August.

Course Spotlight: Using Data Classes in Python

Data classes were introduced in Python 3.7. When using data classes, you don’t have to write boilerplate code to get proper initialization, representation, and comparisons for your objects.

Topics:

00:00:00 – Introduction
00:02:15 – Musicians as programmers
00:03:40 – Happy Path Programming Podcast
00:04:19 – The essence of functional programming
00:10:55 – Making Data Classes Work for You - PyCon US 2022 talk
00:15:14 – What are things that have drawn you to Python?
00:17:39 – More than just a scripting language
00:21:35 – Consulting with companies
00:23:40 – Video Course Spotlight
00:25:04 – The frozen flag in a data class and __post_init__
00:29:02 – Invariance and functional programming
00:31:02 – How do you see it changing your programming?
00:34:49 – Not forcing an “approach” to Python
00:38:46 – Writing style in programming and books
00:42:31 – Who has been the intended audience for your books?
00:45:26 – Educational debt
00:46:50 – Python’s community
00:51:03 – New book about concurrency
01:00:51 – Winter and Summer Tech Forums
01:07:23 – Conference precautions and safety
01:08:51 – What are you excited about in the world of Python?
01:11:40 – What do you want to learn next?
01:12:23 – How can people follow the things you do?
01:13:13 – Thanks and goodbye

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Digging Into PyScript & Preventing or Handling Python Errors

Fri, 24 Jun 2022 12:00:00 +0000

Have you heard about PyScript? The brand-new framework has the community excited about building interactive Python applications that run entirely within the user’s browser. Would you like to dig into the details beyond the “Hello World” examples? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python tutorial from Bartosz Zaczyński about PyScript. The article provides an initial look at the framework and then takes you deep into the intricacies. We also share additional resources to help familiarize you with the project.

Christopher talks about another Real Python article, this one on how to approach managing errors in Python. The tutorial “LBYL vs EAFP: Preventing or Handling Errors in Python” is from frequent contributor Leodanis Pozo Ramos.

We cover several other articles and projects from the Python community, including discussing the PSF’s Python Developers Survey 2021 Results, Django static files and templates, how to profile Python code, a launcher for penetration testing, and a project for confirming Python versions through syntax errors.

Course Spotlight: Building a Django User Management System

In this video course, you’ll learn how to extend your Django application with a user management system, complete with email sending and third-party authentication.

Topics:

00:00:00 – Introduction
00:02:32 – LBYL vs EAFP: Preventing or Handling Errors in Python
00:08:38 – How To Profile Python Code
00:18:52 – Sponsor: CData Software
00:19:35 – Django Static Files and Templates
00:26:35 – A First Look at PyScript: Python in the Web Browser
00:38:20 – Video Course Spotlight
00:39:39 – Python Developers Survey 2021 Results
00:48:19 – python-syntax-errors: Version-Specific No-Ops
00:50:03 – arsenal: Inventory Launcher for Penetration Testing
00:55:03 – Thanks and goodbye

Topics Links:

LBYL vs EAFP: Preventing or Handling Errors in Python – In this tutorial, you’ll learn about two popular coding styles in Python: look before you leap (LBYL) and easier to ask forgiveness than permission (EAFP). You can use these styles to deal with errors and exceptional situations in your code. You’ll dive into the LBYL vs EAFP discussion in Python.
How To Profile Python Code – No matter how good you are, sometimes your code just runs slowly. Learning how to properly profile your software to identify and fix bottlenecks is a useful skill. This article talks about what you need to know to measure your code’s performance and how to use the cProfile, profile, and timeit libraries, along with others.
Django Static Files and Templates – “Static files like CSS, JavaScript, and fonts are a core piece of any modern web application. They are also typically confusing for Django newcomers since Django provides tremendous flexibility around how these files are used. This tutorial will demonstrate current best practices for configuring static files in both local development and production.”
A First Look at PyScript: Python in the Web Browser – In this tutorial, you’ll learn about PyScript, a new framework that allows for running Python in the web browser with few or no code modifications and excellent performance. You’ll leverage browser APIs and JavaScript libraries to build rich, highly interactive web applications with Python.

Discussion:

Projects:

Additional Links:

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Getting Started in Python Cybersecurity and Forensics

Fri, 17 Jun 2022 12:00:00 +0000

Are you interested in a career in security using Python? Would you like to stay ahead of potential vulnerabilities in your Python applications? This week on the show, James Pleger talks about Python information security, incident response, and forensics.

James has been doing information security for over fifteen years, working at some of the biggest companies, government agencies, and startups. He shares numerous Python resources to dive into detecting threats and improving your projects.

We discuss how to learn about security topics and get involved in the community. Make sure you check out the massive collection of links in the show notes this week.

Course Spotlight: Exploring HTTPS and Cryptography in Python

In this course, you’ll gain a working knowledge of the various factors that combine to keep communications over the Internet safe. You’ll see concrete examples of how to keep information secure and use cryptography to build your own Python HTTPS application.

Topics:

00:00:00 – Introduction
00:01:28 – How did you find the show?
00:02:00 – Evolution of roles in security
00:04:09 – Why is Python leveraged in security?
00:07:51 – Red team vs blue team
00:10:16 – Application security and bug bounties
00:13:31 – What’s your background?
00:14:07 – Company focus between regulations vs engineering
00:18:09 – Ways to get involved and keep learning
00:21:56 – Different perspective from computer science
00:23:35 – Red vs blue reprise
00:25:07 – Shifting landscape of vulnerabilities
00:30:06 – How do you approach tests?
00:32:30 – Incident response
00:35:54 – Video Course Spotlight
00:37:19 – Where does Python come in during an incident?
00:43:08 – Crossing into forensic research
00:48:43 – Where to practice security research and learn more?
00:51:41 – What’s the security community like?
00:56:05 – What are you excited about in the world of Python?
00:57:53 – What do you want to learn next?
01:00:17 – Where can people learn more about what you do?
01:00:39 – Thanks and goodbye

Security Specific Tools Written in Python:

Incident Response and Memory Forensics:

Honeypot Resources:

Bug Bounty Programs:

Security and Hacking Conferences:

Additional Links:

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Build Streamlit Data Science Dashboards & Verbose Regex f-Strings

Fri, 10 Jun 2022 12:00:00 +0000

Would you like a fast way to share your data science project results as an interactive dashboard instead of a Jupyter notebook? Streamlit is a library for creating simple web apps and dashboards using just Python. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about the article “Forget About Jupyter Notebooks - Showcase Your Research Using Dashboards.” It covers the basics of turning a data science script into an interactive dashboard using Streamlit. We also share some additional resources to get you started with the library.

Christopher discusses an article covering ways to make life easier when working with Python regular expressions. He talks about composing verbose regexes using f-strings and potentially reusing these patterns.

We cover several other articles and projects from the Python community, including a news roundup, a step-by-step project to build a URL shortener with FastAPI, the fact that Python’s functions are sometimes classes, an automatic water pistol pigeon deterrent project, a discussion about music playlists for coding, a project for Python metadata extraction without execution, and a powerful audio-to-MIDI converter library.

Course Spotlight: Using Python Class Constructors

In this video course, you’ll learn how class constructors work in Python. You’ll also explore Python’s instantiation process, which has two main steps: instance creation and instance initialization.

Topics:

00:00:00 – Introduction
00:02:22 – ctx Library Hijacked to Steal AWS Keys
00:04:33 – Typosquatting Attack on ‘requests’
00:06:55 – Build a URL Shortener With FastAPI and Python
00:10:51 – Sponsor: Rookout
00:11:31 – Python’s Functions Are Sometimes Classes
00:14:05 – Forget Jupyter, Showcase Your Data with Dashboards
00:22:08 – The Unreasonable Effectiveness of f-strings and re.VERBOSE
00:25:43 – Robotic Water Pistol as Pigeon Deterrent
00:28:13 – Video Course Spotlight
00:29:34 – Do You Have a Favorite Playlist for Coding?
00:40:05 – dowsing: Metadata Extraction Without Execution
00:42:01 – spotify/basic-pitch: A lightweight yet powerful audio-to-MIDI converter
00:49:12 – Thanks and goodbye

News:

Topic Links:

Build a URL Shortener With FastAPI and Python – In this step-by-step project, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.
Python’s Functions Are Sometimes Classes – Ever use list() or enumerate()? Think of them as functions? They’re not—they’re classes. Sometimes we call classes functions in Python. Why? And what’s a “callable”?
Forget Jupyter, Showcase Your Data with Dashboards – Streamlit can be used as an alternative to Jupyter notebooks for sharing research data. Streamlit is a relatively new library for creating simple web apps and dashboards using just Python. Learn why it might be the right choice for your next data project.
The Unreasonable Effectiveness of f-strings and re.VERBOSE – A look at one or two ways to make life easier when working with Python regular expressions.
Robotic Water Pistol as Pigeon Deterrent – Max built a wifi-equipped water gun to shoot the pigeons on his balcony. It is controlled over the Internet by a Python script running openCV reading the camera image from an old iPhone. See all the details.

Discussion:

Do You Have a Favorite Playlist for Coding?

Projects:

Additional Links:

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Managing Large Python Data Science Projects With Dask

Fri, 03 Jun 2022 12:00:00 +0000

What do you do when your data science project doesn’t fit within your computer’s memory? One solution is to distribute it across multiple worker machines. This week on the show, Guido Imperiale from Coiled talks about Dask and managing large data science projects through distributed computing.

We talk about projects where an orchestration system like Dask will help. Dask is designed to take advantage of parallel computing, spreading the work and data across multiple machines. Many familiar techniques for working with pandas and NumPy data are supported with Dask equivalents.

We also discuss the differences between managed and unmanaged memory. Guido shares advice on how to tackle memory issues while working with Dask.

This week we also talk briefly with Jodie Burchell, who will be a guest host on upcoming episodes. As a data scientist, Jodie will be bringing new topics, projects, and discussions to the show.

Course Spotlight: Exploring Scopes and Closures in Python

Topics:

00:00:00 – Introduction
00:01:56 – Guido at PyCon DE 2022
00:02:14 – Working on Dask for Coiled
00:03:27 – Dask project history
00:04:00 – How would someone start to use Dask?
00:10:28 – Managing distributed data
00:11:18 – Data files CSV vs Parquet
00:15:02 – Managed vs unmanaged memory
00:22:42 – Video Course Spotlight
00:24:01 – Dask active memory manager
00:28:36 – Learning best practices and Dask tutorials
00:33:06 – Where is Dask being used?
00:35:45 – What are you excited about in the world of Python?
00:37:55 – What do you want to learn next?
00:40:31 – Thanks, Guido
00:40:40 – Introduction to Jodie Burchell
00:45:28 – Goodbye

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Questions for New Dependencies & Comparing Python Game Libraries

Fri, 27 May 2022 12:00:00 +0000

What are the differences between the various Python game frameworks? Would it help to see a couple of game examples across several libraries to understand the distinctions? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a Real Python article by previous guest Jon Fincher titled “Top Python Game Engines”. Jon compares five different game frameworks and provides example projects and thorough commentary for each.

We talk about a blog post by recent guest Adam Johnson about determining if a project is well maintained. He suggests twelve questions to decide whether to add a new dependency to your project.

We cover several other articles and projects from the Python community, including a news roundup, Python decorator patterns, finding the smallest and largest values with min() and max(), a discussion about the most-used Python packages, the pony object-relational mapper, and a project to read PEPs in your console.

Course Spotlight: Using Pygame to Build an Asteroids Game in Python

In this course, you’ll build a clone of the Asteroids game in Python using Pygame. Step by step, you’ll add images, input handling, game logic, sounds, and text to your program.

Topics:

00:00:00 – Introduction
00:02:04 – News: Python Release Python 3.11.0b1
00:02:56 – Faster CPython project
00:04:16 – nogil conversation at the 2022 summit
00:06:08 – PEP 690: Lazy Imports
00:08:14 – DjangoCon US & Europe 2022 Call for Proposals
00:09:25 – Top Python Game Engines
00:20:38 – Sponsor: CData Software
00:21:20 – Python Decorator Patterns
00:24:00 – The Well-Maintained Test: 12 Questions for New Dependencies
00:29:27 – Python’s min() and max(): Find Smallest and Largest Values
00:33:33 – Video Course Spotlight
00:34:44 – Which Python Packages Do You Use the Most?
00:41:42 – pony: Pony Object Relational Mapper
00:47:41 – pepdocs: Read PEPs in Your Console
00:50:20 – Thanks and goodbye

News:

Topic Links:

Top Python Game Engines – In this tutorial, you’ll explore several Python game engines available to you. For each, you’ll code simple examples and a more advanced game to learn the game engine’s strengths and weaknesses.
Python Decorator Patterns – Decorators are a way of wrapping functions around functions, they’re a common technique for providing pre- and post-conditions on your code. Learn about the different ways decorators get invoked and how to write each pattern.
The Well-Maintained Test: 12 Questions for New Dependencies – There is lots of openly available code out there, but how do you know if you should build a dependency on some random coder’s package? 12 Questions you should ask yourself before using a library.
Python’s min() and max(): Find Smallest and Largest Values – In this tutorial, you’ll learn how to use Python’s built-in min() and max() functions to find the smallest and largest values. You’ll also learn how to modify their standard behavior by providing a suitable key function. Finally, you’ll code a few practical examples of using min() and max().

Discussion:

Which Python Packages Do You Use the Most?

Projects:

Additional Links:

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Advantages of Protobuf for Serialization in Python

Fri, 20 May 2022 12:00:00 +0000

Would you like a way to send structured serialized data between different platforms and languages? What if the data was self-documenting, could automatically generate Python code, and would validate itself? This week on the show, Liran Haimovitch talks about protocol buffers and communicating with microservices through Remote Procedure Calls (RPC).

Protocol buffers, aka protobuf, are a language-neutral, platform-neutral system for serializing structured data. Liran talks about how they go beyond text-based protocols like JSON, providing the benefits above, along with faster transmissions and a smaller footprint.

Liran shares how his company uses protobuf to communicate between their tools. We also discuss using gRPC to communicate between microservices and scaling infrastructure in either direction.

Course Spotlight: Testing Your Code With pytest

Topics:

00:00:00 – Introduction
00:01:59 – PyCon US 2022 Talk on protobuf
00:04:46 – PyCon 2019 Talk on Understanding Python’s Debugging Internals
00:05:34 – The Production-First Mindset Podcast
00:07:03 – Protobuf and serialization
00:11:17 – Static vs dynamic serializers
00:13:58 – Text vs binary serializers and metadata
00:21:08 – How long have you been using protobuf?
00:21:40 – What does it look like to set up?
00:24:45 – Video Course Spotlight
00:26:11 – Performance challenges and trade-offs
00:34:29 – Remote procedure calls
00:41:13 – Using RPC for microservices
00:47:21 – Scaling your infrastructure up or down
00:50:35 – Working across different languages
00:54:02 – What is Rookout?
00:55:11 – What are you excited about in the world of Python?
00:55:59 – What do you want to learn next?
00:56:57 – How can people learn more about what you do?
00:57:31 – Thanks and goodbye

Show Links:

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Start Testing Your Python with doctest & Pagination in Django

Fri, 13 May 2022 12:00:00 +0000

Did you know you can add testing to your Python code while simultaneously documenting it? Using docstrings, you can create examples of how your functions should interact in a Python REPL and test them with the built-in doctest module. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article by previous guest Mike Driscoll about testing with doctest. This is a great way to get started with testing your own code, and it offers the added benefit of documenting functionality.

We talk about the recent Real Python article “Pagination for a User-Friendly Django App.” Spreading your content across multiple pages can significantly improve the user experience of your web application. This article takes you through configuring Django’s built-in pagination tool and how to combine it with other web tools.

We discuss a recent article about Python type hints and the author’s disappointment. We also include reactions from a couple of online communities.

We cover several other articles and projects from the Python community, including why it’s important to close files in Python, how dunder methods are awesome, a bidirectional Python dictionary, prettier git diffs, and a command-line game to learn git.

Spotlight: Python Coding Interviews: Tips & Best Practices

In this step-by-step course, you’ll learn how to take your Python coding interview skills to the next level and use Python’s built-in functions and modules to solve problems faster and more easily.

Topics:

00:00:00 – Introduction
00:02:15 – PyCon US 2022 Follow-up
00:07:57 – Why Is It Important to Close Files in Python?
00:15:17 – Dunder Methods in Python: The Ugliest Awesome Sauce
00:24:26 – Sponsor: Mailtrap
00:25:08 – Python Testing With doctest
00:28:20 – Python Bidirectional Dictionary
00:30:27 – Pagination for a User-Friendly Django App
00:36:07 – Video Course Spotlight
00:37:27 – Python’s “Type Hints” are a bit of a disappointment to me
00:52:25 – dunk: Prettier Git Diffs
00:53:43 – git-gud: Command-Line Game to Learn git
00:55:40 – Thanks and goodbye

Topic Links:

Why Is It Important to Close Files in Python? – Model citizens use context managers to open and close file resources in Python, but have you ever wondered why it’s important to close files? In this tutorial, you’ll take a deep dive into the reasons why it’s important to close files and what can happen if you dont.
Dunder Methods in Python: The Ugliest Awesome Sauce – Double-underscore methods, also known as “dunder methods” or “magic methods” are an ugly way of bringing beauty to your code. Learn about constructors, __repr__, __str__, operator overloading, and getting your classes working with Python functions like len().
Python Testing With doctest – Python’s doctest module allows you to write unit tests through REPL-like sessions in your docstrings. Learn how to write and execute doctest code. Also available in video.
Python Bidirectional Dictionary – Learn about the Bidict library, a bidirectional dictionary where your keys and your values can both be used to look up an item. This can be a useful tool when dealing with mapped data, like country code to country name, where you want to look up either side of the relationship.
Pagination for a User-Friendly Django App – In this tutorial, you’ll learn how to serve paginated content in your Django apps. Using Django pagination can significantly improve your website’s performance and give your visitors a better user experience.

Discussion:

Projects:

Additional Links:

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Run Python in a Browser With Pyodide & The Power of f-Strings

Fri, 06 May 2022 12:00:00 +0000

Have you heard about the projects working toward getting Python to run in the browser? Maybe you would like to try it out for yourself, by building an interactive Python REPL with Pyodide and WebAssembly (WASM). This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about a step-by-step project that shows you how to build a Python code editor in the browser using WebAssembly through Pyodide and CodeMirror. You’re going to be hearing a lot about Pyodide in the coming months, and here’s a chance for you to play around while building a small project.

Christopher shares an article about the power of Python f-strings. It covers some lesser-known features like variable debugging, nesting, and detailed formatting.

We also have a couple of topics up for discussion this week. They’re both related to finding work as a Python developer.

We cover several other articles and projects from the Python community, including the 2038 date problem, a primer about Python virtual environments, how to build a site connectivity checker in Python, a free book on digital signal processing in Python, and a project to build a voice-activated, password-protected wooden box.

Spotlight: Using Python’s datetime Module

Have you ever wondered about working with dates and times in Python? In this video course, you’ll learn all about the built-in Python datetime library. You’ll also learn about how to manage time zones and daylight saving time, and how to do accurate arithmetic on dates and times.

Topics:

00:00:00 – Introduction
00:02:38 – 2038 Date Problem
00:07:46 – Python Virtual Environments: A Primer
00:16:06 – Python f-Strings Are More Powerful Than You Might Think
00:20:19 – Sponsor: CData Software
00:21:01 – Build an Editor in Python and WebAssembly
00:28:19 – Build a Site Connectivity Checker in Python
00:30:21 – What Jobs Can I Have Knowing Python?
00:39:53 – Video Course Spotlight
00:41:12 – Projects for a Self-Taught Dev to Help Get a Job?
00:50:46 – ThinkDSP: Free Book on Digital Signal Processing in Python
00:53:14 – Durin’s Box: Voice-Activated, Password-Protected Wooden Box
00:55:05 – Thanks and goodbye

Topic Links:

2038 Date Problem (Funny, but True) – Coders old enough to remember Y2K are already dreading 2038. Join the conversation.
Python Virtual Environments: A Primer – In this tutorial, you’ll learn how to use a Python virtual environment to manage your Python projects. You’ll also dive deep into the structure of virtual environments built using the venv module, as well as the reasoning behind using virtual environments.
Python f-Strings Are More Powerful Than You Might Think – Learn about the lesser-known features of Python’s f-strings, including date formatting, variable debugging, nested f-strings, and conditional formatting.
Build an Editor in Python and WebAssembly – Step-by-step instructions on how to build a code editor in the browser using Python and WebAssembly (WASM), via Pyodide and CodeMirror.
Build a Site Connectivity Checker in Python – In this step-by-step project, you’ll build a Python site connectivity checker for the command line. While building this app, you’ll integrate knowledge related to making HTTP requests with standard-library tools, creating command-line interfaces, and managing concurrency with asyncio and aiohttp.

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Projects:

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Type-Safe ORM With Prisma Client & Real Python at PyCon US 2022

Fri, 22 Apr 2022 12:00:00 +0000

Are you using an Object-Relational Mapper (ORM) for your Python projects? What if it could work with SQL or No-SQL databases and be fully type-safe? This week on the show, Robert Craigie talks about Prisma Client Python.

Prisma Client Python is built on top of Prisma, which was created for TypeScript and Node.js. It uses a schema file to declare your application’s data models and relationships in a human-readable form. The schema file allows you to easily switch the database type.

Prisma Client is different from other Python ORMs. It is fully type-safe and can be used with or without async.

We talk about how Robert started the project and what types of challenges he’s faced. He also shares areas of improvement and how to contribute to the project.

We also have a conversation with several Real Python core team members about PyCon US 2022. We will have a booth at the conference where we hope you’ll come and connect with us. The team also shares what to expect from PyCon and what they’re excited about this year.

Course Spotlight: Python REST APIs With FastAPI

In this course, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.

Topics:

00:00:00 – Introduction
00:02:15 – Have you worked on other open-source projects?
00:02:59 – What is Prisma?
00:05:00 – What are advantages of using an ORM?
00:06:52 – What problem is Prisma Client solving?
00:08:43 – What was involved in porting the project over?
00:09:55 – Creating a Prisma schema
00:12:09 – Other challenges along the way
00:14:57 – Dangers of not having type safety
00:16:27 – Sponsor: Linear B
00:17:06 – How long have you been working on the project?
00:18:57 – How could someone contribute to the project?
00:21:03 – In what situations does Prisma Client excel?
00:24:21 – What other projects do you currently work on?
00:24:57 – What are you excited about in the world of Python?
00:28:01 – What do you want to learn next?
00:29:52 – Thanks and Goodbye
00:30:14 – Introduction of RP team
00:31:09 – What are we doing at PyCon 2022?
00:35:55 – How to Get the Most Out of PyCon US
00:37:42 – Tutorials at PyCon US 2022
00:40:24 – Video Course Spotlight
00:41:49 – Talks at PyCon US 2022
00:50:16 – Sprints at PyCon US 2022
00:54:38 – Final thoughts
00:57:30 – Thanks and goodbyes

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Class Constructors & Pythonic Image Processing

Fri, 15 Apr 2022 12:00:00 +0000

Do you know the difference between creating a class instance and initializing it? Would you like an interactive tour of the Python Pillow library? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about the recent Real Python tutorial “Image Processing With the Python Pillow Library.” It walks you through manipulating, filtering, and creating images from scratch.

Christopher shares an article about Python class constructors, exploring the two-step instance creation and initialization process.

We also have a couple of discussions this week. The first is about contributing to open source projects. The second topic is about searching large codebases before adding features.

We cover several other articles and projects from the Python community, including the counter-intuitive rise of Python in scientific computing, preparation for interview questions, a project for adding pointer hell to Python, and a fast and powerful graphical user interface tool kit for Python with minimal dependencies.

Spotlight: Python vs JavaScript for Python Developers

Python and JavaScript are two of the most popular programming languages in the world. In this course, you’ll take a deep dive into the JavaScript ecosystem by comparing Python vs JavaScript. You’ll learn the jargon, language history, and best practices from a Python developer’s perspective.

Topics:

00:00:00 – Introduction
00:02:18 – Image Processing With the Python Pillow Library
00:11:10 – The Counter-Intuitive Rise of Python in Scientific computing
00:17:15 – 20 Python Interview Questions
00:25:48 – Sponsor: FusionAuth
00:26:25 – Python Class Constructors: Control Your Object Instantiation
00:31:17 – Do You Contribute to Open Source Projects?
00:42:43 – Video Course Spotlight
00:44:08 – How To Search Large Codebases Before Adding a Feature?
00:49:34 – pointers.py: Bringing the Hell of Pointers to Python
00:52:16 – DearPyGui: A fast and powerful Graphical User Interface Toolkit for Python
00:57:11 – Thanks and goodbye

Topic Links:

Image Processing With the Python Pillow Library – Learn how to use the Python Pillow library to deal with images. Combine this with some NumPy to process images and create animations.
The Counter-Intuitive Rise of Python in Scientific computing – Explore why Python’s ability to write code quickly and access more libraries can outperform heavily optimized compiled code.
20 Python Interview Questions – Practice up for that next interview. Questions about data structures, language concepts, and some common standard library functions.
Python Class Constructors: Control Your Object Instantiation – Learn how class constructors work in Python and explore the two steps of Python’s instantiation process: instance creation and instance initialization.

Discussion:

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Creating Better Error Messages for Python 3.10 & 3.11

Fri, 08 Apr 2022 12:00:00 +0000

What goes into creating those enhanced error messages in the latest versions of Python? How does the new PEG parser help to pinpoint where errors have occurred? This week on the show, Pablo Galindo Salgado talks about the work that goes into creating these improvements.

Pablo is a core CPython developer and is the release manager for Python versions 3.10 and 3.11. He is also serving his second term on the Python Steering Council.

Pablo is pleasantly surprised by the positive feedback for the new error messages in Python 3.10. He shares some of the upcoming enhancements for 3.11. We talk about how the new PEG parser allows for greater context when defining errors and pinpointing where they occur.

We talk about how he started contributing to CPython. He also shares some of the programming experiences he had while studying physics at university.

Course Spotlight: Starting With Linear Regression in Python

Topics:

00:00:00 – Introduction
00:01:56 – Member of the Python Steering Council
00:02:40 – Physics background and research use of Python
00:08:43 – How did you get involved in core development?
00:10:27 – Why did you take on the role of release manager?
00:13:38 – What challenges have you found along the way?
00:19:08 – Sponsor: LinearB
00:19:48 – What motivated you to add enhanced error messages?
00:29:33 – How does the PEG parser help in these situations?
00:37:04 – PEG parser and infinite lookahead
00:40:58 – Identifying where the syntax is wrong
00:43:28 – Finding where a comma is missing
00:48:49 – Is this a dictionary missing a colon, or is it a set?
00:50:19 – Identifying missing portions of a try … except block
00:51:44 – Video Course Spotlight
00:53:00 – Informing library maintainers and not slowing performance
00:56:18 – Enhanced error messages coming in 3.11
01:06:38 – Real Python preview of Python 3.11
01:07:28 – What are you excited about in the world of Python?
01:13:00 – What do you want to learn next?
01:15:43 – How to contribute to the project?
01:20:24 – Thanks and goodbye

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Building a Hash Table in Python and Thoughtful REST API Design

Fri, 01 Apr 2022 12:00:00 +0000

Do you understand how a hash table works? What if you could learn about building one while practicing test-driven development? What are best practices when designing a REST API? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about the recent Real Python article “Build a Hash Table in Python With TDD.” The tutorial shows how to implement a hash table prototype from scratch in Python. It also provides a hands-on crash course in test-driven development.

Christopher shares an article on designing REST APIs and provides some of his own best practices. We cover authentication implementation, good naming conventions, versioned APIs, and ways to specify dates.

We cover several other articles and projects from the Python community, including a news roundup, a PEP on removing dead batteries from the standard library, a comparison of the Python list vs tuple, a guide to writing user-friendly CLIs in Python, just enough Cython to be useful, a cross-platform TUI and ASCII animation package, and code for running black on Python code blocks in documentation files.

Course Spotlight: Command Line Interfaces in Python

Command-line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.

Topics:

00:00:00 – Introduction
00:02:23 – PEP 594: Removing Dead Batteries From the Standard Library
00:05:33 – Python 3.10.3, 3.9.11, 3.8.13, and 3.7.13 Now Available
00:08:37 – EuroPython 2022: Ticket Sales Open
00:09:34 – Python list vs tuple Comparison
00:12:19 – How to Write User-Friendly CLIs in Python
00:20:14 – Sponsor: Anvil
00:20:55 – Build a Hash Table in Python With TDD
00:26:11 – Just Enough Cython to Be Useful
00:36:21 – Video Course Spotlight
00:37:45 – How to Design Better REST APIs
00:47:10 – blacken-docs: Run black on Python Code Blocks within Documentation Files
00:49:09 – asciimatics: Cross Platform TUI and ASCII Animation Package
00:52:03 – Thanks and goodbye

News:

Topic Links:

Python list vs tuple Comparison – Learn how list and tuple are similar and how they’re different, including storage and speed differences and how to choose between them.
How to Write User-Friendly CLIs in Python – Learn how to write user-friendly command-line interface applications and an overview of several of the popular CLI libraries: argparse, Click, Typer, Docopt, and Fire.
Build a Hash Table in Python With TDD – In this step-by-step tutorial, you’ll implement the classic hash table data structure using Python. Along the way, you’ll learn how to cope with various challenges such as hash code collisions while practicing test-driven development (TDD).
Just Enough Cython to Be Useful – Cython is a superset of Python designed to give C-like performance. Ever wanted to learn the basics? This article shows you how to get started.
How to Design Better REST APIs – Fifteen language-agnostic tips on REST API design, including good naming conventions, ways to specify dates, versioned APIs, authentication keys, pagination, and when to use which HTTP methods.

Projects:

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Becoming More Effective at Manipulating Data With Pandas

Fri, 25 Mar 2022 12:00:00 +0000

Do you wonder if you’re taking the right approach when shaping data in pandas? Is your Jupyter workflow getting out of hand? This week on the show, Matt Harrison talks about his new book, “Effective Pandas: Patterns for Data Manipulation.”

Matt discusses working as a corporate consultant and migrating Excel users toward Python. We explore several “NumPy-isms” that beginners get stuck on. Matt shares advice about chaining operations in pandas, which some developers find controversial.

Course Spotlight: Sorting Data in Python With Pandas

In this video course, you’ll learn how to sort data in a pandas DataFrame using the pandas sort functions sort_values() and sort_index(). You’ll learn how to sort by one or more columns and by index in ascending or descending order.

Topics:

00:00:00 – Introduction
00:01:32 – Working as a consultant
00:03:39 – Moving from Excel to Python
00:06:50 – Who is the book for?
00:10:15 – Using real data for examples
00:16:16 – Sponsor: CData Software
00:16:58 – What are patterns for data manipulation?
00:18:38 – Cleaning and preparing data
00:21:33 – What concepts were you most eager to share?
00:26:57 – An example of chaining operations in pandas
00:33:20 – NumPy-isms and other challenges in learning pandas
00:40:20 – The use of exercises throughout the book
00:43:50 – Video Course Spotlight
00:45:01 – Challenges of using color throughout the book
00:51:40 – Avoiding the slow path in pandas
00:56:03 – What are you excited about in the world of Python?
00:56:58 – What would you like to learn next?
00:58:16 – Effective Pandas book
00:58:38 – Social connections
00:58:58 – Thanks and goodbye

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Making Your Notebook Interactive and Using Python's Assert

Fri, 18 Mar 2022 12:00:00 +0000

Would you like to build visualizations that allow your audience to play with data? How do you effectively use Python’s assert statement during development? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about an article that shows how to build interactive visualizations with pandas, seaborn, and ipywidgets. These widgets allow you to add sliders, buttons, and dropdown menus to your Jupyter Notebooks.

Christopher shares the Real Python article “Python’s assert: Debug and Test Your Code Like a Pro”. It covers how to use assert statements to document, debug, and test code while in development.

We cover several other articles and projects from the Python community, including a news roundup, code review guidelines for data science teams, a project to manage your to-do lists using Python and Django, a Python 4 dream list, a static site generator based on Django, and a book of practical Python projects.

Course Spotlight: Building a Neural Network & Making Predictions With Python AI

Topics:

00:00:00 – Introduction
00:02:12 – Tomli TOML Parser Will Be in Python 3.11 Standard Library
00:03:53 – Python Core Moving Bug Tracking to Github
00:05:54 – Python Release: Python 3.11.0a6
00:06:09 – PEPs have a new home with a shiny, new theme!
00:07:03 – Python’s assert: Debug and Test Your Code Like a Pro
00:10:35 – Sponsor: FusionAuth
00:11:12 – Interactive Visualizations with Pandas, Seaborn and Ipywidgets
00:16:06 – Code Review Guidelines for Data Science Teams
00:19:22 – Manage Your To-Do Lists Using Python and Django
00:23:56 – Video Course Spotlight
00:24:56 – Your Python 4 Dream List
00:34:37 – django-distill | Static Site Generator Based on Django
00:38:40 – Practical Python Projects Book
00:46:02 – Thanks and Goodbye

News:

Topic Links:

Python’s assert: Debug and Test Your Code Like a Pro – Learn how to use Python’s assert statement to document, debug, and test code in development.
Interactive Visualizations with Pandas, Seaborn and Ipywidgets – Create interactive visual output using ipywidgets.
Code Review Guidelines for Data Science Teams – Although written for data science teams, a good article on why any team of coders should do code reviews. How to do them, what to look for, and how to improve your code.
Manage Your To-Do Lists Using Python and Django – Use Django to build a to-do list manager app. This step-by-step tutorial will teach you how to use Django’s class-based views to build a powerful app while dramatically reducing your development time.
Your Python 4 Dream List – “If there was to ever be Python 4 (not a minor version increment, but full fledged new Python), what would you like to see in it?”

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Tools for Setting Up Python on a New Machine

Fri, 11 Mar 2022 12:00:00 +0000

There are many ways to get Python installed on your computer. If you were going to start fresh, what tools would you use? What if you need to manage multiple versions of Python and virtual environments? What about all the additional tools that make your coding workflow complete? This week on the show, Calvin Hendryx-Parker is here to talk about bootstrapping your Python environment.

Calvin is the co-founder and CTO of Six Feet Up and a Python Web Conference co-organizer. As a consultant, Calvin has set up countless machines to run Python. He configures environments that can scale from in-office projects to distributed cloud-based applications.

We cover tools for installing Python, managing multiple language versions, and configuring virtual environments. Calvin talks about setting up command-line applications in isolated environments. We also discuss traveling with Python on an iPad.

Course Spotlight: Start Managing Multiple Python Versions With pyenv

In this step-by-step course, you’ll learn how to install multiple Python versions and switch between them, including project-specific virtual environments, with ease with pyenv.

Topics:

00:00:00 – Introduction
00:02:42 – Giving talks at IndyPy
00:03:12 – Calvin’s talk “Half a Dozen Ways to Fail at Python”
00:07:41 – Advice on installing Python using Homebrew and pyenv
00:11:48 – The consistency of using a Docker container
00:18:49 – Creating virtual environments and installing packages
00:21:51 – Sponsor: ScoutAPM
00:22:38 – Creating requirements.txt files and moving from pipenv to piptools
00:30:54 – Paying attention to security issues and your software supply chain
00:34:19 – The goal of making a repeatable developer build experience
00:39:30 – Video Course Spotlight
00:40:47 – Using pipx to set up CLI code tools
00:44:52 – Using bpytop to monitor performance
00:46:41 – Tools for using Python on the iPad
00:53:06 – Python Web Conference
00:56:08 – Moving your dotfiles across multiple machines with chezmoi
01:02:22 – Thanks and goodbye

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Defining Optional Arguments and Moving Beyond "Beginner" Python

Fri, 04 Mar 2022 12:00:00 +0000

How do you define Python functions that accept optional arguments or default values? Are you wondering how to go beyond being a beginner with Python? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Christopher shares a Real Python article, “Defining Python Functions With Optional Arguments.” We talk about function flexibility, specifying default values, and using args & kwargs.

We discuss resources for a Python beginner to move beyond the basics and become more competent. Both of us share our experiences moving past these hurdles.

We cover several other articles and projects from the Python community, including a news roundup, ten tools you may have wished you knew when you started working with Python, Python’s zipfile for manipulating your zip files efficiently, how one company optimized Python API server code by 100x, a dependency-free Python library for downloading YouTube videos, and how to use Python on the command line with other shell commands.

Course Spotlight: Defining Python Functions With Optional Arguments

Topics:

00:00:00 – Introduction
00:03:08 – Django Project Reformats Entire Codebase With Black
00:03:53 – PEP 673: Self Type Was Accepted
00:05:16 – PEP 654 Accepted: Exception Groups
00:07:45 – Upcoming Python Feature PEPs
00:10:01 – Defining Python Functions With Optional Arguments
00:15:12 – Sponsor: CData Software
00:15:53 – 10 Tools I Wish I Knew When I Started Working With Python
00:23:22 – How We Optimized Python API Server Code 100x
00:29:47 – Python’s zipfile: Manipulate Your ZIP Files Efficiently
00:32:47 – Video Course Spotlight
00:33:59 – How Do I Go From Being a Beginner to Competent in Python?
00:47:02 – pytube: Dependency-Free Python Library for Downloading – YouTube Videos
00:49:30 – pz: Use Python on the Command Line With Other Shell Commands
00:52:23 – Thanks and goodbye

News:

Django Project Reformats Entire Codebase With Black – Related discussion on Hacker News.
PEP 673: Self Type Was Accepted – This PEP introduces a simple and intuitive way to annotate methods that return an instance of their class. This behaves the same as the TypeVar-based approach specified in PEP 484 but is more concise and easier to follow. Related Twitter thread by core dev Raymond Hettinger.
PEP 654 Accepted: Exception Groups – New standard for grouping exceptions together
Upcoming Python Feature PEPs – “These PEPs are a great way of getting the freshest info about what might be included in the upcoming Python releases. So, in this article we will go over all the proposals that are going to bring some exciting new Python features in a near future!”

Topics:

Defining Python Functions With Optional Arguments – Learn about Python optional arguments and how to define functions with default values. You’ll also see how to create functions that accept any number of arguments using *args and **kwargs.
10 Tools I Wish I Knew When I Started Working With Python – Learn about how venvs, flake8, black, isort, pytest, commitizen, semantic-release, pre-commit hooks, and Github Actions work together!
How We Optimized Python API Server Code 100x – Tricks we used to speed up calls to our analytical API written in Python: asyncio, SQLAlchemy, asyncpg, Cython plugins, improved data structures, and replaced some Pandas with NumPy.
Python’s zipfile: Manipulate Your ZIP Files Efficiently – In this guided tutorial, you’ll learn how to manipulate ZIP files using Python’s zipfile module from the standard library. Through hands-on examples, you’ll learn how to read, write, compress, and extract files from your ZIP files quickly.
How Do I Go From Being a Beginner to Competent in Python?

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OAuth 2 and Authentication Choices for Your Python Project

Fri, 25 Feb 2022 12:00:00 +0000

Have you thought about what authentication system you want to use for your Python project? Should you use an existing Python library or a third-party service? This week on the show, Dan Moore is here to talk about authentication systems and OAuth 2.

Dan is the head of developer relations at FusionAuth. He shares advice about setting up an authentication system, setting up device grants, using social login, and addressing privacy issues. Dan also provides multiple resources to learn much more about the topic.

Course Spotlight: Using Google Login With Flask

In this course, you’ll create a Flask application that lets users sign in using their Google login. You’ll learn about OAuth 2 and OpenID Connect and also find out how to implement some code to handle user session management.

Topics:

00:00:00 – Introduction
00:01:58 – What is FusionAuth?
00:03:13 – What is the single-responsibility principle?
00:04:14 – Thinking about setting up an authentication system
00:08:42 – Background on OAuth
00:13:26 – Device grants
00:19:23 – Using another provider’s login and addressing privacy issues
00:28:39 – Video Course Spotlight
00:29:53 – Resources to learn more about privacy and identity
00:32:39 – Setting up an OAuth system
00:35:59 – DIY authentication pitfalls and hashing passwords
00:42:57 – Staying on top of updates and social engineering
00:51:29 – Resources for learning more about OAuth
00:54:30 – What are you excited about in the world of Python?
00:55:42 – What do you want to learn next?
00:56:49 – Final words and social connections
00:57:47 – Thanks and goodbye

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Drawing Fractals With Python and Working With a Weather API

Fri, 18 Feb 2022 12:00:00 +0000

Have you been wanting to explore fractals and complex numbers in Python? Would you like to practice working with APIs in Python through a new project? This week on the show, Christopher Trudeau is here, and he’s taking on the task of curating new issues of PyCoder’s Weekly going forward. He’ll be joining me as a cohost every other week and bringing a fresh batch of PyCoder’s Weekly articles and projects.

We discuss a Real Python article about drawing the Mandelbrot set in Python. The tutorial guides you through creating fractal art using Matplotlib and Pillow. We also share a new step-by-step project building a weather-reporting command-line interface (CLI) app.

We cover several other articles and projects from the Python community, including a news roundup, a better Pygame main loop, ways to work with static and media files in Django, and a library for pleasing console output.

Course Spotlight: Make a 2D Side-Scroller Game With Pygame

In this step-by-step course, you’ll learn how to use Pygame. This library allows you to create games and rich multimedia programs in Python. You’ll learn how to draw items on your screen, implement collision detection, handle user input, and much more!

Topics:

00:00:00 – Introduction
00:02:15 – Black, the Python Code Formatter, Is Stable
00:04:07 – Apple Removes Python 2.7 in macOS Monterey 12.3
00:06:36 – GitHub Survey on Languages and More Developer Things
00:10:14 – CPython 3.11.0a5 Is Available
00:11:48 – Django Security Releases Issued: 4.0.2, 3.2.12, and 2.2.27
00:12:43 – A Better Pygame Main Loop
00:21:22 – Sponsor: CData Software
00:22:04 – Draw the Mandelbrot Set in Python
00:29:12 – The Fastest Way to Read a CSV in Pandas
00:35:16 – Video Course Spotlight
00:36:26 – Working With Static and Media Files in Django
00:41:42 – Raining Outside? Build a Weather CLI App With Python
00:48:15 – konsole: Readable, Pleasing Console Output
00:50:27 – Thanks and Goodbye

News:

Black, the Python Code Formatter, Is Stable – The popular Python autoformatter Black is finally non-beta software. Related discussion on Hacker News and Black’s stability policy doc.
Apple Removes Python 2.7 in macOS Monterey 12.3 – “Python 2.7 was removed from macOS in this update. Developers should use Python 3 or an alternative language instead.” Also see the related discussion on Hacker News.
GitHub Survey on Languages and More Developer Things – JavaScript retains the top spot, Python keeps second place gained in 2019 over Java, which holds in third. TypeScript continues fourth after racing up tenth in 2017 to fourth in 2020. JavaScript + TypeScript seem to put that way ahead in terms of amount of code on GitHub.
CPython 3.11.0a5 Is Available – “We needed to tame some angry buildbots, but after a small fight, we won with just some scratches! Here you have a shiny new alpha release: Python 3.11.0a5.”
Django Security Releases Issued: 4.0.2, 3.2.12, and 2.2.27 – Includes fixes for a possible XSS via debug template tag and a denial-of-service possibility in file uploads.

Articles:

A Better Pygame Main Loop – Improving your game’s main loop for smoother gameplay that takes less battery power: “Now your players’ laptops run cool while playing, and the graphics don’t have ugly tearing artifacts any more!”
Draw the Mandelbrot Set in Python – In this tutorial, you’ll visualize the famous Mandelbrot set using Python’s Matplotlib and Pillow libraries. You’ll learn how to draw the fractal in black and white, grayscale, and color.
The Fastest Way to Read a CSV in Pandas – This article covers pandas’ default CSV reading, the faster and more parallel CSV reader introduced in v1.4, and a different approach that can make things even faster.
Working With Static and Media Files in Django – A look at how to work with static and media files in a Django project, locally and in production.

Projects:

Raining Outside? Build a Weather CLI App With Python – In this tutorial, you’ll write a nicely formatted Python CLI app that displays information about the current weather in any city that you provide the name for.
konsole: Readable, Pleasing Console Output

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Improving Your Django and Python Developer Experience

Fri, 11 Feb 2022 12:00:00 +0000

How often have you thought about your Developer Experience (DX)? How do you improve your workflow, find documentation, and simplify code formatting? This week on the show, Adam Johnson is here to talk about his new book, “Boost Your Django DX.”

Adam provides advice on improving your developer experience, specifically inside Django and within Python. We discuss tools to simplify code formatting and linting.

Adam shares a couple of his own Django tools designed to simplify development and keep your projects updated. He also covers documentation resources for finding answers quickly.

Course Spotlight: Django View Authorization: Restricting Access

This course covers how to restrict your web pages to users with different roles through Django view authorization. You’ll learn about HttpRequest.user objects, decorators that authenticate views, and ways to notify your users with the Django messages framework.

Topics:

00:00:00 – Introduction
00:01:53 – The Django Project Technical Board
00:04:08 – Change in the release cadence for Django
00:06:08 – Kaizen, continous improvement, and helping others improve
00:10:23 – Common ways to improve developer experience
00:14:06 – Sponsor: Scout APM
00:14:54 – Developer documentation tools
00:20:24 – Bang shortcuts when searching with DuckDuckGo
00:22:37 – Who is the intended audience for the book?
00:23:33 – Managing secrets and giving general advice
00:26:49 – Writing books to fill in the gaps
00:29:12 – Video Course Spotlight
00:30:24 – Topics for the blog, favicons, and type hints
00:39:22 – What are your recent Django-related projects?
00:42:05 – Sources for Django news
00:45:01 – What are you excited about in the world of Python?
00:45:59 – What do you want to learn next?
00:47:43 – Shout-outs and social connections
00:48:43 – Thanks and goodbye

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Manipulating and Analyzing Audio in Python

Fri, 04 Feb 2022 12:00:00 +0000

Would you like to experiment with analyzing or manipulating audio with Python? This week on the show, we have Braden Riggs from DolbyIO to discuss extracting audio features and Python libraries for reshaping audio. Braden shares techniques from his recent talk at PyData Global, “Unlocking More From Your Audio Data!”

We share several articles, tutorials, and Python libraries to get you started working with audio. We begin with a quick introduction to audio digitization and feature extraction. After discussing modifying audio files, we share tools and techniques for exploring audio analysis through data science.

Braden talks about speech-to-text conversion, sentiment analysis, and Natural Language Toolkit (NLTK). He also shares a valuable collection of audio technology resources for developers.

Course Spotlight: Playing and Recording Sound in Python

Topics:

00:00:00 – Introduction
00:02:46 – What is Dolby IO?
00:05:08 – Audio and data science background
00:08:17 – Digital audio basics
00:12:07 – Audio feature extraction
00:18:31 – Sponsor: Snyk - The Big Fix
00:19:17 – PyAudioAnalysis and processing audio files with Python
00:26:16 – Feature extraction and audio analysis talk at PyData Global
00:32:33 – Jupyter notebook of presentation and DolbyIO awesome audio list
00:34:13 – librosa - package for music analysis
00:37:55 – Video Course Spotlight
00:39:11 – Speech to text and NLTK
00:54:28 – What are you excited about in the world of Python?
00:55:49 – What do you want to learn next?
00:56:53 – Shout outs and social connections
00:58:04 – Thanks and goodbye

Show Links:

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What Is a JIT and How Can Pyjion Speed Up Your Python?

Fri, 28 Jan 2022 12:00:00 +0000

How can you can speed up Python? Have you thought of using a JIT (Just-In-Time Compiler)? This week on the show, we have Real Python author and previous guest Anthony Shaw to talk about his project Pyjion, a drop-in JIT compiler for CPython 3.10.

Anthony has been working on Pyjion over the past year and recently released version 1.0. He talks about how he took over the project from Brett Cannon and Dino Viehland. He covers the background on compilers and assembly that he needed to take on this project.

We discuss where a tool like this can speed up your Python code, and we consider alternative solutions. We also talk about his desire to make the project as deeply compatible with Python code as possible.

Anthony talks about how his dive into writing the CPython Internals book led him into the project. We talk about what type of developer would benefit from exploring the book.

We also cover his recent Real Python article, titled “Advanced Visual Studio Code for Python Developers.” It’s an excellent resource that VS Code users should bookmark to revisit as they grow with the tool.

Spotlight: CPython Internals Book: Your Guided Tour Through the Python 3 Interpreter

Unlock the inner workings of the Python language, compile the Python interpreter from source code, and participate in the development of CPython.

Topics:

00:00:00 – Introduction
00:02:15 – Cloud Developer Advocate at Microsoft
00:04:57 – Pyjion, a drop-in JIT compiler for CPython
00:07:52 – PyCon 2020 & 2021 talks and wanting to take on the project
00:12:46 – How Pyjion uses .NET 6
00:17:32 – Trying out Pyjion functionality online
00:21:43 – Sponsor: Honeybadger
00:22:28 – Portability of projects using Pyjion
00:29:55 – Focus on compatibility with Python code
00:33:07 – Choosing to make it based on Python 3.10
00:37:45 – What would be prerequisites to work on the project?
00:40:40 – Other ways to help with project
00:44:34 – CPython Internals: Who is the book for?
00:49:46 – What resources do you need to work through the book?
00:52:04 – Spotlight: CPython Internals Book
00:53:21 – Do you use an IDE or code editor?
00:56:12 – Why did you decide to write the book?
00:57:12 – Advanced Visual Studio Code for Python Developers
01:03:20 – What are you excited about in the world of Python?
01:04:03 – What do you want to learn next?
01:05:33 – Thanks and goodbye

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Designing for Users and Building a Social Network With Django

Fri, 21 Jan 2022 12:00:00 +0000

Are you looking for a project to practice your Django skills? Designing the fundamental interactions of a social network is an instructive way to explore models and relationships while learning advanced Django skills. This week on the show, we talk with previous guest Martin Breuss about his new four-part series, “Build a Social Network With Django”.

Martin talks about the design process and ways to include user-focused features. He shares a handy CSS library to make your front end look modern with minimal effort.

We digress into a discussion of personal vs professional use of social networks. We also talk about the additional Real Python resources to help you build out the rest of your project.

Course Spotlight: How to Set Up a Django Project

Topics:

00:00:00 – Introduction
00:01:46 – Build a Social Network with Django series
00:04:05 – Setting project scope and constraints
00:09:09 – Why use Django for this project?
00:12:23 – Learning through errors
00:15:29 – Sponsor: Honeybadger
00:16:14 – Models and relationships
00:19:05 – Implementing a post-save hook
00:23:17 – Django front end with the Bulma CSS framework
00:28:42 – Templates and profile pages
00:31:03 – Following, posts, and thoughts on user-centric features
00:37:18 – Personal and professional use of social media
00:48:03 – Setting up redirects
00:49:08 – Video Course Spotlight
00:50:19 – Additional Real Python resources for Django, including a Learning Path
00:53:07 – What would you add if this were a portfolio project?
00:54:14 – What are you excited about in the world of Python?
00:56:36 – What do you want to learn next?
00:57:54 – Thanks and goodbye

Show Links:

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Launching Python, Virtual Environments, and Locking Dependencies With Brett Cannon

Fri, 14 Jan 2022 12:00:00 +0000

Would you like a simple command to launch your Python programs using the newest version of the language installed on your machine? This week on the show, we continue our conversation with Brett Cannon. Brett discusses his project, the Python Launcher for Unix.

We dive into Brett’s workflow to set up projects, virtual environments, and prepare for distribution. He shares some of the tools he employs and reasons for keeping things simple.

We also talk about PEP 665, which specifies a file format to list an application’s Python package installation requirements. Brett shares why he co-authored the PEP and a bit of the community’s reaction. It leads to a deeper conversation about going beyond ‘requirements.txt’ to lock files.

Course Spotlight: Data Visualization Interfaces in Python With Dash

In this course, you’ll learn how to build a dashboard using Python and Dash. Dash is a framework for building data visualization interfaces. It helps data scientists build fully interactive web applications quickly.

Topics:

00:00:00 – Introduction
00:01:43 – Python Launcher for Unix
00:09:36 – Hiding a virtual environment directory
00:13:30 – Sponsor: Scout APM
00:14:17 – Environment managers and being an editor vs an IDE person
00:17:32 – PEP 665, lock files, and Brett’s dependency workflow
00:20:56 – The hurdles of source distributions and version control repositories
00:25:07 – Drawbacks of requirements files and standardizing lock files
00:28:19 – Why PEP 665 is controversial and the changes to setuptools
00:33:48 – Video Course Spotlight
00:35:16 – Are these examples of growing pains?
00:37:51 – Unfair comparisons to packaging in Rust or other languages
00:42:35 – What are you excited about in the world of Python?
00:43:54 – What do you want to learn next?
00:49:23 – Thanks and goodbye

Show Links:

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Continuing to Unravel Python's Syntactic Sugar With Brett Cannon

Fri, 07 Jan 2022 12:00:00 +0000

A year ago, we had Brett Cannon on the show to discuss his blog series about unravelling Python’s syntactic sugar. Brett has written 15 more entries in the series, and he returns to the show this week to continue our conversation. We dive into unravelling ‘async’ and ‘await’ statements and their relationship with Python’s generators.

While working through the series, he has uncovered some issues lying under the surface of CPython. We discuss a couple of these discoveries and how the core developers resolved them.

Brett was recently re-elected to the Python Steering Council, and he talks about how the current direction of the council is shifting. We also discuss how Brett uses Twitter polls to gauge community sentiment and factors it into development decisions.

This conversation covered so many topics that we split it into two episodes. Next week’s episode will continue our discussion.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

Topics:

00:00:00 – Introduction
00:01:54 – Python Steering Council Reelection
00:06:06 – Python Extension for VSCode and the Language Server Protocol (LSP)
00:10:38 – Using twitter polls to gauge sentiment
00:19:19 – Sponsor: Honeybadger
00:20:04 – Unravelling Python’s syntactic sugar series
00:25:30 – The desugar GitHub repo and unearthing problems in CPython
00:29:48 – Why have __iter__() for an iterator?
00:32:47 – Is discovering and showcasing these “issues” stressful?
00:35:19 – A reverse chronology of some Python features
00:38:34 – How the heck does async/await work in Python
00:51:29 – Video Course Spotlight
00:52:54 – Using “spam” vs “foo” and metasyntactic variables
00:57:30 – Goodbye

Show Links:

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2021 Real Python Articles Wrap Up

Fri, 24 Dec 2021 12:00:00 +0000

It’s been a year of change at Real Python! The Real Python team has written, edited, curated, illustrated, and produced a mountain of Python articles this year. We also added many new members to the team, updated the site’s features, and created new styles of tutorials and projects.

Two members of the Real Python team join us this week, Martin Breuss and Sadie Parker. We wanted to share a year-end wrap-up with a collection of articles and step-by-step projects that showcase what our team created this year.

Sadie and Martin help to shepherd articles through the multi-stage editing process. Along with the rest of the team, they make sure these resources impart crucial Python knowledge and provide a thorough didactic experience.

We hope you enjoy this review! Programming note, there won’t be an episode next week, but we will be back in January and look forward to bringing you a year full of great guests, articles, and topics.

Course Spotlight: Using Pygame to Build an Asteroids Game in Python

In this course, you’ll learn the basics of creating powerful web applications with In this course, you’ll build a clone of the Asteroids game in Python using Pygame. Step by step, you’ll add images, input handling, game logic, sounds, and text to your program.

Topics:

00:00:00 – Introduction
00:02:24 – Changes at Real Python Over 2021
00:10:11 – Build a Platform Game in Python With Arcade
00:13:40 – Make Your First Python Game: Rock, Paper, Scissors!
00:20:00 – Build a Content Aggregator in Python
00:25:30 – Video Course Spotlight
00:26:45 – Python Inner Functions: What Are They Good For?
00:30:44 – Build a Personal Diary With Django and Python
00:34:14 – Hosting a Django Project on Heroku
00:42:00 – Python & APIs: A Winning Combo for Reading Public Data
00:46:03 – Beautiful Soup: Build a Web Scraper With Python
00:52:25 – Advanced Visual Studio Code for Python Developers
00:59:55 – A Few Special Mentions, Data Science, and More
01:02:33 – Thanks and goodbye

Show Links:

Additional Links:

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A Python Journey: Cyber Security, Automating AWS, and TDD

Fri, 17 Dec 2021 12:00:00 +0000

The Python community continually grows, with many users coming from different languages and backgrounds. This week on the show, we talk with developer Hugh Tipping about his Python journey. Hugh is also a member of the Real Python community.

Hugh has a background in programming C and Perl and started to use Python in a cyber security job. He explains the way he used Python to search for malware. Hugh provides some suggestions for security packages and tools.

Recently Hugh has been working with Amazon Web Services (AWS) and using the Boto3 library to automate services and manage credentials. We also discuss his affinity for test-driven development and the use of pytest.

Course Spotlight: Host Your Django Project on Heroku

In this course, you’ll learn how to host your Django project in the cloud for free and with little hassle. You’ll use Heroku, which takes the burden of infrastructure management off your shoulders.

Topics:

00:00:00 – Introduction
00:01:44 – How did you get introduced to Python?
00:05:52 – What types of tasks were you doing in cyber security?
00:10:21 – Why do you think Python is popular for cyber security?
00:14:57 – Python libraries to check for security vulnerabilities
00:18:07 – Sponsor: Cloudsmith
00:18:52 – Boto3 for managing AWS with Python
00:25:21 – What is your background with test-driven development?
00:33:58 – Video Course Spotlight
00:35:22 – Finding Real Python and learning about Python tools
00:41:42 – Getting involved in the Real Python community
00:45:45 – What are excited about in the world of Python?
00:47:32 – What do you want to learn next?
00:53:14 – Thanks and goodbye

Show Links:

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Solving Advent of Code Puzzles With Python

Fri, 10 Dec 2021 12:00:00 +0000

Are you ready to break open the first days of puzzles from the annual Advent of Code challenge? Advent of Code is an advent calendar of twenty-five programming puzzles published each December. Practicing solving puzzles is a great way to build your Python skills. This week on the show, we have previous guest and Real Python author Geir Arne Hjelle to discuss his recent article titled, “Advent of Code: Solving Your Puzzles With Python.”

We discuss the history of this online Advent calendar and the creator Eric Wastl. Geir Arne covers techniques to help you organize your code and test when solving the puzzles. He shares additional Python tools for parsing text, managing puzzle data, and visualizing your program output. We also talk about even more places you can practice solving programming puzzles.

Course Spotlight: Regular Expressions and Building Regexes in Python

Topics:

00:00:00 – Introduction
00:02:03 – Geir Arne’s new full-time role at Real Python
00:02:55 – Advent of Code: Solving Your Puzzles With Python
00:09:25 – Using different languages to solve the puzzles
00:12:21 – Different ways to approach the problems
00:16:31 – Sponsor: CData Software
00:17:12 – Organizing your code into reusable functions
00:19:38 – Unique user source data and managing puzzle data
00:24:40 – Additional Python tools that might be useful
00:31:13 – Video Course Spotlight
00:32:42 – Ways to enhance code output and visualization
00:34:50 – Leaderboards
00:37:52 – Real Python office hours discussion of Advent of Code
00:39:30 – Additional resources to practice solving puzzles with Python
00:50:15 – Supporting Advent of Code
00:50:40 – What are you excited about in the world of Python?
00:51:59 – What do you want to learn next?
00:55:52 – Thanks and goodbye

Show Links:

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Discussing Type Hints, Protocols, and Ducks in Python

Fri, 03 Dec 2021 12:00:00 +0000

There seem to be three kinds of Python developers: those unaware of type hints or have no opinion, ones that embrace them, and others who have an allergic reaction at the mention of them. Python is famously a dynamically typed language, but there are advantages to adding type hints to your code. This week on the show, we have Luciano Ramalho to discuss his recent talk titled, “Type hints, protocols, and good sense.”

Luciano was not a fan of type hints. He’s only recently come around to their potential with the introduction of protocols in PEP 544. Python has adopted a gradual type system that is optional at all levels. We discuss the advantages, pitfalls, and recent developments around type hinting in Python.

We also talk about the second edition of Luciano’s book Fluent Python. He researched type hints in-depth for the book, which led to his recent conference talks on the subject. He also shares his experience with adding opinionated asides to the book in a fun and unique way.

Course Spotlight: Python Type Checking

In this course, you’ll look at Python type checking. Traditionally, types have been handled by the Python interpreter in a flexible but implicit way. Recent versions of Python allow you to specify explicit type hints that can be used by different tools to help you develop your code more efficiently.

Topics:

00:00:00 – Introduction
00:02:02 – Are you interested in creative uses for Python?
00:04:41 – Protocol: The keystone of type hints
00:08:14 – What is duck typing?
00:12:44 – Protocols declaring one method and emerging from a code base
00:17:04 – An example where type hint was too lax
00:21:20 – What if Python always had a strict type system?
00:33:23 – Sponsor: Cloudsmith
00:34:09 – Bias in companies using type hints, and projects that fail checking
00:40:27 – Background on personal use of type hints and added complexity
00:45:07 – Unsuitability of type hints for checking business rules
00:52:30 – Video Course Spotlight
00:53:46 – Fluent Python, 2nd edition
00:56:05 – Who is the intended developer for the book?
00:58:12 – Soapbox sections of the book
00:59:35 – What were things you were excited to update or add to the book?
01:05:46 – Metaprogramming portion of the book
01:08:17 – What are you excited about in the world of Python?
01:10:35 – What do you want to learn next?
01:18:41 – Shoutouts, plugs, and/or social connections
01:19:47 – Thanks and goodbye

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Building a Content Aggregator and Working With RSS in Python

Fri, 19 Nov 2021 12:00:00 +0000

Have you wanted to work with RSS feeds in Python? Maybe you’re looking for a new project to build for your portfolio that uses Django, unit tests, and custom commands. This week on the show, we have Real Python author Ricky White to talk about his recent step-by-step project titled, “Build a Content Aggregator in Python.”

Ricky has been authoring the Real Python interview series for several years and was formerly our Community Manager. He talks about what inspired him to create this project and the Python technology and libraries to build it. He also shares advice about adding tests to personal portfolio projects.

We start the show by discussing Python’s GIL (Global Interpreter Lock) and the efforts to potentially remove it in future versions of Python. This change could make a significant impact on Python code running on multi-core processors. We talk about two recent articles covering the developments.

Course Spotlight: Get Started With Django: Build a Portfolio App

In this course, you’ll learn the basics of creating powerful web applications with Django, a Python web framework. You’ll build a portfolio website to showcase your web development projects, complete with a fully functioning blog.

Topics:

00:00:00 – Introduction
00:02:07 – Citizenship
00:03:52 – Ricky’s Real Python interviews
00:05:55 – Upcoming interview with Eric Wastl about Advent of Code
00:08:05 – Notes From the Meeting On Python GIL Removal
00:18:41 – Sponsor: Cloudsmith
00:19:26 – Build a Content Aggregator in Python
00:20:28 – Django background
00:23:37 – What web technologies were you using before Python?
00:25:07 – What motivated the project?
00:26:46 – Technical hurdles
00:30:52 – Including tests in a portfolio project
00:32:56 – Django custom commands
00:37:02 – Video Course Spotlight
00:38:16 – RSS Feeds - Really Simple Syndication and Podcasts
00:42:16 – Working with django-apscheduler
00:47:06 – Taking the project further and CSS frameworks
00:51:04 – What are you excited about in the world of Python?
00:53:58 – What do you want to learn next?
00:55:52 – Shoutouts and social connections
00:56:57 – Thanks and goodbye

Show Links:

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The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming

Fri, 12 Nov 2021 12:00:00 +0000

Do you remember the One Laptop Per Child program? What went wrong, and what can we learn from the program’s failure? What are the potential pitfalls of charismatic technology, and how can we avoid them when introducing students to programming? This week on the show, former guest Al Sweigart and author Morgan Ames are here to talk about her book “The Charisma Machine - The Life, Death, and Legacy of One Laptop per Child.”

We discuss the OLPC program and how idealized visions of our programming backgrounds can become traps. Morgan explains how these utopian visions are still used to attempt to disrupt education. Along with this cautionary tale, we also talk about educational programs that are working and how entry points to programming are changing.

Course Spotlight: Using Pandas to Make a Gradebook in Python

Topics:

00:00:00 – Introduction
00:02:13 – Morgan’s background
00:02:58 – Computer Science and Information Science
00:04:46 – Early introduction of computers to schools
00:08:25 – What was the One Laptop Per Child (OLPC) Program?
00:12:32 – Turtle art and Scratch
00:16:34 – Sponsor: CData Software
00:17:15 – What is the global south?
00:19:12 – How does charisma apply to objects and technology?
00:28:30 – Python language design and Learn to Code
00:34:53 – Media technology scares and the self taught coder
00:40:35 – Video Course Spotlight
00:41:41 – Technically precocious boys and exclusion of others
00:43:47 – Minecraft and technology maintenance skills
00:49:08 – Skewed utopian visions and lack of ongoing support
00:52:54 – Shifting feminine vs masculine perceived roles in computing
01:02:41 – Changing entry points for programming
01:09:00 – The why of “learn to code” and empowerment
01:13:13 – Metaverse, nostalgia, and dystopia
01:14:45 – The Diamond Age and Code Hero Kickstarter
01:16:53 – Avoiding future charismatic traps
01:18:28 – Shoutouts and social connections
01:21:04 – Thanks and goodbyes

Show Links:

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Exploring Django Templates, Tags, and Filters

Fri, 05 Nov 2021 12:00:00 +0000

Are you getting the most out of the Django framework? It’s a powerful web framework if you’re not interested in reinventing the wheel. Django includes a useful template system with inheritance for composing reusable HTML. This week on the show, we have previous guest and Real Python author Christopher Trudeau to talk about his recent articles and courses about Django.

Christopher explains how Django templates help you avoid rewriting large portions of HTML for your web applications. His first article covers the built-in tags and filters provided by the framework. The second one dives into how customize and implement your own filters and tags. Christopher also talks about his process for choosing topics for articles and courses.

We start the episode by covering a recent article by CPython Developer in Residence, Łukasz Langa. We talked in a previous episode about his plan to study where all the Python core developer activity goes. He’s gathered several years of GitHub data, and we discuss the post.

Course Spotlight: Building With Django REST Framework

This course will get you ready to build with Django REST Framework. The Django REST framework (DRF) is a toolkit built on top of the Django web framework that reduces the amount of code you need to write to create REST interfaces.

Topics:

00:00:00 – Introduction
00:02:03 – Where does all the effort go? Looking at Python core developer activity
00:07:23 – Notes From the Meeting On Python GIL Removal Between Python Core and Sam Gross
00:08:36 – Django background
00:11:28 Flask vs Django
00:14:37 – Django Templates: Built-In Tags and Filters
00:25:26 – Sponsor: Cloudsmith
00:26:12 – Django Templates: Implementing Custom Tags and Filters
00:32:54 – Django Rest Framework - video course
00:42:09 – Video Course Spotlight
00:43:16 – What goes into picking an article to turn into a course?
00:53:18 – What are you excited about in the world of Python?
00:57:00 – Support Django development by donating to the Django Software Foundation
01:00:55 – Thanks and goodbye

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Creating and Manipulating PDFs in Python With borb

Fri, 29 Oct 2021 12:00:00 +0000

Have you wanted to generate PDFs from your Python project? Many of the current libraries require designing the document down at the pixel level. Would you be interested in a tool that lets you specify the page layout while it handles the specific details of laying out the text? This week on the show, we talk with Joris Schellekens about his library for creating and manipulating PDFs named borb.

borb is a pure Python library that can read, write and manipulate PDFs. You can use it to build fillable forms, invoices with attached data files, and multiple column document layouts. We discuss the extensive example repository Joris has created for the library.

Joris shares his background in working with PDFs. He talks about starting the project and the challenges he had to overcome. We also talk about licensing and maintaining an open-source library.

Course Spotlight: Writing Idiomatic Python

Topics:

00:00:00 – Introduction
00:01:58 – Articles about borb
00:03:25 – History of the project
00:07:26 – Background in PDFs and Postscript
00:09:18 – Signatures and other challenges of working in PDFs
00:11:33 – Reading from PDFs and standards of versions
00:14:54 – Features of the library and creating documents
00:18:25 – Creating layout features
00:20:42 – How are fonts handled in borb?
00:21:19 – Sponsor: Cloudsmith
00:22:04 – Why use JSON across the library?
00:22:55 – Embedding data and files within a PDF
00:25:12 – What features were crucial for you to include in borb?
00:28:48 – Why creating a separate examples repository?
00:31:04 – Article series about borb
00:32:25 – Writing a book about borb
00:33:44 – Python 3.10 and borb
00:34:19 – Video Course Spotlight
00:35:39 – Licensing borb and AGPL
00:45:14 – Other open-source projects and Stack Overflow answers
00:46:37 – Working with forms in borb
00:47:55 – Additional tools for working with PDFs
00:50:15 – Different users of the library
00:53:36 – Thoughts on the future of PDFs
00:58:10 – What are you excited about in the world of Python?
00:58:40 – What do you want to learn next?
01:00:25 – Social connection info
01:00:46 – Thanks and goodbye

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Ready to Publish Your Python Packages?

Fri, 22 Oct 2021 12:00:00 +0000

Are you interested in sharing your Python project with the broader world? Would you like to make it easily installable using pip? How do you create Python packages that share your code in a scalable and maintainable way? This week on the show, Real Python author and former guest Dane Hillard returns to talk about his new book, “Publishing Python Packages.”

Dane shares his research into creating Python packages. We talk about the tools, techniques, and potential pitfalls of publishing your packages. Dane also discusses his experiences unraveling projects and determining dependencies.

We also talk about Dane’s recent conference talk at PyGotham titled “Keeping code safe and modern with semantic searches.” He shares some security tools and practices.

Course Spotlight: Documenting Python Code: A Complete Guide

This course will get you up to speed with how to document your Python code. Documenting your code is an important step to help developers and users fully understand its usage and purpose.

Topics:

00:00:00 – Introduction
00:01:57 – PyGotham Talk: Keeping code safe and modern with semantic searches
00:06:24 – What was the driving reason for starting Publishing Pyhton Packages?
00:08:54 – What is CookieCutter?
00:11:53 – The book as a research project
00:15:20 – Sponsor: CData Software
00:16:01 – What is a package?
00:18:56 – What is a published package?
00:25:37 – What other experiences made you want to write this book?
00:28:05 – Where to start?
00:33:26 – Balance of examples and explanation
00:40:49 – Cohesion and studying the relationships between sections of code
00:46:19 – Documentation and tools like Sphinx
00:50:18 – Video Course Spotlight
00:51:30 – What were you most excited to share in the book?
00:54:06 – Additional resources for packaging
00:57:17 – What are you excited about in the world of Python?
00:57:53 – What do you want to learn next?
00:59:30 – Shout outs and plugs
01:00:28 – Thanks and goodbye

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Welcoming the CPython Developer in Residence

Fri, 15 Oct 2021 12:00:00 +0000

Earlier this year, the Python Software Foundation announced the creation of the Developer in Residence role. The first Visionary Sponsors of the PSF have provided funding for this new role for one year. What development responsibilities does this job address? This week on the show, we talk to previous guest Łukasz Langa about becoming the first CPython Developer in Residence.

We talk about how the first months in this role are shaping up. Łukasz discusses the need to address the backlog of open issues and pull requests. He also talks about how he is working to help the project’s volunteers move their contributions forward.

We cover his PyCon 2021 talk about generating real-time FM audio synthesis in Python. He also shares his experience developing a similar synthesis engine for an embedded hardware project.

Course Spotlight: Speech Recognition With Python

In this course, you’ll cover the fundamentals of speech recognition with Python. You’ll learn which speech recognition library gives the best results and build a full-featured “Guess The Word” game with it.

Topics:

00:00:00 – Introduction
00:01:51 – PyCon 2021 talk
00:20:54 – CPython Developer in Residence: Why did you want the role?
00:29:23 – Sponsor: DataStax Astra DB
00:29:55 – What were the expected responsibilities of the role?
00:35:47 – Working through BPO issues and GitHub pull requests
00:42:14 – Moving to migrate the systems
00:51:10 – Video Course Spotlight
00:52:26 – What trends can you see currently?
00:57:28 – Do you feel the amount of activity is increasing due to the role being in place?
01:03:46 – What skills would you look for in a person in this role?
01:07:26 – If there was a team of people how would things be different?
01:11:00 – How to follow along with the journey?
01:13:35 – Gathering and sharing data about the process
01:18:45 – What are you excited about in the world of Python?
01:25:04 – What do you want to learn next?
01:26:41 – Shout outs and plugs
01:31:36 – Thanks and goodbye

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Exploring the New Features of Python 3.10

Fri, 08 Oct 2021 12:00:00 +0000

Python 3.10 is here! This week on the show, two former guests and Real Python authors return to talk about the new version. Geir Arne Hjelle’s article was posted to the site Monday, and it’s titled “Python 3.10: Cool New Features for You to Try”. Christopher Trudeau’s video course came out on Tuesday, and it covers the topics from the article with multiple visual examples of Python 3.10 code.

Geir Arne and Christopher worked together to create code examples of the new features used in both. We talk about more user-friendly error messages, structural pattern matching, enhancements to Python’s type system, and much more.

Geir Arne and Christopher not only cover the new features but they offer advice about ways you might incorporate them into your code. We also discuss what you should think about before running the new version for your projects.

Course Spotlight: Cool New Features in Python 3.10

In this course, you’ll explore some of the coolest and most useful features in Python 3.10. You’ll appreciate more user-friendly error messages, learn about how you can handle complicated data structures with structural pattern matching, and explore new enhancements to Python’s type system.

Topics:

00:00:00 – Introduction
00:02:20 – Better Error Messages
00:07:14 – Structural Pattern Matching
00:14:14 – Sponsor: Snyk
00:14:55 – Type Unions, Aliases, and Guards
00:22:34 – Future Annotations
00:26:46 – Stricter Zipping of Sequences
00:28:30 – New Functions in the statistics Module
00:31:46 – Video Course Spotlight
00:32:50 – Asynchronous Iteration
00:38:59 – Default Text Encodings
00:41:33 – Context Manager Syntax
00:43:19 – Modern and Secure SSL
00:45:10 – Should you upgrade now?
00:49:59 – How to Detect Python 3.10 at Runtime
00:53:07 – Thanks and goodbye

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Make Your Python App Interactive With a Text User Interface (TUI)

Fri, 01 Oct 2021 12:00:00 +0000

Have you wanted to create a Python application that goes further than a command-line interface? You would like it to have a friendly interface but don’t want to make a GUI (Graphical User Interface) or web application. Maybe a TUI (Text User Interface)would be a perfect fit for the project. This week on the show, we have Will McGugan to talk about his projects Textual and Rich.

Rich is a Python library for writing rich text to the terminal with color and style. It’s a great tool if you want to display advanced content such as tables, markdown, and syntax-highlighted code. We talk about how Will started on the project and how it’s developed over the years.

We also talk about Will’s new project Textual, a TUI using much of Rich at its core. He shares how the project is coming along and what are challenges in developing this type of application. We discuss how a TUI has more in common with CSS and web development than command line or graphical interfaces.

We also have a quick announcement at the top of the show from CPython Developer in Residence Łukasz Langa about next week’s release of Python 3.10.

Course Spotlight: Rock, Paper, Scissors With Python: A Command Line Game

Topics:

00:00:00 – Introduction
00:02:08 – Python 3.10 Release Party - Announcement
00:03:32 – Will McGugan and the background of the Rich library
00:10:11 – Moya framework
00:21:38 – Sponsor: DataStax Astra DB
00:22:10 – The spark that started Textual
00:26:31 – Needing AsyncIO for a TUI
00:28:07 – Describing a TUI (Text User Interface)
00:33:57 – Scrolling, resizing, and similarities with CSS
00:36:37 – Video Course Spotlight
00:38:03 – What areas were difficult in developing Textual?
00:39:42 – Similarities to game development
00:41:47 – Testing across different terminals
00:45:01 – What were you excited to include in the project?
00:47:04 – Are there particular uses you foresee for Textual?
00:49:21 – Career changes and open source reviewing
00:54:21 – Version numbers and Textual
00:55:49 – What are you excited about in the world of Python?
00:58:27 – What do you want to learn next?
01:00:57 – Shoutouts and plugs
01:01:47 – Thanks and goodbye

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Measuring Your Python Learning Progress

Fri, 24 Sep 2021 12:00:00 +0000

Where are you along the path of learning Python? Do you feel like you’re making progress? What are ways you can put the learning path into a more precise focus? This week on the show, we talk with previous guest Martin Breuss about his recent article “How Long Does It Take to Learn Python?”

Martin discusses methods for measuring your progress and the various reasons for learning the language. We talk about how different backgrounds will affect your approach. We also suggest resources to help you on your path.

We share a couple of recent Python projects to round out the episode. The first is a library to draw stylized maps from OpenStreetMap data. The other is a framework for the analysis and visualization of trees, which includes a set of phylogenomic tools.

Course Spotlight: Using the Python return Statement Effectively

In this step-by-step course, you’ll learn how to use the Python return statement when writing functions. Additionally, you’ll cover some good programming practices related to the use of return. With this knowledge, you’ll be able to write readable, robust, and maintainable functions in Python.

Topics:

00:00:00 – Introduction
00:01:44 – Real Python Core Team Member
00:03:13 – How long does it take to learn Python?
00:03:40 – Why did you want to explore this topic?
00:05:05 – Python backgrounds Martin and Christopher
00:16:39 – What area excited you?
00:19:38 – Sponsor: Rev AI
00:20:16 – What are other areas that define, why Python?
00:23:30 – Keeping the scope narrower
00:24:48 – Measuring your progress
00:32:49 – Video Course Spotlight
00:34:12 – Effective mentorship
00:39:02 – Using search engines
00:43:07 – The journey of learning Python
00:48:38 – Programming challenges and practicing
00:49:40 – pretty maps - Minimal Python library to draw customized maps
00:52:02 – ETE Toolkit - Python environment for tree exploration
00:54:30 – Thanks and goodbye

Show Links:

Projects:

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Learning Python Through Illustrated Stories

Fri, 17 Sep 2021 12:00:00 +0000

Are you a visual learner? Does it help to have programming concepts shared with concrete examples and images? Would you like to see if your child might be interested in programming? This week on the show, we talk with author Shari Eskenas about her books, “A Day in Code - Python: Learn to Code in Python Through an Illustrated Story” and “Learn Python Through Nursery Rhymes & Fairy Tales.”

We talk about the books and what inspired her to bring programming to picture books. Shari discusses her goal of providing a fun way for beginners to experience learning to code.

Shari is also an electrical engineer with multiple patents and the founder of Sundae Electronics. We talk briefly about SoundBrake, which is an audio device that alerts headphone users to outside sounds.

Shari’s programming background is primarily in C, and we cover her path to Python. We also discuss how she is using Python and the Raspberry Pi to prototype new projects.

Course Spotlight: Reading and Writing Files With Pandas

In this course, you’ll learn about the Pandas IO tools API and how you can use it to read and write files. You’ll use the Pandas read_csv() function to work with CSV files. You’ll also cover similar methods for efficiently working with Excel, CSV, JSON, HTML, SQL, pickle, and big data files.

Topics:

00:00:00 – Introduction
00:02:05 – A Day in Code Books
00:05:32 – Showing Python classes and object oriented concepts
00:08:02 – What Python concepts did you want to cover in the book?
00:09:17 – Translating the book and concepts from C to Python
00:11:04 – Reception of the book
00:12:10 – Using real objects and names in code
00:14:05 – Sponsor: DataStax Astra DB
00:14:38 – Learn Python Through Nursery Rhymes & Fairy Tales
00:20:25 – Sundae Electronics and SoundBrake
00:26:47 – Video Course Spotlight
00:27:57 – Prototyping with Python and Raspberry Pi
00:38:19 – When did you start to learn Python?
00:39:31 – Real Python community
00:41:33 – How do you see yourself using Python in the future?
00:43:39 – What are you excited about in the world of Python?
00:45:02 – What do you want to learn next?
00:45:51 – Shout outs and plugs: Kickstarter campaign
00:46:40 – Social media connections
00:47:08 – Thanks and goodbye

Show Links:

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Advantages of Completing Small Python Projects

Fri, 10 Sep 2021 12:00:00 +0000

Are you a beginner or intermediate Python programmer who has made it through some of the fundamentals? Have you tried to tackle a big project but got stuck and frustrated? Completing some small projects might be the answer. This week on the show, we have author Al Sweigart and talk about his new book, “The Big Book of Small Python Projects.”

We discuss the advantages of sometimes thinking small in terms of Python programs. We talk about completing projects and the benefits of manually copying code by typing it in yourself. Al also has suggestions about tools for beginners and intermediate developers.

Course Spotlight: Grow Your Python Portfolio With 13 Intermediate Project Ideas

Get started on 13 Python project ideas that are just right for intermediate Python developers. They’ll challenge you enough to help you become a better Pythonista.

Topics:

00:00:00 – Introduction
00:01:53 – Writing books and PyCascades 2021 Author panel
00:06:58 – Did you type in code from books as beginner?
00:10:16 – About the book
00:20:53 – Sponsor: Rev AI
00:21:30 – General instructions on how to start with the book
00:27:39 – Working with a debugger
00:30:28 – Experimenting with existing code
00:34:27 – Tools for learning touch typing
00:38:31 – Video Course Spotlight
00:39:37 – IDEs, Mu, and Python’s IDLE
00:45:42 – Online diff tool
00:47:27 – Some of the projects, games, animations
00:51:58 – Finishing things
00:54:07 – What are areas of Python you see beginners struggle with?
00:59:41 – What is something you wish you were shown as a beginner?
01:02:07 – What are you excited about in the world of Python?
01:04:34 – What do you want to learn next?
01:07:27 – Shout outs and social connections
01:08:33 – Thanks and goodbye

Show Links:

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Harnessing Python's math Module and Exposing Practical Pandas Functions

Fri, 03 Sep 2021 12:00:00 +0000

How well do you know Python’s math module? Maybe you’ve used a few of the constants or arithmetic functions. You may be surprised by the amount of functionality hiding within this built-in library, and perhaps you don’t need to reach for an additional outside library. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss a recent video course about the math module. David shares a recent article about implementing efficient queues and stacks with Python’s deque (double-ended queue) class. We also talk about an article that shares 25 Pandas functions you may not have known to exist.

We cover several other articles and projects from the Python community including, visualization and interactive dashboards in Python with HoloViz, designing a camera with Python and PyRayT, graphing data science with Python and networkx, another useful Python pdf library, and runtime software verification and automated testing for scientific software in Python with a project named paranoidscientist.

Course Spotlight: Exploring the Python math Module

In this step-by-step course, you’ll learn all about Python’s math module for higher-level mathematical functions. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math!

Topics:

00:00:00 – Introduction
00:02:09 – Python’s deque: Implement Efficient Queues and Stacks
00:07:31 – Visualization and Interactive Dashboard in Python
00:14:11 – Sponsor: DataStax Astra DB
00:14:42 – Design a Camera with Python and PyRayT
00:19:39 – 25 Pandas Functions You Didn’t Know Existed
00:27:03 – Graph Data Science With Python and NetworkX
00:33:01 – Exploring the Python math Module
00:40:51 – Video Course Spotlight
00:42:12 – paranoidscientist: Runtime Software Verification and Automated Testing for Scientific Software in Python
00:45:33 – borb: A Python PDF library
00:49:01 – Thanks and goodbye

Show Links:

Python’s deque: Implement Efficient Queues and Stacks – In this step-by-step tutorial, you’ll learn about Python’s deque and how to use it to perform efficient pop and append operations on both ends of your sequences. Deques are commonly used to build queues and stacks.

Visualization and Interactive Dashboard in Python – Have you ever heard of HoloViz? It’s a set of Python visualization and plotting tools for browser-based data visualization and presentation. In this article, Sophia Yang — a senior data scientist at Anaconda — explains why she loves HoloViz and what her workflow looks like when using it.

Design a Camera with Python and PyRayT – Camera lenses are more complex than you might think. If you cut open a camera to look at a cross-section, you’d see that a lens is actually made up of several smaller lenses. This article explores how camera lenses work using a new Python raytracing project called PyRayT that’s designed for designing optical systems.

25 Pandas Functions You Didn’t Know Existed – Did you know that you can style the pandas DataFrame output in a notebook? This listicle covers twenty-five pandas functions that you may not have heard of, including .explode(), .squeeze(), and the pandas DataFrame styler. There’s something in this article for everyone, so read it to find out how you can take your pandas skills to the next level.

Graph Data Science With Python and NetworkX – Graph theory network analysis can yield deep data insights that are difficult to tease out via alternative methods. Get a taste of what graph analysis can do in this quick, hands-on tutorial that uses NetworkX to create and analyze a graph.

Exploring the Python math Module – In this step-by-step course, you’ll learn all about Python’s math module for higher-level mathematical functions. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math!

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Building With CircuitPython & Constraints of Python for Microcontrollers

Fri, 27 Aug 2021 12:00:00 +0000

Can you make a version of Python that fits within the memory constraints of a microcontroller and have it still feel like Python? That is the intention behind CircuitPython. This week on the show, we have Scott Shawcroft, who is the project lead for CircuitPython.

We talk about all things CircuitPython. While working with the language on several projects I have developed many of my own questions to ask Scott. Scott answers my questions about boot loaders, packages, the bundle, and bluetooth low energy (BLE).

He also talks about the struggle of fitting the language and board specific libraries within tiny memory constraints. We discuss projects and boards for beginners, and many resources to learn more.

Course Spotlight: Getting Started With MicroPython

Are you interested in the Internet of Things, home automation, and connected devices? If so, then you’re in luck! In this course, you’ll learn about MicroPython and the world of electronics hardware. You’ll set up your board, write your code, and deploy a MicroPython project to your own device.

Topics:

00:00:00 – Introduction
00:01:56 – Background With CircuitPython
00:07:22 – Lightning talk and CircuitPython as a subset of Python
00:16:01 – Working with CircuitPython: Bootloaders and Packages
00:27:20 – Specific libraries, adding modules, and pip
00:37:21 – Sponsor: Rev.ai
00:37:57 – How to program, text editor or IDE
00:41:19 – Bluetooth Low Energy (BLE) and programming devices remotely
00:50:02 – Video Course Spotlight
00:51:06 – Do you consider yourself a Maker?
00:54:58 – Bringing CircuitPython coding to the Raspberry Pi?
00:59:56 – Suggestions for beginner hardware
01:00:55 – Suggestions for add-on boards for audio or displays
01:10:20 – Electronics Show and Tell Wednesdays
01:11:51 – Events, video streams, and Discord server
01:16:00 – What are you excited about in the world of Python?
01:17:23 – What do you want to learn next?
01:18:42 – Shoutouts and plugs
01:22:40 – Social media info
01:23:52 – Thanks and goodbye

Show Links:

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Python's Assignment Expressions and Fixing a Botched Release to PyPI

Fri, 20 Aug 2021 12:00:00 +0000

Have you started to use Python’s assignment expression in your code? Maybe you have heard them called the walrus operator. Now that the controversy over the introduction in Python 3.8 has settled down, how can you use assignment expressions effectively in your code? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares a recent article by previous guest Brett Cannon about what to do if you botch a release to PyPI. It’s a valuable resource to keep bookmarked for when things go sideways. We also talk about a recent project by Brett, a Python launcher for Unix based operating systems.

We cover several other articles and projects from the Python community including, a Python framework with a built-in database and authorization support from Replit, do coders learn how to use entire libraries just from the documentation, how to use sleep() to code a Python uptime bot, monitor your home’s temperature and humidity with Raspberry Pis and Prometheus, and a fast Settlers of Catan Python implementation with a strong AI player.

Course Spotlight: Using sleep() to Code a Python Uptime Bot

In this course, you’ll learn how to add time delays to your Python programs. You’ll use the built-in time module to add Python sleep() calls to your code. To practice, you’ll use time.sleep() when making an uptime bot that checks whether a website is still live.

Topics:

00:00:00 – Introduction
00:02:43 – The Walrus Operator: Python 3.8 Assignment Expressions
00:12:41 – Replit.web: Python Framework With Built-in Database and Auth
00:16:55 – Sponsor: Gather
00:17:39 – Do Coders Learn How to Use Entire Libraries Just From the Docs?
00:26:15 – Using sleep() to Code a Python Uptime Bot
00:31:53 – Monitor Home Temperature and Humidity With Raspberry Pis and Prometheus
00:39:51 – Video Course Spotlight
00:40:57 – What to Do When You Botch a Release on PyPI
00:47:25 – catanatron: Fast Settlers of Catan Python Implementation
00:50:37 – python-launcher: Python Launcher for Unix
00:56:59 – Thanks and goodbye

Show Links:

The Walrus Operator: Python 3.8 Assignment Expressions – In this tutorial, you’ll learn about assignment expressions and the walrus operator. The biggest change in Python 3.8 was the inclusion of the := operator, which you can use to assign variables in the middle of expressions. You’ll see several examples of how to take advantage of this new feature.

Replit.web: A Python Framework With Built-in Database and Auth Support – The folks over at replit have released a new Python web framework with built-in authentication and database support and, more interestingly, hosting. In a few lines of code, you can have a Python web app connected to a database, authenticating users, and hosted on replit. This could be a great tool for quickly building and hosting prototypes or experimental projects.

Do Coders Really Learn How to Use Entire Libraries Just From the Documentation? – How do you learn a new library? Do you start with the docs? What do you do if the documentation is lacking? Or do you first search for video lessons or written tutorials?

Using sleep() to Code a Python Uptime Bot – Learn how to add time delays to your Python programs. You’ll use the built-in time module to add Python sleep() calls to your code. To practice, you’ll use time.sleep() when making an uptime bot that checks whether a website is still live.

Monitor Your Home’s Temperature and Humidity With Raspberry Pis and Prometheus – Do you enjoy collecting and analyzing data, or are you looking for a fun project to improve your data skills? Do you also enjoy tinkering with hardware? Then this project might be a fun one for you to check out! Learn how to set up a RaspberryPi using Prometheus to collect and monitor temperature sensor data.

What to Do When You Botch a Release on PyPI – Mistakes happen to everyone. But what do you do if you make a mistake when releasing a package to PyPI? Don’t panic! There are a number of things you can do to fix a bad release. This article walks you through several scenarios and suggested solutions.

Projects:

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Supporting Python Open Source Projects and Maintainers

Fri, 13 Aug 2021 12:00:00 +0000

How do you define open source software? What are the challenges an open source project and maintainers face? How do maintainers receive financial, legal, security, or other types of help? This week on the show, we have Josh Simmons from Tidelift and the Open Source Initiative to help answer these questions.

Josh does open source ecosystem strategy for Tidelift. We talk about what Tidelift is and how they support maintainers. We also discuss the types of support maintainers need and the barriers that can block that support.

Josh also serves as President of the Open Source Initiative. OSI is actively involved in open source community-building and education. He talks about how collectives and foundations can be powerful tools in the open source ecosystem.

Course Spotlight: Python Inner Functions

Topics:

00:00:00 – Introduction
00:01:49 – What is Tidelift?
00:03:41 – What is your role?
00:05:28 – What is a lifter?
00:06:50 – How much software used by businesses is open-source?
00:10:26 – Comparing risks of proprietary, open-source, and commercial software
00:18:52 – How to define a project as open-source
00:30:17 – Defining rights and freedoms of open-source
00:32:39 – Sponsor: Sentry
00:33:43 – Obstacles in the way for contributions
00:42:51 – Other types of support open-source contributors need
00:46:55 – A goal to work with foundations and individual maintainers
00:49:54 – Benefits of improved documentation, a force multiplier
00:51:26 – Video Course Spotlight
00:52:37 – What makes a managed open-source project?
00:54:58 – What are you excited about in the world of Python?
00:55:51 – What do you want to learn next?
00:57:10 – Shoutout and call to action
00:59:27 – Social media information
00:59:46 – Thanks and goodbye

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Starting With FastAPI and Examining Python's Import System

Fri, 06 Aug 2021 12:00:00 +0000

Have you heard of FastAPI? An application programming interface is vital to make your software accessible to users across the internet. FastAPI is an excellent option for quickly creating a web API that implements best practices. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We share an introduction to FastAPI written by the framework’s author, Sebastián Ramírez. The goal behind the article is to get you started creating production-ready APIs.

David covers an article about the Python import system and how it remains a mystery for many Python developers. We share some additional Real Python resources on the import system and statements.

We cover several other articles and projects from the Python community including, a buffet of specialized data types with Python’s collections module, maps with Django using GeoDjango, PostGIS, and Leaflet, moving SciPy to the Meson build system, what’s new in Python 3.11, a community-maintained Python framework for creating mathematical animations, and easily make PDFs with pdfme.

Course Spotlight: Python Modules and Packages: An Introduction

In this course, you’ll explore Python modules and Python packages, two mechanisms that facilitate modular programming. See how to write and import modules so you can optimize the structure of your own programs and make them more maintainable.

Topics:

00:00:00 – Introduction
00:02:18 – Python’s collections: A Buffet of Specialized Data Types
00:08:07 – Maps With Django: GeoDjango, PostGIS, and Leaflet
00:12:12 – Moving SciPy to the Meson Build System
00:18:16 – Sponsor: Sentry
00:19:18 – What’s New In Python 3.11
00:24:32 – Behind the Scenes: How the Python Import System Works
00:31:34 – Video Course Spotlight
00:32:40 – Using FastAPI to Build Python Web APIs
00:38:42 – manim: A Community-Maintained Python Framework for Creating Mathematical Animations
00:41:56 – pdfme: Make PDFs Easily
00:44:42 – Thanks and goodbye

Show Links:

Python’s collections: A Buffet of Specialized Data Types – Python has a number of useful data types beyond the built-in lists, tuples, dicts, and sets. In this tutorial, you’ll learn all about the series of specialized container data types in the collections module from the Python standard library. Learning the collections module is a great way to level up your Python programming knowledge!

Maps With Django: GeoDjango, PostGIS, and Leaflet – This quickstart guide shows you how to create a web map using Django’s GeoDjango module. Data for the map is stored in a PostgreSQL database using the PostGIS extension, and Leaflet, a lightweight JavaScript library for interactive maps, is used on the front-end. You’ll not only learn how to set up the Django application and display the map but also add markers to the map and automatically center the map on the application user’s location.

Moving SciPy to the Meson Build System – In accordance with PEP 632, distutils will be deprecated in Python 3.10 and in Python 3.12 it will be removed. This posed a big problem for SciPy, since it’s build system depends on NumPy’s distutils module — an extension of Python’s built-in distutils. The SciPy maintainers set out to find a new build system and settled on Meson, which solves a number of build issues and even scores a 4x speed-up on build times!

What’s New In Python 3.11 – Python 3.10 is still in beta, but work on Python 3.11 has already begun. Big changes include some major improvements to tracebacks as well as a new cube root function in the math module.

Behind the Scenes: How the Python Import System Works – Importing a Python module is probably one of the most used language features. But Python’s import system remains a mystery to many Python developers, even folks with years of experience. This in-depth article explores how the import system works from the top down. You’ll learn everything from the difference is between absolute and relative imports to how Python searches for modules and packages and resolves naming conflicts.

Using FastAPI to Build Python Web APIs – In this guide, written by FastAPI creator Sebastián Ramírez, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.

Projects:

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Start Using a Debugger With Your Python Code

Fri, 30 Jul 2021 12:00:00 +0000

Are you still sprinkling print statements throughout your code while writing it? Print statements are often clunky and offer only a limited view of the state of your code. Have you thought there must be a better way? This week on the show, we have Nina Zakharenko to discuss her conference talk titled “Goodbye Print, Hello Debugger.”

We talk about how to get started debugging your code by adding a single keyword. Nina discusses the differences between debugging on the command line vs using the tools included with an Integrated Development Environment(IDE). She also shares tricks and best practices. If you haven’t seen Nina’s conference talk, it’s a great starting point.

We also talk about working through the last 18 months and how to recharge your creative batteries. We briefly discuss two other presentations Nina gave about CircuitPython and getting started with electronics and Python.

Course Spotlight: Python Debugging With pdb

Topics:

00:00:00 – Introduction
00:01:46 – Follow up on PyCascades 2021 and 2022
00:02:45 – Election to the PSF Board
00:05:06 – Current levels of burnout
00:09:09 – Possible ways to recharge
00:12:18 – Goodbye Print, Hello Debugger
00:14:46 – What are the types of things you can examine the state of?
00:18:01 – Where to start?
00:22:37 – Sponsor: Sentry
00:23:39 – Why is debugging not shown as often in Python?
00:26:49 – Debugging in VSCode
00:29:37 – Adding breakpoints in Flask or Django Templates
00:31:32 – What is pdb and how does it compare to ipdb?
00:39:40 – What are common debugging mistakes?
00:43:50 – Video Course Spotlight
00:44:48 – Why include a disclaimer about the talk?
00:46:13 – Where did you learn about debugging?
00:49:23 – Nina’s conference talks about CircuitPython
00:58:33 – What are you excited about in the world of Python?
00:59:51 – What do you want to learn next?
01:04:02 – Social info
01:04:27 – Thanks and goodbye

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What Can You Do With Python and Counting Objects Using "Counter"

Fri, 23 Jul 2021 12:00:00 +0000

How is Python being used today, and what can you do with the language? Do you want to develop software, dive into data science and math, automate parts of your job and digital life, or work with electronics? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python article that covers the incredible variety of ways you can use Python. David shares an article about the pythonic way to count objects using the Counter class from the collections module. We discuss the ways it can lead to cleaner and more efficient code.

We cover several other articles and projects from the Python community including, the inaugural CPython developer-in-residence, typeclasses in Python, GitHub Copilot writes a text-based game, friendlier tracebacks in REPLs (including Jupyter), 120+ interactive interview challenges, a module that helps you build complex pipelines of batch jobs, and a tool for plotting in the terminal.

Course Spotlight: Understanding Python List Comprehensions

Python list comprehensions make it easy to create lists while performing sophisticated filtering, mapping, and conditional logic on their members. In this course, you’ll learn when to use a list comprehension in Python and how to create them effectively.

Topics:

00:00:00 – Introduction
00:02:01 – Łukasz Langa Is the Inaugural CPython Developer-in-Residence
00:05:03 – What Can I Do With Python?
00:11:19 – Typeclasses in Python
00:17:59 – Sponsor: Sentry
00:19:01 – Copilot Writes a Text-Based Game in Python
00:27:31 – Python’s Counter: The Pythonic Way to Count Objects
00:34:14 – interactive-coding-challenges: 120+ Interactive Python Coding Interview Challenges With Anki Flashcards
00:38:11 – Video Course Spotlight
00:39:11 – Friendlier Tracebacks in REPLs (Including Jupyter)
00:49:37 – luigi: Python Module That Helps You Build Complex Pipelines of Batch Jobs
00:52:13 – plotext: Plotting in the Terminal
00:55:24 – Thanks and goodbye

Show Links:

Łukasz Langa Is the Inaugural CPython Developer-in-Residence

What Can I Do With Python? – You’ve finished a course or finally made it to the end of a book that teaches you the basics of programming with Python. You’ve learned about variables, lists, tuples, dictionaries, for and while loops, conditional statements, object-oriented concepts, and more. So, what’s next? What can you do with Python nowadays?

Typeclasses in Python – Sometimes you need to change the behavior of a function based on the type of argument passed to it. This is a classic example of polymorphism in programming. In this article, you’ll learn how this is typically done in Python, compare that to polymorphism in other languages, and see how the new classes library can make the whole process easier.

Copilot Writes a Text-Based Game in Python – GitHub’s new Copilot feature has a lot of people talking. The project’s goal is to be an AI pair programmer — a tool that can suggest entire lines of code or even entire functions! In this amusing article, one developer who gained access to Copilot’s technical preview shares how the AI wrote an entire text-based adventure game that turned out to be the solution to an exercise from a Python instructional book.

Python’s Counter: The Pythonic Way to Count Objects– In this step-by-step tutorial, you’ll learn how to use Python’s Counter to count several repeated objects at once. You’ll also learn how to use Counter objects to enhance other computations that you do in Python.

interactive-coding-challenges: 120+ Interactive Python Coding Interview Challenges With Anki Flashcards

Friendlier Tracebacks in REPLs (Including Jupyter) – Tracebacks are often the start of any Python debugging journey. But tracebacks can be difficult to read and confusing to beginners. The friendly-traceback project aims to lift the veil of confusion for tracebacks by providing more helpful error messages with lots of context. New updates to the project include better tracebacks in Jupyter Notebooks!

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Planning a Faster Future at the Python Language Summit

Fri, 16 Jul 2021 12:00:00 +0000

Do you wonder what the future may hold for the Python language? Are there speed improvements coming soon? What if you could be in the room while the core developers discuss Python’s future? This week on the show, we have Joanna Jablonski, who was invited to the Python Language Summit 2021 as a journalist to summarize and document the event.

A small group of core developers from the Python community gather to work toward a healthy future for the language. Through presentations and group discussions, they share insights, ideas, and potential problems.

Joanna has been the executive editor at Real Python and she was invited to write a series of blog posts for the Python Software Foundation (PSF) that summarize the presentations and deeper conversations of the summit. We walk through the two days and discuss the topics covered.

Several of the presentations focused on performance and speeding up CPython. There were conversations about packaging, documentation, the standard library, handling exceptions, testing, and more.

Course Spotlight: Defining and Calling Python Functions

In this course, you’ll learn how to define and call your own Python function. You’ll also learn about passing data to your function and returning data from your function back to its calling environment.

Topics:

00:00:00 – Introduction
00:01:58 – What is the Python Language Summit?
00:06:44 – How do you summarize the talks?
00:08:17 – Terminology and technological level of the conversations
00:13:02 – PEP 654 — Exception Groups and except*
00:16:10 – Sponsor: Sentry
00:17:12 – Progress on Running Multiple Python Interpreters in Parallel in the Same Process
00:18:15 – CPython Performance Improvements at Instagram
00:20:41 – Making CPython Faster
00:23:22 – HPy — Present and Future
00:25:07 – Lightning Talks, Round 1
00:31:03 – Video Course Spotlight
00:32:34 – The Challenges of Packaging Python for a Linux Distro
00:36:12 – The Python Documentation Work Group
00:39:11 – What Is the stdlib?
00:42:07 – What Should I Work on as a Core Dev?
00:44:34 – Fuzzing and Testing Python With Properties
00:46:00 – Lightning Talks, Round 2
00:53:41 – What are you excited about in the world of Python?
00:54:46 – What do you want to learn next?
00:55:57 – Shout out and social connections
00:56:57 – Thanks and goodbye

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Exploring the functools Module and Complex Numbers in Python

Fri, 09 Jul 2021 12:00:00 +0000

Are you ready to expand your Python knowledge into the intermediate to advanced territory? What tools are awaiting your discovery inside Python’s functools module? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article about the functools module, which adds functionality for caching, function overloading, better definitions for decorated functions, and more. David talks about a new Real Python article about working with complex numbers in Python. We also cover a tutorial about troubleshooting memory problems in Python.

We cover several other articles and projects from the Python community including, DevOps interview questions, correlation analysis in Python, pivot and plot data with pandas, how to use Python and OpenCV to play online chess with a real chessboard, and generating hardware pinout diagrams as SVG images.

Course Spotlight: Python Decorators 101

Topics:

00:00:00 – Introduction
00:01:56 – The Future of FastAPI and Pydantic Is Bright
00:04:33 – Simplify Complex Numbers With Python
00:10:37 – Functools: The Power of Higher-Order Functions in Python
00:18:40 – Sponsor: Sentry
00:19:42 – How to Pivot and Plot Data With Pandas
00:23:06 – devops-exercises: DevOps Interview Questions
00:32:09 – Video Course Spotlight
00:33:25 – Correlation Analysis 101 in Python
00:39:28 – How to Troubleshoot Memory Problems in Python
00:46:16 – Use Python and OpenCV to Play Online Chess With a Real Chessboard
00:49:28 – pinout: Generate Hardware Pinout Diagrams as SVG Images
00:54:21 – Thanks and goodbyes

Show Links:

The Future of FastAPI and Pydantic Is Bright – Not long ago there was some chatter on the internet about a change in Python 3.10 that would impact Python projects that check types at runtime. The discussion centered around FastAPI and Pydantic and had some folks worried about the future of those projects. In this article, FastAPI’s creator explains what the discussion was all about and why the future of FastAPI and Pydantic remains bright.

Simplify Complex Numbers With Python – In this tutorial, you’ll learn about the unique treatment of complex numbers in Python. Complex numbers are a convenient tool for solving scientific and engineering problems. You’ll experience the elegance of using complex numbers in Python with several hands-on examples.

Functools: The Power of Higher-Order Functions in Python – The functools module is one of the “hidden gems” of the Python standard library. This article takes you on a tour of everything in functools. You’ll learn how to implement caching, function overloading, and a whole lot more.

How to Pivot and Plot Data With Pandas – One of the challenges of working with data is knowing how to manipulate the data format for a particular analysis. And there’s no single correct format. You need to know how to melt, pivot, and transpose data into a format that fits whatever you’re analyzing. If you enjoy this article, be sure to also check out Stefanie’s Pandas Workshop.

devops-exercises: DevOps Interview Questions

Correlation Analysis 101 in Python – Correlation analysis is a useful part of exploratory data analysis. It can help you identify potential relationships between various features of your data. In this helpful guide, you’ll learn how to do correlation analysis in a pandas DataFrame. You’ll see how to display a correlation matrix as a heatmap and explore some guidelines for identifying when correlation might imply causation.

How to Troubleshoot Memory Problems in Python – Memory problems can be frustrating. They’re hard to diagnose and fix, and memory issues in Python applications can be especially frustrating thanks to the language’s garbage collection system. In this article, you’ll learn a six-step process for troubleshooting memory problems that the EvalML team used to solve a tricky problem with their library.

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Securing Your Python Software Supply Chain With Dustin Ingram

Fri, 02 Jul 2021 12:00:00 +0000

How well do you know your software supply chain? When you PIP install a package, what steps can you take to minimize the risk of installing something malicious? This week on the show, we have Dustin Ingram, a director of the Python Software Foundation (PSF) and a maintainer of the Python Package Index (PyPI).

We talk about Dustin’s PyCon 2021 talk titled “Secure Software Supply Chains for Python”. Dustin shares the types of attacks you should be aware of and how you can make your supply chain more trustworthy. We cover tools, techniques, and best practices.

Dustin also discusses what it takes to keep the Python Package Index running and the players working to keep it going into the future.

Course Spotlight: A Beginner’s Guide to Pip

This course is a great introduction to pip for those who are getting started Python, and for those who want to understand more about what is happening when you install new packages into your environment. It’s a worthy investment of your time to understand the fundamentals of pip.

Topics:

00:00:00 – Introduction
00:01:51 – Developer Advocate at Google
00:04:34 – A director of the PSF
00:06:27 – A maintainer of PyPI
00:12:29 – Secure Software Supply Chains for Python - PyCon 2021
00:15:53 – Do I need to be a security expert as a Python developer?
00:17:23 – Typo-squatting of package names
00:19:46 – Sponsor: Scout APM
00:20:52 – Dependency confusion and private repos
00:26:00 – What are some best practices?
00:31:55 – How to lessen the scale of “I don’t know what I don’t know”?
00:36:33 – Tools and techniques that can help
00:44:11 – Video Course Spotlight
00:45:30 – Namespaces on PyPI
00:53:03 – What does it take to power the Python Package Index?
01:01:57 – What are you excited about in the world of Python?
01:03:55 – What do you want to learn next?
01:05:52 – What is something you thought you knew about Python, but were wrong about it?
01:08:46 – Shout outs and social information
01:10:16 – Thanks and goodbye

Show Links:

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Support the podcast & join our community of Pythonistas

Practicing Python With CSV Files and Extracting Values With "filter()"

Fri, 25 Jun 2021 12:00:00 +0000

Are you ready to practice your Python skills some more? There is a new set of practice problems prepared for you to tackle, and this time they’re based on working with CSV files. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares an article about functional programming with a focus on the “filter” function. The tutorial covers how to process an iterable and extract the items that satisfy a given condition. It also covers combining filter with other functional tools, and compares it to coding with Pythonic tools like list comprehensions and generator expressions.

We cover several other articles and projects from the Python community including, Excel, Python, and the future of data science, a Bayesian analysis of Lego prices in Python, why can’t comments appear after a line continuation character, teaching Python on the Raspberry Pi400 at the public library, a cross-platform editor designed for writing novels built with Python and Qt, and a text user interface with rich as the renderer.

Spotlight: Python vs JavaScript for Python Developers

Topics:

00:00:00 – Introduction
00:02:29 – Excel, Python, and the Future of Data Science
00:07:50 – Python Practice Problems: Parsing CSV Files
00:17:09 – Sponsor: Digital Ocean’s App Platform
00:17:45 – A Bayesian Analysis of Lego Prices in Python With PyMC3
00:23:02 – Why Can’t Comments Appear After a Line Continuation Character?
00:28:40 – Python’s filter(): Extract Values From Iterables
00:34:57 – Video Course Spotlight
00:36:24 – How I Teach Python on the Raspberry Pi 400 at the Public Library
00:46:23 – novelWriter: Cross-Platform Editor Designed for Writing Novels Built With Python and Qt
00:48:02 – textual: A Text User Interface With Rich as the Renderer
00:54:58 – Thanks and goodbye

Show Links:

Excel, Python, and the Future of Data Science – What’s the most widely used tool in data science? Is it pandas or NumPy? Is it the Python language itself? Not really. It’s Excel. You might argue that data scientists aren’t using Excel as their primary tool, and you might be right. But Excel enables non-technical users, like small business owners, to gain insights into their data. In this article, Anaconda CEO Peter Wang discusses his goal of making Python and PyData the “conceptual successor” to Excel.

Python Practice Problems: Parsing CSV Files – In this tutorial, you’ll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You’ll work through the problems yourself and then compare your results with solutions developed by the Real Python team.

A Bayesian Analysis of Lego Prices in Python With PyMC3 – Follow along with this in-depth analysis of LEGO prices to see Bayesian analysis in action. Along the way, you’ll how pooled and unpooled linear models can be used to determine if a LEGO set is fairly priced. The article is quite technical, so experience with Bayesian statistics is recommended.

Why Can’t Comments Appear After a Line Continuation Character? – Chaining together many object methods can create long tines that break the PEP 8 79-character line length recommendation. You can use \ to break the chain of methods onto individual lines, but if you want to leave comments at the end of some of the lines, you’re out of luck. There’s another pattern, though, that solves this.

Python’s filter(): Extract Values From Iterables – In this step-by-step tutorial, you’ll learn how Python’s filter() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace filter() and make your code more Pythonic.

How I Teach Python on the Raspberry Pi 400 at the Public Library – Community-based programming courses are a great way to introduce folks to computer programming that otherwise may not have the means to do so. One of the barriers to learning to code is cost. You need a computer to program on, after all. But with the advent of tiny computers like the Raspberry Pi, computers aimed at education are more affordable than ever.

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Expanding the International Python Community With the PSF

Fri, 18 Jun 2021 12:00:00 +0000

The popularity of Python is continuing to grow Developers across the globe are embracing the language. How is Python being used in all of these different countries? How does an organization like the Python Software Foundation (PSF) work toward the goals in its mission statement for supporting and growing this international community? This week on the show, we have Marlene Mhangami, a PSF board member and part of the Diversity and Inclusion Work Group.

Marlene lives in Zimbabwe on the continent of Africa. She has been organizing events not only locally in Zimbabwe but across all of Africa. She is the chair of Pycon Africa and has given talks at Pycon US, Pycon UK, and Pycon India.

She has been working locally as an organizer and educator. We talk about the challenges of teaching technology and programming to a population of young people. Some of these students don’t have access to computers.

She is also currently pursuing a computer science degree with the University of London. Along with her studies, she is also interning with NVidia. She is working with them on the RAPIDS project with a focus on the cuDF library.

Spotlight: Introduction to Sorting Algorithms in Python

In this course, you’ll learn all about five different sorting algorithms in Python from both a theoretical and a practical standpoint. You’ll also learn several related and important concepts, including Big O notation and recursion.

Topics:

00:00:00 – Introduction
00:02:05 – Connecting during PyCon 2021
00:03:08 – Roles with the Python Software Foundation (PSF)
00:05:39 – Python in Africa
00:10:31 – School overseas and return to Zimbabwe to build a Python community
00:13:47 – Teaching technology and Python to students who don’t have computers
00:22:14 – Sponsor: Digital Ocean’s App Platform
00:22:50 – Work with the PSF and building geographic diversity
00:27:49 – PSF work groups
00:32:10 – Organizing PyCon Africa 2019 and bringing a continent of communities together
00:35:51 – How is Python being used in Africa?
00:38:05 – Video Course Spotlight
00:39:24 – Working with NVidia RAPIDS and cuDF
00:43:57 – What are you excited about in the world of Python?
00:46:12 – What do you want to learn next?
00:52:45 – What is something you thought you knew about Python but were wrong about it?
00:59:41 – Thanks and goodbye

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Detecting Deforestation With Python & Using GraphQL With Django and Vue

Fri, 11 Jun 2021 12:00:00 +0000

Are you looking for an in-depth data science project to practice your skills on? Perhaps you would like to add new tools to your Python web development projects instead? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares an article about how to go about detecting deforestation from satellite images. He covers how a data science team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard.

We also discuss a Real Python article about building a blog using Django, Vue.js, and GraphQL. GraphQL is a great tool to enhance your API to make it more flexible. The step by step project walks you through turning your Django blog data models into a GraphQL API.

We cover several other articles and projects from the Python community including, the tools and tech used to run a one-woman hardware company, visualizing data in Python using plt.scatter(), why the sad face when using Black, how to iterate over dataframe rows (and should you?), pipx is now a PyPA member project, and real-time lossless audio compression in Python with pyFLAC.

Spotlight: Explore Your Dataset With Pandas

In this step-by-step course, you’ll learn how to start exploring a dataset with Pandas and Python. You’ll learn how to access specific rows and columns to answer questions about your data. You’ll also see how to handle missing values and prepare to visualize your dataset in a Jupyter Notebook.

Topics:

00:00:00 – Introduction
00:02:11 – Build a Blog Using Django, Vue, and GraphQL
00:10:06 – Detecting Deforestation From Satellite Images
00:16:35 – Sponsor: Digital Ocean’s App Platform
00:17:11 – The Tools and Tech I Use to Run a One-Woman Hardware Company
00:29:13 – Visualizing Data in Python Using plt.scatter()
00:34:24 – Why the Sad Face?
00:40:20 – Video Course Spotlight
00:41:26 – How to Iterate Over DataFrame Rows (And Should You?)
00:48:31 – pyFLAC: Real-Time Lossless Audio Compression in Python
00:53:47 – pipx: Install and Run Python Applications in Isolated Environments
00:59:59 – Thanks and goodbye

Show Links:

Build a Blog Using Django, Vue, and GraphQL – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use.

Detecting Deforestation From Satellite Images – How would you go about detecting deforestation — a contributor to climate change — from satellite images? In this article, you’ll learn how one team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard. The article discusses methodology and results, and is a great read about building an ML solution. The project code is available on GitHub.

The Tools and Tech I Use to Run a One-Woman Hardware Company – Winterbloom makes open-source, boutique synthesizers. There’s a lot that goes into running a hardware company. Someone has to design the hardware, code the firmware, write the documentation, not to mention administrate the company. Winterbloom does all of this with just one engineer — Stargirl Flowers. Learn what tools and tech Stargirl uses to run her company, and how Python fits into the big picture in more ways than one.

Visualizing Data in Python Using plt.scatter() – In this tutorial, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.

Why the Sad Face? – The Black autoformatter adopts some conventions that might surprise you the first time you use it. One of those conventions — the “sadface dedent” — moves closing parentheses in function signatures and other block headers to their own lines. This creates a line containing nothing but “):”, which looks like a sad face emoji. Łukasz Langa, Black’s creator, explains why Black does this.

How to Iterate Over DataFrame Rows (And Should You?) – How to iterate over pandas DataFrame rows is one of the top voted questions with the pandas tag on Stack Overflow. That question is also the most copied answer with a code block on the entire site. Clearly, lots of people want to iterate over the rows in a DataFrame. But should you do this, or are there better options?

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Create Web Applications Using Only Python With Anvil

Fri, 04 Jun 2021 12:00:00 +0000

What if you could create an application and deploy it to the web with just Python? Wouldn’t it be nice to skip the additional full-stack development steps of learning three different languages in addition to Python? That’s the idea behind Anvil. This week on the show, we have Meredydd Luff, co-founder of Anvil.

We talk about the history of Anvil and how the founders wanted to simplify web app creation. We discuss their choice to make the project open source and how it benefited the project’s development. We also cover creating a portfolio of projects and things that employers look for in the hiring process.

Course Spotlight: Python’s map() Function: Transforming Iterables

In this step-by-step course, you’ll learn how Python’s map() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace map() in a Pythonic and efficient way.

Topics:

00:00:00 – Introduction
00:01:33 – What is Anvil?
00:03:16 – Why did you choose Python for Anvil?
00:06:47 – The three general groups of developers
00:17:04 – Sponsor: Digital Ocean’s App Platform
00:17:40 – What Python libraries are available to developers?
00:19:48 – Working with PDFs and files
00:22:17 – Free accounts scaling to business accounts and self hosting
00:24:32 – Modularity of code and user management
00:31:12 – High school students creating projects and starting programming
00:35:33 – Video Course Spotlight
00:36:45 – What is server code vs client code?
00:41:35 – Designing the UI and responsive web design
00:48:10 – Working with APIs and modularity of code
00:49:47 – Building a portfolio of projects as a employment strategy
00:52:29 – If you were to start this project over from scratch, what would you change?
00:55:01 – What were the benefits to Anvil of making it open source?
00:57:10 – What has been your biggest challenge?
01:01:16 – Anvil is hiring
01:05:18 – What are you excited about in the world of Python?
01:07:35 – What do you want to learn next?
01:09:54 – Thanks and goodbye

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Selecting the Ideal Data Structure & Unravelling Python's "pass" and "with"

Fri, 28 May 2021 12:00:00 +0000

How do you know you’re using the correct data structure for your Python project? There are so many built into Python and even more that are importable from the collections module. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. We discuss a recent three-part video course on selecting the ideal data structure.

Along with comparing the types of dictionaries, data records, arrays, stacks, and more, David covers a recent Real Python article about the namedtuple. This deep dive covers how to use the namedtuple to write cleaner code.

We also discuss new articles from previous guest Brett Cannon. He has added two posts to his Python syntactic sugar series about unravelling the pass and with statement.

We cover several other articles and projects from the Python community including, async in Flask 2.0, Python projects on Github that are examples of best practices and good architecture, how SpaceX sort of lands starship, the new ti-84 calculator with Python, and building a Python spell checker.

Spotlight: Stacks and Queues: Selecting the Ideal Data Structure

In this course, you’ll learn about three of Python’s data structures: stacks, queue and priority queues. You’ll look at multiple types and classes for all of these and learn which implementations are best for your specific use cases.

Topics:

00:00:00 – Introduction
00:02:06 – Write Pythonic and Clean Code With namedtuple
00:05:38 – Unravelling the pass Statement
00:10:31 – Unravelling the with statement
00:13:38 – Async in Flask 2.0
00:19:04 – Sponsor: Digital Ocean’s App Platform
00:19:40 – Python Projects on Github That Are Examples of Best Practices and Good Architecture
00:26:43 – How SpaceX Lands Starship (Sort Of)
00:32:00 – Stacks and Queues: Selecting the Ideal Data Structure
00:38:34 – Video Course Spotlight
00:40:39 – Texas Instruments To Release New TI-84 Calculator With Python
00:44:23 – spylls: Python spell checker
00:47:36 – Thanks and goodbye

Show Links:

Write Pythonic and Clean Code With namedtuple – In this step-by-step tutorial, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.

Unravelling the pass Statement – When you need to indicate that a bit of code intentionally does nothing, then you need to reach for Python’s pass statement. In the latest installment of Brett’s “Syntactic Sugar” series, you’ll learn how pass works, when to use it, and why it’s a uniquely Python concept.

Unravelling the with statement

Async in Flask 2.0 – This article looks at Flask 2.0’s new async functionality and how to leverage it in your Flask projects. You’ll learn how Flask processes requests asynchronously using a traditional WSGI server, instead of the ASGI server used by many other async web frameworks. You’ll also learn how to simulate Flask 2.0 async in Flask 1.X applications.

Python Projects on Github That Are Examples of Best Practices and Good Architecture – This Reddit thread is full of GitHub repos that might make for some good code reading.

How SpaceX Lands Starship (Sort Of) – While waiting for SN15 to launch, Thomas Goddard set out to pull together a 2-dimensional simulation of the Starship landing. Tying together knowledge of trajectory optimization, Thomas modeled the landing in Python with the CasADI library and used Matplotlib to generate an animation which, when played side-by-side with the footage of the landing, results in remarkable similarity to the actual landing dynamics.

Stacks and Queues: Selecting the Ideal Data Structure – Learn about three of Python’s data structures: stacks, queue and priority queues. You’ll look at multiple types and classes for all of these and learn which implementations are best for your specific use cases.

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Scaling Data Science and Machine Learning Infrastructure Like Netflix

Fri, 21 May 2021 12:00:00 +0000

Would you move your data science project from a laptop to the cloud? Would you also like to have snapshots of your project saved along the way so that you can go back in time or share the state of your project with another team member? This week on the show, we have Savin Goyal from Netflix. Savin is the technical lead for machine learning infrastructure at Netflix. He joins us to talk about Metaflow, an open-source tool to simplify building, managing, and scaling data science projects.

Metaflow addresses the needs of the numerous data scientists who work at Netflix. Machine learning is key strength for the streaming service. They tried several existing tools to scale their own internal infrastructure and after this experimentation developed Metaflow.

We talk about the history of the project and how someone could get started with the open-source version. Savin also contrasts the cost of infrastructure as compared to data scientists and the cost of their time.

Course Spotlight: Simplify Python GUI Development With PySimpleGUI

In this step-by-step course, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application.

Topics:

00:00:00 – Introduction
00:01:53 – What is Metaflow?
00:04:15 – Savin’s background in data science and infrastructure
00:06:06 – Democratization of infrastructure and iteration of tools
00:10:34 – What information is saved about the infrastructure requirements for a project?
00:17:17 – How are the requirements annotated?
00:18:39 – Sponsor: Digital Ocean’s App Platform
00:19:15 – How do project snapshots work?
00:29:33 – Cost of infrastructure vs data scientists
00:32:28 – Working with data at Netflix scale
00:37:55 – Video Course Spotlight
00:39:06 – Getting an organization to use new tools and then making open-source
00:49:51 – Documentation of Metaflow and getting started on solving infrastructure problems
00:53:57 – What made you interested in working on infrastructure tools?
00:55:13 – What is something you are excited about in the world of Python?
00:56:18 – What do you want to learn next?
00:58:14 – Thanks and goodbye

Show Links:

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Building a Platform Game With Arcade and Covering Python News Monthly

Fri, 14 May 2021 12:00:00 +0000

Did you know the Python Software Foundation is hiring! With the recent support of three Visionary Sponsors, the PSF has been able to open positions for a developer-in-residence and a Python packaging project manager. Real Python now has a monthly Python news article. Frequent guest of the show, David Amos compiles and summarizes the biggest Python news from the past month.

This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. We discuss David’s news article from the last month. We also discuss previous guest Jon Fincher’s new step-by-step tutorial about creating a platform game with the arcade framework.

We cover several other articles and projects from the Python community including, how to use ipywidgets to make your Jupyter notebook interactive, the hidden performance overhead of Python C extensions, adding else to for loops, film simulations from scratch using Python, a gradual programming language named Hedy, and a Python raytracer.

Spotlight: CPython Internals: Your Guide to the Python 3 Interpreter

Unlock the Inner Workings of the Python Language, Compile the Python Interpreter From Source Code, And Participate in the Development of CPython

Topics:

00:00:00 – Introduction
00:02:04 – How to Use ipywidgets to Make Your Jupyter Notebook Interactive
00:06:07 – Build a Platform Game in Python With arcade
00:12:35 – Sponsor: Digital Ocean’s App Platform
00:13:11 – The Hidden Performance Overhead of Python C Extensions
00:21:17 – For-Else: A Weird but Useful Feature in Python
00:25:42 – Python News: What’s New From April 2021?
00:39:43 – Spotlight: CPython Internals Now in Paperback!
00:41:15 – Film Simulations From Scratch Using Python
00:47:44 – hedy: Hedy Is a Gradual Programming Language, Which Increases in Syntactic Elements Level by Level
00:50:27 – Python-Raytracer: A Basic Ray Tracer That Exploits NumPy Arrays and Functions to Work Fast
00:53:31 – Thanks and goodbye

Show Links:

How to Use ipywidgets to Make Your Jupyter Notebook Interactive – Jupyter Notebooks are great for exploratory data analysis. They’re also a good way to share results and analysis with other people, who can alter the notebook to further explore the data themselves. But there are some limitations to notebook interactivity. That’s where ipywidgets comes in! In this tutorial you’ll learn how to create widgets like check boxes, drop-down menus, sliders, and how to handle events like button clicks.

Build a Platform Game in Python With arcade – Building games can be a fun way to learn new Python concepts and practice techniques you’ve already learned. Plus, they make for great projects to share! This step-by-step tutorial shows you how to build a platform game using the arcade library. You’ll learn techniques for designing levels, sourcing assets, and implementing advanced features

The Hidden Performance Overhead of Python C Extensions – It’s no secret that Python is slower than compiled languages like C, C++, and Rust. If you need a performance boost, you can write compiled Python C extensions. But there are some hidden performance costs that you should be aware of if you decide to do this. This article explains two ways that Python C extensions can actually be slower than pure Python and discusses some solutions and work around for them.

For-Else: A Weird but Useful Feature in Python – Python for loops have an unusual feature: they support an else block that only executes if there is no break in the loop. The pattern isn’t used very often with the argument against it being that it is a bit weird and potentially difficult to understand. But there may be times when for/else makes sense. This article presents three situations where for/else is useful and argues that, in these situations, the pattern makes the code more readable.

Python News: What’s New From April 2021? – April 2021 was an eventful month in the world of Python. In this article, you’ll get up to speed on everything that happened in the past month, including new sponsorships for the PSF, changes to Python error messages, and a community-led discussion over the future of type annotations.

Film Simulations From Scratch Using Python – In analog photography, you can achieve different “looks” for your photographs by selecting different kinds of film to shoot with. Digital camera manufacturers often include different presets to simulate different kinds of film. In this article, you’ll learn how to simulate different films on your own images using color lookup tables, or CLUTs, using NumPy and the Pillow image library.

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Organizing and Restructuring DjangoCon Europe 2021

Fri, 07 May 2021 12:00:00 +0000

Are you interested in learning more about Django? Would you like to meet other professionals and learn how they are using Django? DjangoCon Europe 2021 is virtual this year, and you can join in from anywhere in the world. This week on the show, we have Miguel Magalhães and David Vaz, two of the organizers of the conference.

We discuss what makes DjangoCon Europe unique. David and Miguel talk about how they got involved and how the conference passes between different countries. They also cover the struggle of upending their plans for hosting the conference in Porto Portugal last year and how this year could use some extra support.

Tickets are available now. DjangoCon Europe is looking for additional sponsors. If you work for an organization that can help, get in contact with them.

Course Spotlight: Get Started With Django: Build a Portfolio App

Topics:

00:00:00 – Introduction
00:01:43 – How did you get involved with DjangoCon Europe?
00:10:42 – European vs US conference differences
00:12:24 – What makes a DjangoCon unique?
00:13:27 – What are some examples of projects using Django?
00:15:02 – Sponsor: Digital Ocean’s App Platform
00:15:38 – What are types of talks will the conference have?
00:20:04 – Conference schedule during the week
00:24:40 – Who is the intended audience?
00:25:29 – Video Course Spotlight
00:26:43 – Sharing your project and lightning talks
00:28:56 – What tools are you using to facilitate a virtual event?
00:33:32 – Ticket grants and sponsors
00:38:55 – What is your background with and use of Django?
00:43:45 – What are you excited about in the world of Python?
00:46:05 – What do you want to learn next?
00:52:25 – Thanks and goodbye

Show Links:

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Podcast Rewind With Guest Highlights for 2020-2021

Fri, 30 Apr 2021 12:00:00 +0000

This week’s show is a bit different. We are taking a well-deserved short break, but we still wanted to share an episode with you. This rewind episode highlights clips from the many interviews over the past year or so of the show.

We also hear from many new listeners who have just discovered the show. Welcome aboard! We wanted to provide a sample of guests, topics, and questions we feature on the show.

For long-time listeners, this will be a brisk walk through past episodes and guests. We’ve talked with many guests, and it was hard to narrow it down to the sample provided here. We hope you enjoy this podcast rewind, and look forward to sharing a fantastic slate of upcoming guests.

Course Spotlight: Plot With Pandas: Python Data Visualization Basics

Topics:

00:00:00 – Introduction
00:01:28 – E03 Brett Slatkin: Is Python a good tool for infrastructure?
00:03:30 – E07 Łukasz Langa: Origins of Black
00:10:55 – E08 Tania Allard: Reproducibility of project results
00:13:06 – Sponsor: Digital Ocean
00:13:47 – E11 Anthony Shaw: DRY (Don’t Repeat Yourself)
00:16:38 – E16 Hannah Stepanek: Creating NumPy types
00:20:02 – E18 Armin Ronacher: What would you change if you started the Flask project from scratch?
00:23:20 – E22 Russell Keith-Magee: Funding open-source projects
00:26:27 – E26 Michael Kennedy: How is the GIL part of the problem?
00:29:18 – Video Course Spotlight
00:30:27 – E30 Christopher Trudeau - The PEG parser
00:33:00 – E39 Reuven Lerner: What makes generator functions different?
00:37:35 – E47 Brett Cannon: Unravelling Python’s syntatic sugar series
00:43:43 – Thanks and goodbye

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Taking the Next Step in Python Game Development

Fri, 23 Apr 2021 12:00:00 +0000

Are you interested in creating video games but feel limited in what you can accomplish within Python? Is there a platform where you can take advantage of your Python skills and provide the benefits of a dedicated game engine? This week on the show, we have Paweł Fertyk. Paweł is a Real Python author and has been creating games as Miskatonic Studio for several years now.

Paweł has worked with PyGame. We recently featured his article on creating a clone of Asteroids in a previous episode. After working with PyGame for a while, he also tried a visual novel engine named Ren’Py, and Panda3D.

After struggling within these Python libraries, he started to look for an open-source game engine that could help him create the types of games he was striving to create. He found Godot and its Python-like scripting language of GDScript. We talk about his creations, the tools, and how game development is not exactly like most other types of development.

Course Spotlight: Make a 2D Side-Scroller Game With PyGame

In this step-by-step course, you’ll learn how to use PyGame. This library allows you to create games and rich multimedia programs in Python. You’ll learn how to draw items on your screen, implement collision detection, handle user input, and much more!

Topics:

00:00:00 – Introduction
00:01:55 – Writing for Real Python
00:02:58 – Asteroids PyGame Article
00:11:05 – Do you think programming games is a good way to learn programming?
00:13:46 – What game technologies did you try before PyGame?
00:18:35 – Trying out Ren’Py, Panda3D, and looking for an engine
00:27:16 – Sponsor: Digital Ocean
00:27:56 – What appealed to you about Godot?
00:33:42 – Working with a GUI editor
00:37:03 – GDScript, programming game logic, and similarities to Python
00:42:46 – Creating Molecules: Osmos clone
00:48:21 – Video Course Spotlight
00:49:33 – Creating Intrepid: 3D Escape Room
00:55:47 – Creating 3D assets and finding collaborators
00:58:18 – Exporting the finished game
01:01:24 – GOAT: Godot Open Adventure Template
01:08:27 – What are you excited about in the world of Python?
01:12:39 – What do you want to learn next?
01:14:57 – Thanks and goodbye

Show Links:

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OrderedDict vs dict and Object Oriented Programming in Python vs Java

Fri, 16 Apr 2021 12:00:00 +0000

Are you looking for a bit of order when working with dictionaries in Python? Are you aware that the Python dict has changed over the last several versions and now keeps items in order? Could you learn more about object-oriented programming in Python by comparing it to another language? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David covers a Real Python article about the differences between the OrderedDict versus a standard dictionary. We discuss a recent video course about how object-oriented programming in Python compares to Java.

We cover several other articles and projects from the Python community including, what is Werkzeug, building a full-text search engine in 150 lines of Python code, loading SQL data into pandas without running out of memory, how to beat the Berlin rental market with a Python script, replacing print with ice cream, and the new version of CircuitPython and Mu.

Course Spotlight: Python vs Java: Object Oriented Programming

In this step-by-step course, you’ll learn about the practical differences in Python vs Java for object-oriented programming. By the end, you’ll be able to apply your knowledge to Python, understand how to reinterpret your understanding of Java objects to Python, and use objects in a Pythonic way.

Topics:

00:00:00 – Introduction
00:02:07 – OrderedDict vs dict in Python: The Right Tool for the Job
00:09:30 – What is Werkzeug?
00:13:08 – Sponsor: Digital Ocean
00:13:47 – Building a Full-Text Search Engine in 150 Lines of Python Code
00:19:53 – Loading SQL Data Into Pandas Without Running Out of Memory
00:25:35 – How I Beat the Berlin Rental Market With a Python Script
00:34:36 – Python vs Java: Object Oriented Programming
00:38:42 – Video Course Spotlight
00:40:11 – Upcoming video courses
00:41:17 – icecream: Never Use print() to Debug Again
00:45:28 – CircuitPython 6.2.0 Released
00:49:40 – Thanks and goodbye

Show Links:

OrderedDict vs dict in Python: The Right Tool for the Job – In this step-by-step tutorial, you’ll learn what Python’s OrderedDict is and how to use it in your code. You’ll also learn about the main differences between regular dictionaries and ordered dictionaries.

What is Werkzeug? – Have you ever noticed that when you install Flask a dependency called Werkzeug is also installed? Werkzeug provides a set of utilities for building a WSGI interface in Python, which is an important part of any web application. This article will take you on a deep dive of Werkzeug and show you exactly how it works so you can have a deeper understanding of Flask applications.

Building a Full-Text Search Engine in 150 Lines of Python Code – Go from data preparation to search engine in just a few lines of Python.

Loading SQL Data Into Pandas Without Running Out of Memory – If you need to load a bunch of SQL query results into a Pandas DataFrame, then you might run into a problem if there are enough rows in the SQL query’s results: it won’t fit in RAM. Panda’s read_sql() function has a batching option, but it loads all of the data into memory, too. So, how do you handle larger-than-memory queries with Pandas? This article will show you how!

How I Beat the Berlin Rental Market With a Python Script – Learn how one Python developer used a Python script to analyze the housing market in Berlin and predict when a property would be sold or when the price would be decreased. While the article doesn’t include a lot of technical details, it’s a great case study of how Python, its rich ecosystem, and a little creativity can turn solving a banal problem — like searching for a new house in a crowded market — into something fun and intellectually rewarding!

Python vs Java: Object Oriented Programming – You may have heard that “everything is an object in Python.” But what does that mean for doing object oriented programming? If you’re coming to Python with a Java background, you’ll want to check out this course to learn how to reinterpret your understanding of Java objects to Python, and use objects in a Pythonic way.

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Getting Started With Refactoring Your Python Code

Fri, 09 Apr 2021 12:00:00 +0000

Do you think it’s time to refactor your Python code? What should you think about before starting this task? This week on the show, we have Brendan Maginnis and Nick Thapen from Sourcery. Sourcery is an automated refactoring tool that integrates into your IDE and suggests improvements to your code.

Nick and Brendan provide advice on how to start refactoring and setting achievable code objectives. We discuss setting up unit testing and building confidence that you aren’t changing your code’s fundamental meaning. We also talk about technical debt and how it can creep into your organization’s projects.

Course Spotlight: Python Booleans: Leveraging the Values of Truth

In this course, you’ll learn about the built-in Python Boolean data type, which is used to represent the truth value of an expression. You’ll see how to use Booleans to compare values, check for identity and membership, and control the flow of your programs with conditionals.

Topics:

00:00:00 – Introduction
00:01:31 – What is refactoring?
00:03:19 – How is it connected to extensibility?
00:04:33 – What are methods for preparing your code?
00:05:35 – Being confident you are not changing the meaning of the code
00:09:34 – Refactoring as you go
00:11:16 – What is technical debt, how is it generated?
00:19:08 – Sponsor: Digital Ocean
00:19:45 – Code metrics
00:23:32 – Holding code in your head, and design patterns
00:28:03 – Comments in code and function definitions
00:35:04 – Automated refactoring
00:39:34 – Using a code formatter
00:43:19 – Video Course Spotlight
00:44:27 – Team decisions around refactoring
00:47:41 – Examples where refactoring made a difference
00:51:56 – A few additional notes about Sourcery
00:52:59 – What is something you are excited about in the world of Python?
00:55:21 – What do you want to learn next?
00:56:20 – Thanks and goodbye

Show Links:

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Building a Neural Network and How to Write Tests in Python

Fri, 02 Apr 2021 12:00:00 +0000

Do you know how a neural network functions? What goes into building one from scratch using Python? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python article titled “Python AI: How to Build a Neural Network & Make Predictions.” This article covers how to train a neural network and create a linear regression model.

We also cover several articles about testing in Python including, writing unit tests, testing code in Jupyter notebooks, and a testing style guide.

We cover several other articles and projects from the Python community including, how to build an Asteroids game with Python and Pygame, a 5-point framework for Python performance management, how it helps to know a Python programmer if you want a vaccination appointment, a Flask mega-tutorial, and the new release of SQLAlchemy.

Course Spotlight: Python Coding Interviews: Tips & Best Practices

Topics:

00:00:00 – Introduction
00:02:01 – Build an Asteroids Game With Python and Pygame
00:08:18 – Python AI: How to Build a Neural Network & Make Predictions
00:11:51 – Sponsor: Scout APM
00:12:56 – How to Write Unit Tests in Python, Part 1: Fizz Buzz
00:19:40 – A 5-Point Framework For Python Performance Management
00:26:16 – Unit Testing Python Code in Jupyter Notebooks
00:30:02 – Python Testing Style Guide
00:31:32 – Video Course Spotlight
00:32:47 – Want a vaccination appointment? It helps to know a Python programmer
00:37:29 – Flask Megatutorial
00:41:22 – SQLAlchemy version 1.4.0
00:45:14 – Thanks and goodbye

Show Links:

Build an Asteroids Game With Python and Pygame – Build a clone of the Asteroids game in Python using Pygame. Step by step, you’ll add images, input handling, game logic, sounds, and text to your program.

Python AI: How to Build a Neural Network & Make Predictions – Build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You’ll learn how to train your neural network and make accurate predictions based on a given dataset.

How to Write Unit Tests in Python, Part 1: Fizz Buzz – Get an introduction to unit testing in Python from the author of the Flask Megatutorial.

A 5-Point Framework For Python Performance Management – “Performance testing — like sailboat racing — depends on the conditions along the racecourse.”

Unit Testing Python Code in Jupyter Notebooks – Even if you code in Jupyter notebooks, there’s no excuse to not be testing your code!

Python Testing Style Guide – Need a quick yet thorough guide to testing? This excellent resource is for you.

Want a vaccination appointment? It helps to know a Python programmer – Programmers are writing scripts to help find vaccine appointments for those who are eligible.

Projects:

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Improving the Learning Experience on Real Python

Fri, 26 Mar 2021 12:00:00 +0000

If you haven’t visited the website lately, then you’re missing out on the updates to realpython.com! The site features a completely refreshed layout with multiple sections to help you take advantage of even more great educational Python content. This week on the show, we have Dan Bader, the person behind Real Python, and all these architectural changes.

Among the features changed are a new bookmarking system, a section to keep track of what you’ve been learning lately, and a much more advanced way to search the site. A new tile system makes it easier to explore learning paths, quizzes, office hours, and other sections of the site.

Dan shares details about the website technology stack and why he started using Python for the core content management system. He also talks about the struggle of being the sole maintainer and feature architect.

Spotlight: Python Basics: A Practical Introduction to Python 3

Go from beginner to intermediate in Python with this complete curriculum, up-to-date for Python 3.9. Python Basics includes exercises, interactive quizzes, and sample projects, so you’ll always know what to focus on next in order to build a strong Python foundation. Paperback copies are available now.

Topics:

00:00:00 – Introduction
00:01:44 – Welcome to the show Dan!
00:03:12 – What updates are happening on the website?
00:04:40 – The “Continue Learning” section
00:16:02 – Updating how search works
00:21:41 – Sponsor: PyCharm
00:22:22 – Implementing bookmarking of articles
00:26:39 – A development team of one
00:28:16 – Surfacing features with explore
00:35:39 – How to take advantage of the new features?
00:39:26 – Spotlight: Python Basics in paper back available now!
00:41:12 – What did it take to implement progress system?
00:47:27 – Closed captions and transcripts complete across all courses
00:48:47 – Python Basics and CPython Internals books
00:53:38 – What are you excited about in the world of Python?
00:58:40 – What do you want to learn next?
01:02:21 – What is something you thought you knew about Real Python but were wrong about it?
01:06:00 – Request for reviews, feedback, and questions
01:09:06 – Thanks and goodbye

Show Links:

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Connecting to MongoDB and Updates on the Python Packaging Landscape

Fri, 19 Mar 2021 12:00:00 +0000

Have you heard about NoSQL databases, or wondered how to use one with Python? How does MongoDB store information and what packages can you use to connect this type of database to your Python project? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python video course about managing namespaces in Python. We also look at a few recent stories about the Python packaging ecosystem.

We cover several other articles and projects from the Python community including, generating customizable PDF reports with Python, how semantic versioning will not save you, PEP 621 is final, a user hits the Python community with 4,000 fake modules, making a synth with Python, and what is running on the Mars helicopter.

Course Spotlight: Navigating Namespaces and Scope in Python

Topics:

00:00:00 – Introduction
00:01:46 – Generate Customizable PDF Reports With Python
00:04:54 – Semantic Versioning Will Not Save You
00:14:55 – Sponsor: Digital Ocean
00:15:34 – PEP 621 Is Final
00:19:51 – Poison Packages: User Hits Python Community With 4000 Fake Modules
00:26:01 – Python and MongoDB: Connecting to NoSQL Databases
00:31:24 – Navigating Namespaces and Scope in Python
00:35:22 – Making a Synth With Python: Oscillators
00:39:19 – Video Course Spotlight
00:40:23 – Python Is Running on the Mars Helicopter
00:44:14 – Thanks and goodbye

Show Links:

Generate Customizable PDF Reports With Python – Learn how to generate custom PDF reports using reportlab and pdfrw with a PyQt GUI.

Semantic Versioning Will Not Save You – Semantic versioning aims to both communicate the version of software as well as promise that certain versions won’t break anything. Sounds great, right? In a lot of cases it is, but a blind reliance on semantic versioning can come back to haunt you.

PEP 621 Is Final – In the near future, you’ll be able to store project metadata in pyproject.toml. Brett Cannon

Poison Packages: User Hits Python Community With 4000 Fake Modules – Recently, a PyPI user going by the name “Remind Supply Chain Risks” uploaded nearly 4,000 fake modules to the index, many of which were named as common misspellings of popular packages. Learn about the incident in this article, and read all the way to the end for four tips every Python developer should follow.

Python and MongoDB: Connecting to NoSQL Databases – Learn how to use Python to interface with the NoSQL database system MongoDB. You’ll get an overview of the differences between SQL and NoSQL, and you’ll also learn about related tools, including PyMongo and MongoEngine.

Navigating Namespaces and Scope in Python – Learn about Python namespaces, the structures used to store and organize the symbolic names created during the execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

Projects:

Making a Synth With Python: Oscillators – Learn how to create oscillators using Python as a foundation for creating your own software synthesizers. This article is one of a three-part series. The other articles cover modulators and controllers.

Python Is Running on the Mars Helicopter

How the First Helicopter on Mars Uses Off-the-Shelf Hardware and Linux

Additional Links:

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Navigating Options for Deploying Your Python Application

Fri, 12 Mar 2021 12:00:00 +0000

What goes into the decision of how to host your Python code or application in the cloud? Which technology stack is the right size for your project? This week on the show, we have Calvin Hendryx-Parker. Calvin talks about cloud hosting options, infrastructure choices, and deployment tools.

Calvin is the co-founder and CTO of Six Feet Up, and co-organizer of the Python Web Conference. We talk about finding the right tools for clients. He also discusses the Python platform they created for hosting a virtual conference.

We also discuss hosting personal portfolio projects. That conversation leads to the question, what types of skills you can showcase through creating a hosted project.

Course Spotlight: Creating PyQt Layouts for GUI Applications

In this step-by-step course, you’ll learn how to use PyQt layouts to arrange and manage the graphical components on your GUI applications. With the help of PyQt’s layout managers, you’ll be able to create polished and professional GUIs with minimal effort.

Topics:

00:00:00 – Introduction
00:01:46 – What considerations to start with for deployment?
00:04:02 – What is Saltstack?
00:06:00 – The changing cloud hosting landscape
00:10:12 – Containers, Docker, and other standards
00:11:28 – How do you learn about this technology?
00:15:58 – Concerns of setting up development vs production environments
00:17:41 – Security concerns
00:19:20 – Sponsor: Scout APM
00:20:26 – Deploying a Python portfolio project
00:23:12 – Deploying for a small business project or API
00:29:11 – Cloud formation, Terraform, additional tools
00:30:22 – Deploying a large project
00:35:12 – Frontend frameworks for large web projects
00:39:30 – Video Course Spotlight
00:40:43 – What does your consultancy do?
00:41:37 – What things do you look for in an employee?
00:50:42 – Python Web Conference 2021
00:57:43 – What are you excited about in the world of Python?
00:59:09 – What do you want to learn next?
01:00:49 – What is something you thought you knew about Python, but were wrong about it?
01:02:23 – Thanks and goodbye

Show Links:

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Consuming APIs With Python and Building Microservices With gRPC

Fri, 05 Mar 2021 12:00:00 +0000

Have you wanted to get your Python code to consume data from web-based APIs? Maybe you’ve dabbled with the requests package, but you don’t know what steps to take next. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article titled, “Python’s APIs: A Winning Combo for Reading Public Data”. David shares another Real Python article about creating microservices using Google Remote Procedure Calls (gRPC).

We also cover several other articles and projects from the Python community including, making a difficult data analysis question easy with pandas, efficiently cleaning text with pandas, the tricky bits of Python concurrency, building rich terminal dashboards, making better assertions for Python tests, and building and managing real-life data science projects with metaflow.

Course Spotlight: Making HTTP Requests With Python

The “requests” library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. This course shows you how to work effectively with “requests”, from start to finish.

Topics:

00:00:00 – Introduction
00:01:46 – Python Microservices With gRPC
00:07:49 – Python’s APIs: A Winning Combo for Reading Public Data
00:15:07 – Making a Difficult Data Analysis Question Easy With Pandas
00:21:07 – Efficiently Cleaning Text With Pandas
00:34:20 – Video Course Spotlight
00:35:27 – Python Concurrency: The Tricky Bits
00:41:49 – Building Rich Terminal Dashboards
00:45:08 – python-precisely: Better Assertions for Python Tests
00:48:45 – metaflow: Build and Manage Real-Life Data Science Projects With Ease
00:52:35 – Thanks and goodbye

Show Links:

Python Microservices With gRPC – Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.

Python’s APIs: A Winning Combo for Reading Public Data – Learn what APIs are and how to consume them using Python. You’ll also learn some core concepts for working with APIs, such as status codes, HTTP methods, using the requests library, and much more.

Making a Difficult Data Analysis Question Easy With Pandas – A great strategy to use when faced with a tricky data analysis problem is to reshape the dataset into a format that turns it into an easy problem. In this article, you’ll look at an example involving a simple calculation and extensive reshaping in pandas.

Efficiently Cleaning Text With Pandas – In this article, you’ll see some examples of cleaning text fields in a large data file and learn several strategies for efficiently cleaning unstructured text fields using Python and pandas.

Python Concurrency: The Tricky Bits – An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Building Rich Terminal Dashboards – Learn how to use the Rich CLI library’s new terminal dashboard feature.

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The Challenges of Developing Into a Python Professional

Fri, 26 Feb 2021 12:00:00 +0000

What’s the difference between writing code for yourself and developing for others? What new considerations do you need to take into account as a professional Python developer? This week on the show, we talk to Dane Hillard about his book “Practices of the Python Pro”.

Dane discusses his philosophy on the design principles that go into writing code. We talk about namespaces, object-oriented design, and how to keep your code extensible. We also consider the how and when of code optimization.

Course Spotlight: Dictionaries and Arrays: Selecting the Ideal Data Structure

Topics:

00:00:00 – Introduction
00:01:29 – Release and response to Practices of the Python Pro
00:03:12 – What was the writing process like?
00:06:09 – What makes someone a professional?
00:12:30 – How have you and the tools changed in Python testing?
00:14:10 – When did you start to see the change in your career?
00:15:42 – Sponsor: PyCharm
00:16:27 – What topic were you excited to share in the book?
00:17:49 – The importance of code design and ergonomics
00:20:52 – Why is managing and designing namespaces important?
00:26:32 – Expanding that design thought process to object-oriented programming
00:30:02 – Differences of functional vs object-oriented programming
00:34:40 – Video Course Spotlight
00:36:04 – What do you mean by extensible?
00:42:59 – How and when to optimize code?
00:45:57 – Sharing developer philosophy
00:46:52 – What are you excited about in the world of Python?
00:48:31 – What do you want to learn next?
00:51:03 – Thanks and goodbye

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Stochastic Gradient Descent and Deploying Your Python Scripts on the Web

Fri, 19 Feb 2021 12:00:00 +0000

Do you know the initial steps to get your Python script hosted on the web? You may have built something with Flask, but how would you stand it up so that you can share it with others? This week on the show, we have the previous guest Martin Breuss back on the show. Martin shares his recent article titled, “Python Web Applications: Deploy Your Script as a Flask App”. David Amos also returns, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares a recent mathematical Real Python article about the stochastic gradient descent algorithm with Python. Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find ideal model parameters.

We also cover several other articles and projects from the Python community including, property-based testing with hypothesis, Python’s tug of war between beginner-friendly features and support for advanced users, how Python integers work, the steering council accepts PEP 634, a magical full-stack framework for Django named django-unicorn, and a visual programming environment called Math Inspector.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

Topics:

00:00:00 – Introduction
00:02:44 – Property-Based Testing With hypothesis, and Associated Use Cases
00:09:55 – Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users
00:18:50 – Sponsor: Scout APM
00:19:54 – How Python Integers Work
00:26:53 – Python Steering Council Accepts PEP 634
00:32:48 – Stochastic Gradient Descent Algorithm With Python and NumPy
00:38:36 – Video Course Spotlight
00:39:39 – Martin Breuss - Followup about Stay at Home Mentorship Program
00:42:13 – Python Web Applications: Deploy Your Script as a Flask App
00:52:25 – django-unicorn: A Magical Full-Stack Framework for Django
00:55:15 – Math Inspector: A Visual Programming Environment for Scientific Computing With NumPy and SciPy
01:00:21 – Thanks and goodbye

Show Links:

Property-Based Testing With hypothesis, and Associated Use Cases – Testing software is hard. Property-based testing can help you create more effective tests. Learn how to do property-based testing with the hypothesis framework by looking at some real-world use cases.

Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users – Python has made some big improvements to tracebacks in recent versions. See how tracebacks have evolved over the last couple of major releases and where there’s still some work left to be done. Check out the discussion on Hacker News.

How Python Integers Work – Python’s integer datatype is pretty different from most other languages because they allow arbitrary precision. Learn how integers work under the hood in this in-depth article.

Python Steering Council Accepts PEP 634 – Pattern matching, which adds a kind of switch-case statement to Python, has been accepted.

Stochastic Gradient Descent Algorithm With Python and NumPy – Learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.

Python Web Applications: Deploy Your Script as a Flask App – In this tutorial, you’ll learn how to go from a local Python script to a fully deployed Flask web application that you can share with the world.

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Unraveling Python's Syntax to Its Core With Brett Cannon

Fri, 12 Feb 2021 12:00:00 +0000

Do you feel like you understand how Python works under the hood? What is syntactic sugar, and how much of it should be in Python? This week on the show, we have Brett Cannon. Brett is a Python core developer and he’s been working on a series of articles where he is unraveling the syntax of Python. His series is a fantastic resource for those wanting to learn how Python is structured and works at its core.

Brett wants to see a version of Python that can run in web browsers, so he started to breakdown Python into its syntactic elements to try to answer the question, what are core elements of Python? His detailed series takes the reader along for the ride.

Brett also works at Microsoft as the dev manager for the Python extension for VS Code. Brett is also serving his third term on the Python steering council, and we discuss recent Python enhancement proposals (PEP) that the council is considering.

Course Spotlight: Cool New Features in Python 3.9

In this course, you’ll explore some of the coolest and most useful features in the newly released Python 3.9. You’ll learn how Python 3.9 makes it easier to work with time zones, dictionaries, decorators, and several other techniques that will make your code cleaner and more efficient.

Topics:

00:00:00 – Introduction
00:01:43 – Working on the Python extension for VSCode
00:04:17 – Microsoft and the Python community
00:07:19 – How long have worked on core Python?
00:11:49 – Ways to contribute to core Python
00:14:19 – Upcoming features and PEPs
00:15:41 – Pattern matching PEPs
00:17:48 – Sponsor: Digital Ocean
00:18:29 – Being a member of the Python Steering Council
00:21:10 – Unravelling Python’s syntatic sugar series
00:24:39 – Magic methods, dunder methods, or special methods
00:27:07 – Are there ways that syntatic sugar can be overused?
00:33:31 – WebAssembly and Python being available in the browser
00:45:51 – Does Circuit Python or MicroPython show a path?
00:52:37 – Video Course Spotlight
00:53:41 – Taking Python syntax down to the implementation layer
01:03:21 – Taking apart Python’s syntax
01:16:07 – What other parts of Python syntax will you be unravelling?
01:24:04 – What are you excited about in the world of Python?
01:26:46 – What do you want to learn next?
01:31:24 – Thanks and goodbye

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C for Python Developers and Data Visualization With Dash

Fri, 05 Feb 2021 12:00:00 +0000

Are you interested in building interactive dashboards with Python? How about a project that takes a flat data file all the way to a web-hosted interactive dashboard? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article about data visualizations using Dash, David covers an article designed to help Python developers understand the fundamentals of C. We discuss a couple of articles about Excel and using Python with Microsoft Office.

We also cover several other articles and projects from the Python community including, out-of-memory crashes in Python, updating all packages with pip-review, data science notebooks for teams, and a command-line tool for looking up colors, shades, and palettes.

Course Spotlight: Command Line Interfaces in Python

Topics:

00:00:00 – Introduction
00:01:36 – CPython Internals Book
00:03:18 – C for Python Programmers
00:06:11 – Dying, Fast and Slow: Out-Of-Memory Crashes in Python
00:13:29 – Automating Excel File Creation and Distribution With Pandas And Outlook
00:18:33 – Update All Packages With pip-review
00:23:49 – Video Course Spotlight
00:25:04 – Ditching Excel for Python: Lessons Learned From a Legacy Industry
00:30:20 – Develop Data Visualization Interfaces in Python With Dash
00:38:11 – Deepnote: Data Science Notebook for Teams
00:41:25 – colorpedia: Command-Line Tool for Looking Up Colors, Shades and Palettes
00:43:45 – Thanks and goodbye

Show Links:

C for Python Programmers – In this tutorial, you’ll learn the basics of the C language, which is used in the source code for CPython, the most popular Python implementation. Learning C is important for Python programmers interested in contributing to CPython.

Dying, Fast and Slow: Out-Of-Memory Crashes in Python – Learn about the different ways that memory issues can manifest in your Python programs, and how you can debug and fix them.

Automating Excel File Creation and Distribution With Pandas And Outlook – See how a little bit of Python can go a long way to automating manual processes and save businesses valuable time.

Update All Packages With pip-review – Keeping track of Python dependencies and updates can be tricky. The pip-review tool automates a lot of this process in a convenient command-line interface.

Ditching Excel for Python: Lessons Learned From a Legacy Industry – Learn how Python is revolutionizing an industry that’s notoriously resistant to change and fraught with every programmer’s most dreaded tool: Excel spreadsheets.

Develop Data Visualization Interfaces in Python With Dash – Learn how to build a dashboard using Python and Dash. Dash is a framework for building data visualization interfaces. It helps data scientists build fully interactive web applications quickly.

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Processing Images in Python With Pillow

Fri, 29 Jan 2021 12:00:00 +0000

Are you interested in processing images in Python? Do you need to load and modify images for your Flask or Django website or CMS? Then you most likely will be working with Pillow, the friendly fork of PIL, the Python imaging library. This week on the show, we have Mike Driscoll, who is writing a new book about image processing in Python.

We dive deep into the types of processing Pillow provides. Mike talks about creating Python GUI applications to take advantage of all the library has to offer. We also talk about his PyDev of the week series and his Python Interviews book.

Course Spotlight: Editing Excel Spreadsheets in Python With openpyxl

In this course, you’ll learn how to handle spreadsheets in Python using the openpyxl package. You’ll learn how to manipulate Excel spreadsheets, extract information from spreadsheets, create simple or more complex spreadsheets, including adding styles, charts, and so on.

Topics:

00:00:00 – Introduction
00:01:40 – Update on Python 101 book
00:03:17 – Pillow: Image Processing With Python
00:04:06 – Kickstarter for the book
00:05:35 – What parts of the Pillow library will the book cover?
00:07:49 – What is ImageChops?
00:09:06 – How do you currently use Pillow?
00:11:06 – What is ImageOps?
00:13:15 – Sponsor Scout APM
00:14:18 – Building a GUI interface for Pillow features
00:16:46 – Other uses for Pillow in testing
00:18:01 – Use in web frameworks and file formats
00:20:17 – What is Pillow not good at?
00:22:13 – Batch processing
00:23:12 – Exif Data and GPS information from images
00:26:57 – Creating a watermark
00:28:58 – Video Course Spotlight
00:30:15 – Writing image process methods as modules
00:33:45 – Timeline for the book release
00:35:04 – Using Pillow in a Jupyter notebook
00:38:02 – Python Interviews Book and PyDev of the Week
00:41:57 – What are you excited about in the world of Python?
00:44:41 – What do you want to learn next?
00:46:25 – Thanks and goodbye

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Creating an Interactive Online Python Conference for PyCascades 2021

Fri, 22 Jan 2021 12:00:00 +0000

How do you create a virtual conference that retains the interactivity of an in-person event? What are the tools needed for talk submissions, ticketing, and live hosting? Can you find those tools written in Python? This week on the show, we have several of the organizers of the PyCascades 2021 conference. They share the process of restructuring a Python conference to meet those challenges.

Nina Zakharenko and Seb Vetter are co-chairs, and Ashia Zawaduk is the conference program chair. PyCascades will be held online from February 19th through 21st, with a day of virtual social events, one of live-streamed talks, and another of mentored sprints.

We discuss ways to recreate the elusive feel of the “hallway” track virtually. They share advice about submitting a talk proposal and ways that you can volunteer for conferences.

Tickets are available now. PyCascades is looking for additional sponsors. If you work for an organization that can help, get in contact with them.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

Topics:

00:00:00 – Introduction
00:01:53 – Introducing the organizers
00:03:27 – Structure and vision for the Conference
00:06:50 – Tools for a virtual conference
00:10:34 – Creating a virtual hallway track
00:12:32 – Testing the platform
00:14:33 – How does a virtual event change the type of audience?
00:15:54 – Opening up the range of available speakers and topics
00:19:35 – Tips for finding success in submitting talk proposals
00:24:28 – Sponsor: PyCharm
00:25:10 – How can someone assist at this and other conferences?
00:26:40 – Preparing first time speakers
00:28:29 – How did each of you get involved?
00:36:13 – Video Course Spotlight
00:37:18 – Currently scheduled talks
00:43:01 – Mentored Sprints for Diverse Beginners
00:49:37 – User groups and meetups
00:52:23 – PyCascades sponsors
00:57:02 – What are you excited about in the world of Python?
01:02:39 – Callout: Get Your Tickets and thanks

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Deep Reinforcement Learning in a Notebook With Jupylet + Gaming and Synthesis

Fri, 15 Jan 2021 12:00:00 +0000

What is it like to design a Python library for three different audiences? This week on the show, we have Nir Aides, creator of Jupylet. His new library is designed for deep reinforcement learning researchers, musicians interested in live music coding, and kids interested in learning to program. Everything is designed to run inside of a Jupyter notebook.

Nir’s initial goal was to create a framework to study deep reinforcement learning, and this led to building a framework for 2D and 3D games and graphics. As he continued the development, he realized that this interactive environment could be a useful tool for learning Python.

We also talk about how he got interested in live music coding and the advanced mathematics of sound synthesis. Nir also shares some resources for finding graphic assets and tools for creating 3D models.

Course Spotlight: Using Jupyter Notebooks

In this step-by-step course, you learn how to get started with the Jupyter Notebook, an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text.

Topics:

00:00:00 – Introduction
00:02:25 – When did you start the project?
00:02:50 – What is deep reinforcement learning?
00:06:11 – How is deep reinforcement learning implemented in Jupylet?
00:06:56 – What graphic libraries are being used?
00:09:56 – What are the audiences for Jupylet?
00:14:15 – Why create features for musicians?
00:15:52 – Interactive code
00:19:13 – Were you using Jupyter Notebooks previously?
00:24:01 – Sponsor Digital Ocean
00:24:40 – Scaling features and making it kid friendly
00:28:59 – Outside help and learning about audio synthesis
00:33:31 – Using NumPy for synthesis, effects, and algorithmic reverb
00:39:08 – Video Course Spotlight
00:40:13 – Relying on other packages for your own package
00:42:26 – Assets for game design and working with 3D
00:47:51 – What has feedback been like?
00:48:31 – Looking for contributors
00:49:45 – More on live music looping
00:53:24 – What are you excited about in the world of Python?
00:55:41 – What do you want to learn next?
01:01:13 – Thanks and goodbye

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What Is Data Engineering and Researching 10 Million Jupyter Notebooks

Fri, 08 Jan 2021 12:00:00 +0000

Are you familiar with the role data engineers play in the modern landscape of data science and Python? Data engineering is a sub-discipline that focuses on the transportation, transformation, and storage of data. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article on data engineering, we talk about a project where researchers downloaded 10 million Jupyter notebooks from Github to gather insights about the current state of data science technology.

We also discuss an article about validating data in Python with the package Cerberus. And this led us to a conversation about a set of coding challenges from Advent of Code.

We also cover several other articles and projects from the Python community including, building my own chess engine, the visual guide to NumPy, a free and open-source alternative to SAP, a library for working with STL files and 3D objects, and is Python really a bottleneck?

Course Spotlight: Building With Django REST Framework

Topics:

00:00:00 – Introduction
00:01:51 – What Is Data Engineering and Is It Right for You?
00:12:07 – Building My Own Chess Engine
00:17:52 – We Downloaded 10,000,000 Jupyter Notebooks From Github: This Is What We Learned
00:28:12 – Video Course Spotlight
00:29:20 – Is Python Really a Bottleneck?
00:34:01 – Validating Data in Python With Cerberus
00:39:04 – NumPy Illustrated: The Visual Guide to NumPy
00:42:54 – erpnext: Free and Open Source Alternative to SAP
00:48:49 – numpy-stl: Library for Working With STL Files and 3D Objects
00:54:54 – Thanks and goodbye

Show Links:

What Is Data Engineering and Is It Right for You? — In this article, you’ll get an overview of the discipline of data engineering. You’ll learn what is and isn’t part of a data engineer’s job, who data engineers work with, and why data engineers play a crucial role in many industries.

Building My Own Chess Engine — Writing your own chess engine is a great way to explore computational complexity and combinatorial aspects of programming. Not to mention it’s pretty fun! Follow along with this reflection on how one coder created his own Chess engine from scratch.

We Downloaded 10,000,000 Jupyter Notebooks From Github: This Is What We Learned — The JetBrains Datalore team downloaded ten million Jupyter Notebooks and analyzed them to determine things like which languages were the most popular, what kinds of content are in notebook cells, and how consistently notebooks can be reproduced. It’s a fascinating look into trends in data science technology!

Is Python Really a Bottleneck? — Python is slow. From one perspective, that is. But what are the true bottlenecks in the data engineering/data processing space, and how does Python compare to other technologies when those factors are considered?

Validating Data in Python With Cerberus — Thanks to an Advent of Code challenge, author Hector Castro was exposed to the Cerberus Python package for data validation. Get a quick introduction to Cerberus and see Hector’s solution to an Advent of Code challenge in this quick-yet-informative read.

NumPy Illustrated: The Visual Guide to NumPy — This illustrated guide to NumPy is a great way to learn NumPy or brush up on the package. Full of great visual aides, this tutorial covers all the basics and more!

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2020 Real Python Articles in Review

Fri, 25 Dec 2020 12:00:00 +0000

It’s been quite the year! The Real Python team has written, edited, curated, illustrated, and produced a mountain of Python articles this year. We also upgraded the site and membership with office hours, transcripts, this podcast, and much more.

We are joined by two members of the Real Python team, David Amos and Joanna Jablonski. We wanted to share a year-end wrap-up with a collection of articles that showcase a diversity of Python topics and the quality of what our team created this year.

Joanna and David help to shepherd articles through the multi-stage editing process. They make sure articles not only impart crucial Python knowledge but also provide a thorough didactic experience.

We hope you enjoy this review and as a programming note, there won’t be an episode next week, but we will be back the following week, and look forward to bringing you a year full of great guests, topics, articles, and projects.

Course Spotlight: Python Turtle for Beginners

Topics:

00:00:00 – Introduction
00:01:41 – Joanna visits the show
00:04:28 – Pandas Project: Make a Gradebook With Python & Pandas
00:07:18 – Build Physical Projects With Python on the Raspberry Pi
00:11:32 – Python Practice Problems: Get Ready for Your Next Interview
00:15:05 – Data Version Control With Python and DVC
00:19:02 – What Are Python Wheels and Why Should You Care?
00:22:57 – Video Course Spotlight
00:23:58 – Python import: Advanced Techniques and Tips
00:26:33 – Hands-On Linear Programming: Optimization With Python
00:29:47 – Customize the Django Admin With Python
00:33:51 – The Python return Statement: Usage and Best Practices
00:36:12 – Python GUI Programming With Tkinter
00:46:47 – Thanks and goodbyes

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Podcast Episodes Referenced:

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How Python Manages Memory and Creating Arrays With np.linspace

Fri, 18 Dec 2020 12:00:00 +0000

Have you wondered how Python manages memory? How are your variables stored in memory, and when do they get deleted? This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article on Python memory management, we also talk about another article about creating even and non-even spaced arrays in Python with np.linspace.

We share an article titled “The Unholy Way of Using Virtual Environments”. This leads to a discussion on how to structure the directories around a virtual environment.

We also cover several other articles and projects from the Python community including, storing a list in an int, why you should use an ORM (Object Relational Manager), unraveling not in Python, an open-source Python fuzzer, and Python static website generators.

Course Spotlight: How Python Manages Memory

Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

Topics:

00:00:00 – Introduction
00:01:38 – np.linspace(): Create Evenly or Non-Evenly Spaced Arrays
00:05:20 – The Unholy Way of Using Virtual Environments
00:17:33 – Storing a list in an int
00:24:48 – Why Should You Use an ORM (Object Relational Mapper)?
00:32:30 – Video Course Spotlight
00:33:24 – Unravelling not in Python
00:40:03 – How Python Manages Memory
00:45:28 – Follow-up from Jupylet
00:46:51 – Announcing the Atheris Python Fuzzer
00:50:42 – nikola: Static Website and Blog Generator

Show Links:

np.linspace(): Create Evenly or Non-Evenly Spaced Arrays – In this tutorial, you’ll learn how to use NumPy’s np.linspace() effectively to create an evenly or non-evenly spaced range of numbers. You’ll explore several practical examples of the function’s many uses in numerical applications.

The Unholy Way of Using Virtual Environments – If you’ve used virtual environments before, you may have created a venv/ folder inside the root directory of your project. This is standard, but has some downsides. Have you every thought about reversing this and putting your project inside your venv/ folder?

Storing a list in an int – For a fun exercise, learn how you can leverage Python’s unlimited integer precision to encode and store lists of any size as a single integer. Because, why not?

Why Should You Use an ORM (Object Relational Mapper)? – Budding web developers learning Model-View-Controller frameworks are taught that they should use an Object Relational Mapper (ORM) to interface with their databases. But the “why” is often brushed aside or omitted entirely, leaving a fledgling programmer with burning questions like “What are ORMs, anyway?” and “What problems do they solve?”

Unravelling not in Python – In the next blog post in his series about Python’s syntactic sugar, Brett Cannon tackles what would seem to be a very simple bit of syntax, but which actually requires diving into multiple layers to fully implement: not.

How Python Manages Memory – Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

Announcing the Atheris Python Fuzzer – Get started with fuzzing, a technique for automatically finding bugs in Python code by repeatedly trying various inputs to your program, using Google’s newly open-sourced Python fuzzer called Atheris.

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Generators, Coroutines, and Learning Python Through Exercises

Fri, 11 Dec 2020 12:00:00 +0000

Have you started to use generators in Python? Are you unsure why you would even use one over a regular function? How do you use the special “send” method and the “yield from” syntax? This week on the show, we have Reuven Lerner to talk about his PyCon Africa 2020 talk titled “Generators, coroutines, and nanoservices.”

Reuven helps developers around the world become more fluent in Python. We talk about some of his teaching techniques and also how he continues to learn. Reuven is a believer in the continued practice of Python through exercises. We discuss his book “Python Workout” and his Weekly Python Exercise courses.

Course Spotlight: Python Generators 101

In this step-by-step course, you’ll learn about generators and yielding in Python. You’ll create generator functions and generator expressions using multiple Python yield statements. You’ll also learn how to build data pipelines that take advantage of these Pythonic tools.

Topics:

00:00:00 – Introduction
00:01:38 – Reuven’s web site: Teaching Python and Data Science around the world
00:03:39 – Training remotely and screen-time overload
00:07:03 – Info-tainment and sparking engagement
00:11:05 – PyCon Africa 2020 - Generators, Coroutines, and Nanoservices
00:13:34 – Using dis() - disassembler for Python bytecode
00:15:09 – Exceptions as signals, not exclusively for errors
00:17:39 – What makes generator functions different?
00:22:05 – Using .send() and creating a coroutine
00:23:39 – Sponsor: Scout APM
00:24:40 – Stopping the generator with .throw()
00:26:28 – Additional uses for generators
00:30:03 – Python Workout: 50 ten-minute exercises
00:34:22 – A combination of techniques and sources for continued learning
00:40:15 – Weekly Python Exercise
00:42:02 – Video Course Spotlight
00:43:08 – Bachelor degree in CS, but a Doctorate in learning sciences
00:48:26 – What are additional techniques you use to keep learning?
00:50:18 – Teaching is a great way to keep learning
00:51:28 – What areas of data science are you focusing on?
00:53:22 – The Business of Freelancing: podcast
00:56:26 – What is something you thought you knew about Python, but were wrong about?
00:59:24 – What is something you are excited about in the world of Python?
01:02:18 – Conferences and user groups
01:05:03 – Thanks and goodbye

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Looping With enumerate() and Python GUIs With PyQt

Fri, 04 Dec 2020 12:00:00 +0000

If you’re coming to Python from a different language, you may not know about a useful tool for working with loops, Python’s built-in enumerate function. This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article covering the details of the enumerate function, we also talk about another article about constructing Python graphical user interface elements in PyQt.

David shares a couple of resources for data scientists, including an article about skills not taught in data science boot camps, and a project for creating synthetic data.

We also cover several other articles and projects from the Python community including, an update about youtubedl, hunting for malicious packages on PyPI, using Python’s bisect module, 73 examples to help you master f-strings, and game programming in Jupyter notebooks.

Course Spotlight: Formatting Python Strings

In this course, you’ll see two items to add to your Python string formatting toolkit. You’ll learn about Python’s string format method and the formatted string literal, or f-string. You’ll learn about these formatting techniques in detail and add them to your Python string formatting toolkit.

Topics:

00:00:00 – Introduction
00:01:53 – The youtube-dl Repository Has Been Restored on GitHub With Help From the Electronic Frontier Foundation
00:04:12 – Python enumerate(): Simplify Looping With Counters
00:07:24 – Hunting for Malicious Packages on PyPI
00:14:31 – Sponsor: Scout APM
00:15:31 – Using Python’s bisect module
00:19:00 – 73 Examples to Help You Master Python’s f-Strings
00:21:35 – 10 Python Skills They Don’t Teach in Bootcamp
00:27:32 – Video Course Spotlight
00:28:28 – Python and PyQt: Creating Menus, Toolbars, and Status Bars
00:33:51 – SDV: Synthetic Data Generation for Tabular, Relational, Time Series Data
00:38:19 – jupylet: Game Programming in Jupyter Notebooks
00:42:59 – Thanks and goodbye

Show Links:

The youtube-dl Repository Has Been Restored on GitHub With Help From the Electronic Frontier Foundation

Python enumerate(): Simplify Looping With Counters – Once you learn about for loops in Python, you know that using an index to access items in a sequence isn’t very Pythonic. So what do you do when you need that index value? In this tutorial, you’ll learn all about Python’s built-in enumerate(), where it’s used, and how you can emulate its behavior.

Hunting for Malicious Packages on PyPI – Jordan Wright installed every package on PyPI to look for malicious content. And he didn’t just inspect code, he actually ran the packages. Brave soul! Learn how he set-up this project and what he learned on his adventure.

Using Python’s bisect module – Python’s bisect module has tools for searching and inserting values into sorted lists. It’s one of his “batteries-included” features that often gets overlooked, but can be a great tool for optimizing certain kinds of code.

73 Examples to Help You Master Python’s f-Strings – f-Strings might be one of the most beloved features in Python 3.6+. Here are 73 examples of how to use f-strings to improve your Python code.

10 Python Skills They Don’t Teach in Bootcamp – Here are ten practical and little-known pandas tips to help you take your skills to the next level.

Python and PyQt: Creating Menus, Toolbars, and Status Bars – In this step-by-step tutorial, you’ll learn how to create, customize, and use Python menus, toolbars, and status bars for creating GUI applications using PyQt.

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Teaching Python and Finding Resources for Students

Fri, 27 Nov 2020 12:00:00 +0000

One of the best ways to learn something well is to teach it. This week on the show, we have Kelly Schuster-Paredes and Sean Tibor from the Teaching Python podcast.

Sean and Kelly teach middle school students Python and share their art and science of teaching Python on their podcast. They wanted to come on the show to talk about the Real Python articles, quizzes, and other resources they use when teaching their students.

We also talk about teaching students how to research topics and use things like advanced search with Google. We discuss using cloud-based tools like collaborative notebooks and some of the core Python concepts students need for a solid foundation.

Kelly and Sean also talk about how the changes to teaching over the past year have had some unexpected benefits. They also talk about a few recent guests and topics covered on their podcast.

Course Spotlight: Basic Data Types in Python

In this course, you’ll learn the basic data types that are built into Python, like numbers, strings, and Booleans. You’ll also get an overview of Python’s built-in functions.

Topics:

00:00:00 – Introduction
00:02:17 – Why did you pick Python for the curriculum?
00:05:48 – Is there a particular IDE or editor you use when teaching?
00:07:35 – Is it helpful using cloud based tools with students?
00:10:02 – What Real Python resources are you using in the classroom?
00:11:48 – Using Google to find good resources
00:18:31 – Sponsor: Linode
00:19:16 – Other Real Python materials
00:27:58 – What video content works with students?
00:30:33 – Video Course Spotlight
00:31:30 – Recent topics and guests on Teaching Python
00:34:39 – Are code samples helpful when teaching?
00:37:06 – Using long form tutorials to demonstrate their learning
00:40:00 – What are you excited about in the world of Python (Sean)?
00:41:02 – What do you want to learn next (Sean)?
00:41:57 – What are you excited about in the world of Python (Kelly)?
00:45:28 – What do you want to learn next (Kelly)?
00:46:59 – Other considerations in selecting Python over Javascript or Swift
00:48:50 – Thanks and goodbye

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Sentiment Analysis, Fourier Transforms, and More Python Data Science

Fri, 20 Nov 2020 12:00:00 +0000

Are you interested in learning more about Natural Language Processing? Have you heard of sentiment analysis? This week on the show, Kyle Stratis returns to talk about his new article titled, Use Sentiment Analysis With Python to Classify Movie Reviews. David Amos is also here, and all of us cover another batch of PyCoder’s Weekly articles and projects.

Kyle discusses an article about distance metrics for machine learning. David shares a Real Python article about Python signal processing and Fourier transforms with scipy.fft. We also cover several other articles and projects from the Python community including, simulating real-world processes in Python with SimPy, working with Microsoft Excel using Python and OpenPyXL, why running code during import is a bad idea, what I wish I knew as a junior dev, the Raspberry Pi 400 personal computer, dynamic sky replacement and harmonization in videos with SkyAR.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

Topics:

00:00:00 – Introduction
00:02:56 – Use Sentiment Analysis With Python to Classify Movie Reviews
00:09:49 – OpenPyXL: Working with Microsoft Excel Using Python
00:12:41 – An Illustration of Why Running Code During Import Is a Bad Idea
00:16:52 – Distance Metrics for Machine Learning
00:22:52 – Sponsor: linode.com
00:22:52 – What I Wish I Knew as a Junior Dev
00:35:29 – Fourier Transforms With scipy.fft: Python Signal Processing
00:39:44 – Simulating Real-World Processes in Python With SimPy
00:43:30 – Video Course Spotlight
00:44:35 – Raspberry Pi 400 Personal Computer Kit Now Available
00:49:55 – SkyAR: Dynamic Sky Replacement and Harmonization in Videos
00:52:04 – Creating an Idea Factory with Roam Research
00:56:02 – Thanks and goodbye

Show Links:

Use Sentiment Analysis With Python to Classify Movie Reviews – In this tutorial, you’ll learn about sentiment analysis and how it works in Python. You’ll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative.

OpenPyXL: Working with Microsoft Excel Using Python – Ah, Excel. Everyone loves to hate it. But let’s face it. Excel is one of the most popular pieces of software ever written. But you love Python, not Excel, which is why you might want to learn OpenPyXL.

An Illustration of Why Running Code During Import Is a Bad Idea (And How It Happens Anyway) – Code that runs when a module is imported is usually a code smell. But sometimes there’s no way around it.

Distance Metrics for Machine Learning – Many machine learning algorithms can be summarized as transforming data to n-dimensional vectors and computing similarity between points by means of some distance metric. This article explores four of these metrics—the Euclidean, Manhattan, Minkowski, and Hamming distances—and how to compute them with Python.

What I Wish I Knew as a Junior Dev – Some of these are things even senior devs need to be reminded of sometimes!

Fourier Transforms With scipy.fft: Python Signal Processing – In this tutorial, you’ll learn how to use the Fourier transform, a powerful tool for analyzing signals with applications ranging from audio processing to image compression. You’ll explore several different transforms provided by Python’s scipy.fft module.

Projects:

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Security and Authorization in Your Python Web Applications

Fri, 13 Nov 2020 12:00:00 +0000

So you built a web application in Python. Now how are you going to authorize users? Security goes beyond authentication. Who gets to do what, where, and when? This week on the show, we have Sam Scott, chief technology officer from Oso. Oso is an open-source policy engine for authorization that you embed in your application.

Sam talks about the typical security and authorization challenges developers face. He discusses building an engine on top of your existing Flask or Django app. We cover the concept of policies, business logic, and some common paradigms.

Course Spotlight: Exploring HTTPS and Cryptography in Python

Topics:

00:00:00 – Introduction
00:01:32 – Sam’s math background
00:03:11 – What is Sage?
00:04:24 – What is post-quantum cryptography?
00:05:19 – Getting Oso started, authentication vs authorization.
00:10:01 – What is a policy engine?
00:12:57 – Confusing business logic with authorization
00:17:09 – Sponsor: Techmeme Ride Home Podcast
00:17:38 – Pip installing Oso, adding to Flask or Django
00:21:15 – What are common security concerns for developers?
00:25:41 – What are security concerns users have?
00:27:14 – What are the worst security issues you’ve found in a Python app?
00:30:12 – Video Course Spotlight
00:31:32 – What are other common authorization “gotchas”?
00:37:16 – Additional Oso resources
00:39:36 – What does writing in Polar look like?
00:42:00 – Are there authorization paradigms?
00:46:02 – What are you excited about in the world of Python?
00:50:05 – What do you want to learn next?
00:50:49 – Thanks and goodbye

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The Python Modulo Operator & Managing Data With SQLite and SQLAlchemy

Fri, 06 Nov 2020 12:00:00 +0000

Are you ready to move beyond flat files for your data in Python? Maybe you’re not sure where to start with databases and SQL. This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We cover a Real Python article about managing data with SQLite and SQLAlchemy.

David explores the intricacies of using the modulo operator (%). We also cover several other articles and projects from the Python community including, how to shoot yourself in the foot with python, exploring fractals on a cloud computer, the DMCA takedown request for youtube-dl, python for feature film, an online multiplayer text-based game framework, and a sorting algorithms visualizer.

Course Spotlight: Playing and Recording Sound in Python

Topics:

00:00:00 – Introduction
00:01:35 – How to Shoot Yourself in the Foot With Python, Part 1
00:11:31 – Data Management With Python, SQLite, and SQLAlchemy
00:19:00 – Sponsor: Techmeme Ride Home Podcast
00:19:29 – Exploring Fractals on a Cloud Computer
00:23:28 – The youtube-dl GitHub Repo Has Received a DMCA Takedown Request From the RIAA
00:29:09 – Video Course Spotlight
00:30:12 – Python Modulo in Practice: How to Use the % Operator
00:36:29 – Python For Feature Film
00:44:01 – evennia: Online Multiplayer Text-Based Game Framework
00:47:11 – Sorting-Algorithms-Visualizer: See How Sorting Algorithm Works With Pygame
00:51:55 – Thanks and goodbye

Show Links:

How to Shoot Yourself in the Foot With Python, Part 1 – If you’re new to Python, you might find yourself confused by some of the situations described in this article. Learn about five mistakes you could make, why they happen, and how to fix them.

Data Management With Python, SQLite, and SQLAlchemy – In this tutorial, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Exploring Fractals on a Cloud Computer – Fractals might be some of the most interesting mathematical structures to study and to visualize. Learn what fractals are and how to create beautiful fractal animations with Python.

The youtube-dl GitHub Repo Has Received a DMCA Takedown Request From the RIAA

Python Modulo in Practice: How to Use the % Operator – In this tutorial, you’ll learn about the Python modulo operator (%). You’ll look at the mathematical concepts behind the modulo operation and how the modulo operator is used with Python’s numeric types. You’ll also see ways to use the modulo operator in your own code.

Python For Feature Film – A look into how Python is used to bring your favorite movies to the big screen.

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Going Beyond the Basic Stuff With Python and Al Sweigart

Fri, 30 Oct 2020 12:00:00 +0000

You probably have heard of the bestselling Python book, “Automate the Boring Stuff with Python.” What are the next steps after starting to dabble in the Python basics? Maybe you’ve completed some tutorials, created a few scripts, and automated repetitive tasks in your life. This week on the show, we have author Al Sweigart to talk about his new book, “Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code.”

We discuss several topics covered in his new book, including using the command line, setting environment variables, formatting code, naming, and starting with version control. We talk about learning Python by creating games and highlight a couple of Python myths. I also ask Al about his earlier books, and about his idea of creating a curriculum around conference talks.

Course Spotlight: Unicode in Python: Working With Character Encodings

Topics:

00:00:00 – Introduction
00:01:35 – Early access release of the new book
00:03:20 – Other books Al has written
00:09:54 – Automate the Boring Stuff as an advice book
00:15:29 – Books about writing Python with games
00:17:36 – Making a book less intimidating
00:19:10 – Helping readers through random things a programmer needs to learn
00:23:09 – Environment variables and the command line
00:28:05 – Naming
00:34:59 – Code formatting
00:36:45 – Why do you enjoy teaching Python concepts with games?
00:42:54 – Video Course Spotlight
00:44:15 – Minimal amount you should know about Git
00:47:08 – Jargon and being clear about terminology
00:50:13 – Al’s first book diving into Object-Oriented Programming
00:59:57 – Python myths covered in the book
01:09:08 – What is something you thought you knew about Python, but were wrong about it?
01:13:12 – What is something you are excited about in the world of Python?
01:18:55 – What do you want to learn next?
01:22:08 – Creating an curriculum from conference talks
01:26:42 – Thanks and goodbye

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Our New "Python Basics" Book & Filling the Gaps in Your Learning Path

Fri, 23 Oct 2020 12:00:00 +0000

Do you have gaps in your Python learning path? If you’re like me, you may have followed a completely random route to learn Python. This week on the show, David Amos is here to talk about the release of the Real Python book, “Python Basics: A Practical Introduction to Python 3”. The book is designed not only to get beginners up to speed but also to help fill in the gaps many intermediate learners may still have.

David has been working on the book for the last two years, and we dive into all the resources that come with it. These include code challenges, quizzes, and multiple projects that are designed to help you cement your learning. We also discuss the people and processes involved in creating, reviewing, and updating the book.

Spotlight: Python Basics: A Practical Introduction to Python 3

Topics:

00:00:00 – Introduction
00:01:36 – Python Basics: A Practical Introduction to Python 3
00:02:39 – How has feedback helped the process?
00:04:17 – Who is the intended audience?
00:06:02 – Covering how to get Python installed on different platforms?
00:11:05 – What topics does the book cover?
00:14:12 – What can be previewed on Real Python?
00:15:03 – What format can you get the book in?
00:18:30 – Code challenges included!
00:21:33 – What other resources are provided to help with cementing your learning?
00:22:41 – What versions of Python are covered?
00:23:08 – How does the book fit into the Real Python learning eco-system?
00:29:35 – Spotlight: How to get a preview of the book!
00:30:38 – What has the writing process been like?
00:33:21 – What does didactic mean, in terms of reviewing materials?
00:39:23 – What were areas you were excited about updating?
00:41:29 – Were there important things you felt needed to be added?
00:45:55 – Who worked on the book?
00:47:13 – What are you excited about in the world of Python?
00:48:13 – What do you want to learn next?
00:49:40 – Thanks and goodbye

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Python Return Statement Best Practices and Working With the map() Function

Fri, 16 Oct 2020 12:00:00 +0000

The Python return statement is such a fundamental part of writing functions. Is it possible you missed some best practices when writing your own return statements? This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We also talk functional programming again with an article on the Python map function and processing iterables without a loop.

We cover several other articles and projects from the Python community including, interactive data visualization with Pygal, everything you need to know about namedtuples, PEP 638 syntactic macros, python for kids, the new Nvidia Jetson board, and a reinforcement learning project named football.

Topics:

00:00:00 – Introduction
00:01:33 – Interactive Data Visualization in Python With Pygal
00:06:40 – Python’s map(): Processing Iterables Without a Loop
00:12:51 – Everything You Need to Know About Python’s NamedTuples
00:19:46 – PEP 638: Syntactic Macros
00:26:43 – Video Course Spotlight
00:27:57 – The Python return Statement: Usage and Best Practices
00:34:42 – Python for Kids
00:38:03 – Build a Face Recognition System With the Nvidia Jetson Nano
00:42:32 – football: Reinforcement Learning Environment
00:45:51 – Thanks and goodbye

Show Links:

Interactive Data Visualization in Python With Pygal – Pygal is an overlooked library for creating interactive plots that can be turned into SVGs with an optimal resolution for printing or displaying on webpages. Learn how it works in this introductory tutorial.

Python’s map(): Processing Iterables Without a Loop – In this step-by-step tutorial, you’ll learn how Python’s map() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace map() in a Pythonic and efficient way.

Everything You Need to Know About Python’s NamedTuples – Are you using NamedTuple in your code? If you aren’t, learn what they are and why you should consider using them in this comprehensive tutorial.

PEP 638: Syntactic Macros – This brand new PEP, which is still in draft mode, proposes adding support for syntactic macros to Python. Syntactic macros are compile-time functions that extend the language’s syntax without adding any new complexity to the language as a whole.

The Python return Statement: Usage and Best Practices – In this step-by-step tutorial, you’ll learn how to use the Python return statement when writing functions. Additionally, you’ll cover some good programming practices related to the use of return. With this knowledge, you’ll be able to write readable, robust, and maintainable functions in Python.

Python for Kids – In this ten part series, senior software engineer Kevin Thomas presents a kid-friendly comprehensive Python development tutorial utilizing a micro:bit development board. The GitHub repo contains all of the sample code as well as links to each tutorial in the series on LinkedIn. The first seven parts are published and the last three are coming soon!

Projects:

Build a Face Recognition System With the Nvidia Jetson Nano 2GB and Python – The Nvidia Jetson Nano is a single board computer similar to a Raspberry Pi. The Jetson Nano really packs a punch, however, thanks to its onboard Nvidia Maxwell GPU.

football: Reinforcement Learning Environment Where Agents Learn to Play Football

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Exploring the New Features of Python 3.9

Fri, 09 Oct 2020 12:00:00 +0000

Python 3.9 has arrived! This week on the show, former guest and Real Python author Geir Arne Hjelle returns to talk about his recent article, “Python 3.9: Cool New Features for You to Try”. Also joining the conversation is Real Python video course instructor and author Christopher Trudeau. Christopher has created a video course, which was released this week also, based on Geir Arne’s article. We talk about time zones, merging dictionaries, the new parser, type hints, and more.

Geir Arne and Christopher not only cover the new features, but they also offer advice about ways you might incorporate them into your code. We discuss what you should think about before updating your code.

Course Spotlight: Cool New Features in Python 3.9

Topics:

00:00:00 – Introduction
00:02:02 – Proper Time Zone support
00:05:38 – What to do if your OS does not have tzdata?
00:07:11 – How do you add time zone info to your Python code?
00:08:24 – Historic changes to time zones
00:10:48 – New operators for updating dictionaries
00:15:44 – Geir Arne’s PyCon 2020 decorators tutorial
00:18:02 – Changes to decorator syntax
00:24:08 – Annotated type hints
00:32:36 – Video Course Spotlight
00:33:26 – The PEG parser
00:37:51 – Potential for new enhancements in upcoming releases
00:42:40 – String methods to remove prefixes and suffixes
00:44:22 – Type hint lists and dictionaries directly
00:47:29 – Topological Sort
00:52:03 – Greatest Common Divisor (GCD) and Least Common Multiple (LCM)
00:53:50 – New HTTP status codes
00:58:29 – Should you upgrade?
01:07:58 – Potential issue with Python 3.10 versioning
01:10:18 – What are you excited about in the world of Python?
01:12:52 – What do you want to learn next?
01:14:08 – Thanks and goodbyes

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Resolving Package Dependencies With the New Version of Pip

Fri, 02 Oct 2020 12:00:00 +0000

If you use Python, then you probably have used pip to install additional packages from the Python package index. Part of the magic behind pip is the dependency resolver, and there is a new version of it in the latest version of pip. This week on the show, we have Sumana Harihareswara and Georgia Bullen, who have been working on the recent releases of pip. Sumana is the project manager for pip, and Georgia has been working on pip’s user experience (UX).

The resolver is how pip determines what to install, and in what order, based on package requirements. We talk about how you can help, from updating to the latest release, testing out the new resolver with your projects, and answering surveys about your experiences. A ton of work has gone into making the updates this year. We also talk about the funding of projects like this in the open-source community.

Course Spotlight: A Beginner’s Guide to Pip

Show Topics:

00:00:00 – Introduction
00:01:41 – Pip updates and changes to dependency resolver
00:08:49 – Different types of wheels
00:11:12 – Pinning package dependencies
00:13:19 – Work on the user experience (UX) of pip
00:15:45 – Documentation at Python packaging authority and thanks to Thea Flowers
00:16:21 – Types of issues that need resolving
00:20:48 – Need for reporting issues
00:23:41 – Pip usability survey and dependency recipes to test
00:27:21 – Call out to open source maintainers to test
00:29:32 – Video Course Spotlight
00:30:43 – How is this UX work different from Simply Secure
00:34:59 – How do you present errors to users?
00:41:14 – Pip release timeline for 2020 and into 2021
00:46:38 – The dynamics of responsibility and power
00:49:43 – What’s involved in getting more funding into open source?
00:54:10 – Grant writing for the PSF
00:57:53 – Call to action: How to help with pip?
01:00:54 – What are you excited about in the world of Python?
01:04:52 – What do you want to learn next?
01:08:22 – Thanks and goodbyes

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Using Pylance to Write Better Python Inside of Visual Studio Code

Fri, 25 Sep 2020 12:00:00 +0000

A big decision a developer has to make is what tool to use to write code? Would you like an editor that understands Python, and is there to help with suggestions, definitions, and analysis of your code? For many developers, its the free tool, Visual Studio Code. This week on the show, we have Savannah Ostrowski, program manager for the Python Language Server and Python in Visual Studio. We discuss Pylance, a new language server with fast, feature-rich language support for Python in VS Code.

Savannah explains what a language server is and the types of features it can provide. This includes type information, code completion, automatic-imports, dead code analysis, code navigation, and more. We also have a discussion about type checking in Python, which led to how Pylance leverages the static type checking tool Pyright, and what are type stubs (.pyi files).

Course Spotlight: Python Type Checking

Topics:

00:00:00 – Introduction
00:01:47 – Current Role at Microsoft
00:03:01 – Background with Python
00:04:21 – Origins of Pylance
00:06:53 – What is a language server?
00:09:56 – Diving deeper into the features individually
00:14:42 – Code navigation and diagnostics
00:15:43 – Methods of defining types and stub files
00:17:28 – What are examples of stub files?
00:21:16 – Comparing Pyright to Mypy
00:23:56 – Video Course Spotlight
00:25:02 – Auto-imports are a contentious feature
00:28:36 – Code actions and dead code analysis
00:31:46 – Pylance working with Jupyter notebooks in VSCode
00:33:30 – Multiple workspaces
00:36:16 – Why do you like to work on developer tools?
00:39:35 – How does a tool like Pylance help a beginner?
00:42:31 – What are you excited about in the world of Python?
00:46:25 – What do you want to learn next?
00:49:24 – Thanks and goodbyes

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Preparing for an Interview With Python Practice Problems

Fri, 18 Sep 2020 12:00:00 +0000

What is an effective way to prepare for a Python interview? Would you like a set of problems that increase in difficulty to practice and hone your Python skills? This week on the show, we have Jim Anderson to talk about his new Real Python article, “Python Practice Problems: Get Ready for Your Next Interview.” This article provides several problems, which include skeleton code, unit tests, and solutions for you to compare your work.

David Amos also joins us this week, and he has brought another batch of PyCoder’s Weekly articles and projects from the Python community. We cover these topics: Structural Pattern Matching, Common Python Data Structures, A Tax Attorney Uses Python, Discover the Role of Python in Space Exploration, and Five Pairs of Magic Methods in Python That You Should Know.

Course Spotlight: How to Implement a Python Stack

In this course, you’ll learn how to implement a Python stack. You’ll see how to recognize when a stack is a good choice for data structures, how to decide which implementation is best for a program, and what extra considerations to make about stacks in a threading or multiprocessing environment.

Topics:

00:00:00 – Introduction
00:01:37 – Python Practice Problems
00:08:19 – Structural Pattern Matching
00:14:16 – Common Python Data Structures
00:17:49 – Video Course Spotlight
00:18:42 – A Tax Attorney Uses Python
00:23:36 – Discover the Role of Python in Space Exploration
00:26:33 – Thanks for the Reviews!
00:27:28 – 5 Pairs of Magic Methods in Python That You Should Know
00:36:56 – python-adventure: Original Colossal Caves Adventure Game
00:41:06 – clifford: Geometric Algebra for Python
00:43:45 – pippi: Computer Music With Python
00:46:24 – Thanks and Goodbye

Show Links:

Python Practice Problems: Get Ready for Your Next Interview – Are you a Python developer brushing up on your skills before an interview? If so, then this tutorial will usher you through a series of Python practice problems meant to simulate common coding test scenarios.

“Structural Pattern Matching”; for Python, Part 2 – The saga of PEP 622 continues with updates to the proposed structure of the match statement—which has some similarities to a switch statement—and a discussion on the best way to document objections to a PEP.

Common Python Data Structures (Guide) – In this tutorial, you’ll learn about Python’s data structures. You’ll look at several implementations of abstract data types and learn which implementations are best for your specific use cases.

A Tax Attorney Uses Python – See how one tax attorney uses Python to automate grueling and repetitive tasks and improve his business. While the article is non-technical, it’s always fun to see how Python is used in diverse fields.

Discover the Role of Python in Space Exploration – In this learning path from Microsoft, you’ll get an introduction to Python, and be inspired to learn, discover, and create using Python-based data science and machine learning to help generate knowledge about the world beyond Earth.

5 Pairs of Magic Methods in Python That You Should Know – Magic, or “dunder,” methods are an important part of creating custom classes in Python. Learn about some commonly used magic methods by exploring hem in pairs that are frequently used together.

python-adventure: Original Colossal Caves Adventure Game, but in Python 3

clifford: Geometric Algebra for Python

pippi: Computer Music With Python

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5 Years Podcasting Python With Michael Kennedy: Growth, GIL, Async, and More

Fri, 11 Sep 2020 12:00:00 +0000

Why is Python pulling in so many new programmers? Maybe some of that growth is from Python being a full-spectrum language. This week on the show we have Michael Kennedy, the host of the podcast “Talk Python to Me”. Michael reflects on five years of podcasting about Python, and many of the changes he has seen in the Python landscape.

We discuss several stories about the different ways Python is being used, and how that is drawing in many new programmers. Michael covers some potential Python stumbling blocks of Async, the Python Global Interpreter Lock (GIL), building desktop apps, and type checking. We also talk about how podcasts can act as a form of language immersion.

Course Spotlight: Editing Excel Spreadsheets in Python With openpyxl

Topics:

00:00:00 – Introduction
00:01:34 – Changes in the Python landscape over the last 5 years
00:08:09 – Changing the perspective from scripts to applications
00:10:48 – Python as a full-spectrum language
00:17:13 – What are the areas of growth for Python
00:22:09 – Stories highlights from Talk Python
00:27:48 – Stack Overflow Developer Surveys
00:33:22 – Enterprise use and contributions to Python
00:38:04 – Video Course Spotlight
00:39:20 – A monk learns Python and OpenCV
00:48:18 – Things that have been hard to do in Python
00:52:07 – How is the GIL part of the problem?
00:56:37 – Leave a review, it will help the show, Thanks!
00:57:04 – More on Async in Python
01:03:38 – Recent courses developed
01:10:05 – Who listens to a Python podcast?
01:13:06 – What are you excited about in the world of Python?
01:17:46 – What do you want to learn next?
01:23:17 – What is something you thought you knew about Python, but were wrong about it?
01:26:58 – Thanks and goodbye

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Data Version Control in Python and Real Python Video Transcripts

Fri, 04 Sep 2020 12:00:00 +0000

Wouldn’t it be nice to a use a form of version control for data? Something that would allow you to track and version your datasets and models. Well, that’s what the tool called DVC is designed to do. This week on the show, David Amos is here and he’s brought another batch of PyCoder’s Weekly articles and projects.

David starts with a Real Python article titled, “Data Version Control With Python and DVC”. We also cover several other articles and projects from the Python community including: where to get exposure to well-written code, delegation – composition and inheritance, good Python project ideas for high school students, never run Python in your downloads folder, and more.

We also have a special guest this week. I talk to Sadie Parker, who recently joined the Real Python team to help create and edit transcripts for all the Real Python video courses. We talk about how to take advantage of all the features this new resource provides. Sadie also discusses how she uses Python to speed up and simplify the editing process. The transcripts and closed captions are now live on the website for all new courses, and we are working through the back catalog.

Course Spotlight: Parallel Iteration With Python’s zip() Function

Topics:

00:00:00 – Introduction
00:02:01 – Data Version Control With Python and DVC
00:08:57 – Where Do You Go to Get Exposure to Well-Written Code?
00:13:13 – Delegation: Composition and Inheritance in Object-Oriented Programming
00:21:46 – What Are Some Good Python Project Ideas for High School Students?
00:26:53 – Video Course Spotlight
00:27:48 – Never Run Python in Your Downloads Folder
00:35:55 – present: A Terminal-Based Presentation Tool With Colors and Effects
00:37:36 – Mini Raspberry Pi Boston Dynamics–inspired Robot
00:41:48 – Sadie Parker and Transcripts for Real Python Video Courses
00:59:26 – Thanks and Goodbye

Show Links:

Data Version Control With Python and DVC – In this tutorial, you’ll learn to use DVC, a powerful tool that solves many problems encountered in machine learning and data science. You’ll find out how data version control helps you to track your data, share development machines with your team, and create easily reproducible experiments!

Where Do You Go to Get Exposure to Well-Written Code? – Maybe someone needs to start a Python reading club…

Delegation: Composition and Inheritance in Object-Oriented Programming – Delegation is often considered one of the three pillars of object-oriented programming. Learn how to use this powerful concept in Python.

What Are Some Good Python Project Ideas for High School Students? – Honestly, some of these ideas are good for any beginning Pythonista!

Never Run Python in Your Downloads Folder – Learn about security issues that exploit how Python interacts with PATH and why you should always think twice about your current working directory.

present: A Terminal-Based Presentation Tool With Colors and Effects

Mini Raspberry Pi Boston Dynamics–inspired Robot – See how one Redditor taught themselves robotics by building a miniature version of a Boston Dynamic’s robot dog “Spot” using a Raspberry Pi, C++, and Python.

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Options for Packaging Your Python Application: Wheels, Docker, and More

Fri, 28 Aug 2020 12:00:00 +0000

Have you wondered, how should I package my Python code? You’ve written the application, but now you need to distribute it to the machines it’s intended to run on. It depends on what the code is, the libraries it depends on, and with whom do you want to share it. This week on the show we have Itamar Turner-Trauring, creator of the website pythonspeed.com. We discuss his article “Options for Packaging Your Python Code: Wheels, Conda, Docker, and More,” covering the how of sharing your code.

Itamar also briefly discusses his Python memory profiler named Fil. We talk about his recent PyCon 2020 presentation, “Small Big Data: What to do When Your Data Doesn’t Fit in Memory.” We also cover several of the resources available on his website for data scientists that want to get deeper into Docker.

Course Spotlight: Python Coding Interviews: Tips & Best Practices

Topics:

00:00:00 – Introduction
00:01:36 – About the naming of pythonspeed.com
00:03:47 – Fil - Python Memory Profiler
00:06:44 – Small Big Data: What to do when your data doesn’t fit in memory - PyCon 2020
00:12:17 – Options for packaging your Python code: Wheels, Conda, Docker, and more
00:15:13 – Python Wheels
00:19:22 – pipx: Install and Run Python Applications in Isolated Environments
00:20:52 – PEX, and friends
00:24:51 – System Package, RPM or DEB
00:29:42 – Conda Packaging and conda-forge
00:36:09 – Video Course Spotlight
00:37:23 – Self-contained executable: PyInstaller, PyOxidizer, Briefcase
00:43:45 – Container image (Docker, Singularity)
00:54:55 – Why alpine may not be the best choice
01:05:28 – Singularity
01:07:50 – What are you excited about in the world of Python?
01:10:40 – What do you want to learn next?
01:13:54 – Thanks and Goodbye

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Python Wheels and Pass by Reference in Python

Fri, 21 Aug 2020 12:00:00 +0000

Have you wondered what Python wheels are? How are they used to package Python code? Does Python use pass by value or pass by reference? This week on the show, David Amos is here to help answer these questions, and he has brought another batch of PyCoder’s Weekly articles and projects.

We talk about an article called “What are Python Wheels, and Why Should You Care.” David talks about a Real Python article about pass by reference in Python. We cover several other articles and projects from the Python community including: transcribing speech to text, 4 powerful features Python is still missing, 10 awesome pythonic one-liners, and even more options for packaging your Python code.

Course Spotlight: Practical Recipes for Working With Files in Python

In this course, you’ll learn how you can work with files in Python by using built-in modules to perform practical tasks that involve groups of files, like renaming them, moving them around, archiving them, and getting their metadata.

Topics:

00:00:00 – Introduction
00:01:25 – 4 Powerful Features Python Is Still Missing
00:14:41 – What Are Python Wheels and Why Should You Care?
00:23:58 – 10 Awesome Pythonic One-Liners Explained
00:38:11 – Video Course Spotlight
00:39:14 – How to Transcribe Speech Recordings Into Text With Python
00:43:34 – Pass by Reference in Python: Background and Best Practices
00:49:54 – Options for Packaging Your Python Code: Wheels, Conda, Docker, and More
00:55:53 – PyOxidizer: A Modern Python Application Packaging and Distribution Tool
01:00:13 – Python and PDF: A Review of Existing Tools
01:04:01 – Thanks and Goodbye

Show Links:

4 Powerful Features Python Is Still Missing – Python doesn’t have true constants, nor does it implement features like tail recursion optimization that many compiled languages employ. Find out what other features Python is “missing” when compared to other languages, and why the core developers haven’t added these features to the language.

What Are Python Wheels and Why Should You Care? – In this tutorial, you’ll learn what Python wheels are and why you should care as both a developer and end user of Python packages. You’ll see how the wheel format has gained momentum over the last decade and how it has made the package installation process faster and more stable.

10 Awesome Pythonic One-Liners Explained – Some things in Python are just better on one line.

How to Transcribe Speech Recordings Into Text With Python – Learn to transcribe speech in recordings like MP3s into text with Python and AssemblyAI’s API

Pass by Reference in Python: Background and Best Practices – In this tutorial, you’ll explore the concept of passing by reference and learn how it relates to Python’s own system for handling function arguments. You’ll look at several use cases for passing by reference and learn some best practices for implementing pass-by-reference constructs in Python.

Options for Packaging Your Python Code: Wheels, Conda, Docker, and More – There’s a lot of ways to package your Python code. Find out which one is right for you.

PyOxidizer: A Modern Python Application Packaging and Distribution Tool

Python and PDF: A Review of Existing Tools – The ultimate list of PDF tools in Python.

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Create Cross-Platform Python GUI Apps With BeeWare

Fri, 14 Aug 2020 12:00:00 +0000

Do you want to distribute your Python applications to other users who don’t have or even use Python? Maybe you’re interested in seeing your Python application run on iOS or Android mobile devices. This week on the show we have Russell Keith-Magee, the founder and maintainer of the BeeWare project. Russell talks about Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.

We spend some time digging into BeeWare’s cross-platform widget toolkit named Toga. Russell talks about some of the intricacies of converting graphical user interface components from across multiple computing platforms. If you’re interested in contributing to an open source project, he discusses how you could get involved in the project. We also talk about the struggle of getting funding for open source projects.

Course Spotlight: Python Decorators 101

Topics:

00:00:00 – Introduction
00:01:36 – History of the BeeWare project
00:05:20 – Is having separate tools helpful?
00:07:06 – Gaming frameworks in Briefcase
00:09:05 – How is Briefcase different from similar tools?
00:14:36 – Platform considerations 32bit vs 64bit
00:16:27 – Coming Mac hardware platform changes
00:18:20 – TOML: Background, use in Python projects, and Briefcase
00:26:29 – How is the project going for different platforms?
00:32:09 – Android hardware as a developer test device
00:36:14 – Making Toga (GUI) cross platform
00:41:21 – What type of interface widgets are available in Toga?
00:44:26 – Video Course Spotlight
00:45:37 – Distribution to the web as a platform
00:49:54 – What is WASM (Web Assembly)?
00:53:26 – Version numbering for BeeWare projects
00:56:23 – What is your day-to-day involvement in the project?
00:58:32 – How would someone get involved in the project?
01:05:15 – Funding open-source projects
01:13:31 – Including a smaller version of Python
01:17:08 – What are you currently excited about?
01:22:17 – What are you interested in learning next?
01:23:07 – Thanks and Goodbye

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Exploring K-means Clustering and Building a Gradebook With Pandas

Fri, 07 Aug 2020 12:00:00 +0000

Do you want to learn the how and when of implementing K-means clustering in Python? Would you like to practice your pandas skills with a real-world project? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

David talks about a Real Python article about how to perform K-means clustering in Python. We also talk about a new project based article on the site about how to create a gradebook using pandas, practicing the skills of importing, merging, and calculating across groups of data. We cover several other articles and projects from the Python community including: JPEG image decoding, object-oriented development with interfaces and mixins, sparking joy with Python, five package picks from Real Python authors, and more.

Course Spotlight: Reading and Writing CSV Files

This course teaches how to read and write data to CSV files using Python’s built in csv module and the pandas library. You’ll learn how to handle standard and non-standard data such as CSV files without headers, or files containing delimiters in the data.

Topics:

00:00:00 – Introduction
00:01:34 – Understanding and Decoding a JPEG Image Using Python
00:08:41 – K-Means Clustering in Python: A Practical Guide
00:12:49 – Pandas Project: Make a Gradebook With Pandas
00:17:54 – Video Course Spotlight
00:18:53 – Interfaces, Mixins and Building Powerful Custom Data Structures in Python
00:32:29 – Sparking Joy With Python
00:43:33 – Python Packages: Five Real Python Favorites
00:52:15 – zxcvbn-python: Dropbox’s Realistic Password Strength Estimator
00:55:43 – Manim: Animation Engine for Explanatory Math Videos
01:00:34 – Thanks and Goodbye

Show Links:

Understanding and Decoding a JPEG Image Using Python – Learn about the JPEG compression algorithm in this comprehensive guide to decoding JPEGs with Python.

K-Means Clustering in Python: A Practical Guide – Learn how to perform k-means clustering in Python. You’ll review evaluation metrics for choosing an appropriate number of clusters and build an end-to-end k-means clustering pipeline in scikit-learn.

Pandas Project: Make a Gradebook With Pandas – With this follow-along Python project, you’ll build a script to calculate grades for a class using pandas. The script will quickly and accurately calculate grades from a variety of data sources. You’ll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics.

Interfaces, Mixins and Building Powerful Custom Data Structures in Python – How to supercharge Python’s built-in data structures and build powerful custom data structures with mixin classes.

Sparking Joy With Python – After a fling with TypeScript, one Python programmer shares some thoughts on keeping the flame alive with Python.

Python Packages: Five Real Python Favorites – In this tutorial, several Real Python authors share Python packages we like to use as alternatives to modules in the standard library. You’ll get to know a number of useful packages, including pudb, requests, parse, dateutil, and typer.

Projects:

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Building PDFs in Python with ReportLab

Fri, 31 Jul 2020 12:00:00 +0000

Have you wanted to generate advanced reports as PDFs using Python? Maybe you want to build documents with tables, images, or fillable forms. This week on the show we have Mike Driscoll to talk about his book “ReportLab - PDF Processing with Python.”

Mike is an author of multiple books about Python, and has recently re-written his Python 101 book. He is also a member of the Real Python team and has written several articles for the site. Along with our discussion about ReportLab and PDFs, Mike talks about being a self-published author. We also talk briefly about his favorite Python GUI framework.

Course Spotlight: How to Work With a PDF in Python

In this step-by-step course, you’ll learn how to work with a PDF in Python. You’ll see how to extract metadata from preexisting PDFs. You’ll also learn how to merge, split, watermark, and rotate pages in PDFs using Python and PyPDF2.

Topics:

00:00:00 – Introduction
00:01:23 – Python 101 book revisions/rewrite
00:04:48 – Python 201 book
00:05:47 – What Python GUI framework do you prefer?
00:12:46 – MouseVsPython YouTube channel
00:14:34 – Why write a ReportLab book?
00:16:11 – Kickstarter and self-publishing books
00:21:38 – Reader feedback about the book
00:22:35 – What other PDF tools are covered in the book?
00:23:48 – Differences with ReportLab Plus
00:25:00 – Flowables and PLATYPUS
00:28:56 – Video Course Spotlight
00:29:49 – What types of projects have you used ReportLab for?
00:35:50 – Creating PDF forms with ReportLab
00:40:21 – LaTeX comparison with ReportLab
00:41:40 – PDFMiner text extraction
00:43:17 – PyFPDF Library for PDF document creation
00:45:28 – Camelot: PDF Table Extraction for Humans
00:47:17 – Working with passwords and encryption - PyPDF2
00:47:56 – What are you excited about in the world of Python?
00:48:47 – Learning OpenCV
00:49:38 – What do you want to learn next in Python?
00:50:20 – Suggestions for Python libraries to read
00:52:11 – Thanks and Goodbye

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Advanced Python Import Techniques and Managing Users in Django

Fri, 24 Jul 2020 12:00:00 +0000

Would you like to clearly understand what’s happening when you use the Python import keyword? Do you want to use modules more effectively to structure your code? Or maybe you’re ready to move to the next level with your Django project by adding user management. This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

We discuss a Real Python article about advanced techniques and tips for using the Python import keyword. David also talks about another recent article on the site about managing users in Django. We cover several other articles and projects from the Python community including: robot programming in Python, f-strings vs .format(), the rise of Python malware, a hardware Python keyboard, and more.

Course Spotlight: Grow Your Python Portfolio With 13 Intermediate Project Ideas

Get started on 13 Python project ideas that are just right for intermediate Python developers. They’ll challenge you enough to help you become a better Pythonista.

Topics:

00:00:00 – Introduction
00:01:31 – The Non-Return of the Python print statement
00:05:46 – Python Malware on the Rise
00:13:46 – Get Started With Django Part 2: Django User Management
00:19:01 – Why Do People Use .format() When f-Strings Exist?
00:27:41 – Video Course Spotlight
00:28:45 – A Beginner’s Guide to Robot Programming With Python
00:36:17 – Python import: Advanced Techniques and Tips
00:40:56 – toolz: A Functional Standard Library for Python
00:46:43 – python-keyboard: A Hand-Wired USB & BLE Keyboard Powered by Python
00:51:09 – Thanks and Goodbye

Show Links:

The (Non-)Return of the Python Print Statement – Guido van Rossum recently proposed re-introducing the Python print statement. He was completely serious and even though the idea didn’t gain traction, it’s interesting to know why he made the proposal.

Python Malware on the Rise – Python’s low barrier to entry, enormous ecosystem, and rapid development process has made it one of he most desired programming languages for millions of developers around he globe—including malicious actors. Read the article at the link above and follow the discussion on Hacker News.

Get Started With Django Part 2: Django User Management – In this step-by-step tutorial, you’ll learn how to extend your Django application with a user management system, complete with email sending and third-party authentication.

Why Do People Use .format() When f-Strings Exist? – f-Strings aren’t exactly a drop-in replacement for .format().

A Beginner’s Guide to Robot Programming With Python – Get a crash course in programming autonomous robots with Python. Don’t have a robot laying around? No problem! Use this open-source simulator to get started.

Python import: Advanced Techniques and Tips – The Python import system is as powerful as it is useful. In this in-depth tutorial, you’ll learn how to harness this power to improve the structure and maintainability of your code.

Projects:

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Ten Years of Flask: Conversation With Creator Armin Ronacher

Fri, 17 Jul 2020 12:00:00 +0000

This week on the show we have Armin Ronacher to talk about the first 10 years of Flask. Armin talks about the origins of Flask and the components that make up the framework. He talks about what goes into documenting a framework or API. He also talks about the community working on the ongoing development of Flask.

He also shares his thoughts about Python, and how it contrasts with Rust and TypeScript. Armin talks about what he would do differently if he were to start development of a project like Flask now.

Course Spotlight: Documenting Python Code: A Complete Guide

This course will get you up to speed with how to document your Python code. Documenting your code is an important step to help developers and users fully understand its usage and purpose.

Topics:

00:00:00 – Introduction
00:01:21 – Director of Engineering at Sentry
00:07:27 – How much are you currently involved with Flask?
00:08:32 – Are you still using both Python and Rust currently?
00:11:37 – The origins of Flask
00:19:14 – Initial reactions and focus
00:21:26 – Where has Jinja use shifted?
00:23:23 – Flask usage trends
00:25:44 – The Pallets Projects name
00:29:08 – Flask version numbers
00:34:21 – Video Course Spotlight
00:35:21 – The community working on Flask
00:38:18 – Thoughts on type checking
00:41:40 – Flask’s documentation
00:43:26 – Thoughts on API documentation
00:47:27 – What would you change if you started from scratch?
00:49:58 – Listener question about using Python with WordPress
00:58:15 – Any suggestions of a Python project source code to read?
01:00:13 – Revisit - What would you change if you started from scratch?
01:03:17 – Thoughts on the current path of Python
01:06:20 – What are you excited about in the world of Python?
01:07:44 – What do you want to learn next?
01:11:35 – Thoughts on conferences and speaking
01:16:19 – Thanks and Conclusion

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Linear Programming, PySimpleGUI, and More

Fri, 10 Jul 2020 12:00:00 +0000

Are you familiar with linear programming, and how it can be used to solve resource optimization problems? Would you like to free your Python code from a clunky command line and start making convenient graphical interfaces for your users? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python article about linear programming in Python. We discuss an article titled “PySimpleGUI: The Simple Way to Create a GUI With Python.” We also cover several other articles and projects from the Python community including: Python’s reduce() function, flaws in the pickle module, advanced pytest techniques, and how to trick a neural network.

Course Spotlight: Parallel Iteration With Python’s zip() Function

This course will get you up to speed with Python’s zip() function. In this course, you’ll discover the logic behind zip() and how you can use it to consistently solve common programming problems, like creating dictionaries.

Topics:

00:00:00 – Introduction
00:01:34 – Python’s reduce(): From Functional to Pythonic Style
00:07:46 – Hands-On Linear Programming: Optimization With Python
00:15:07 – Pickle’s Nine Flaws
00:22:31 – Video Course Spotlight
00:23:33 – Advanced pytest Techniques I Learned While Contributing to pandas
00:33:41 – PySimpleGUI: The Simple Way to Create a GUI With Python
00:38:20 – How to Trick a Neural Network in Python 3
00:43:31 – TextAttack: A Python Framework for Adversarial Attacks, Data Augmentation, and Model Training in NLP
00:46:09 – byob: BYOB (Build Your Own Botnet)
00:49:09 – Thanks and Goodbye

Show Links:

Python’s reduce(): From Functional to Pythonic Style – In this step-by-step tutorial, you’ll learn how Python’s reduce() works and how to use it effectively in your programs. You’ll also learn some more modern, efficient, and Pythonic ways to gently replace reduce() in your programs.

Hands-On Linear Programming: Optimization With Python – In this tutorial, you’ll learn about implementing optimization in Python with linear programming libraries. Linear programming is one of the fundamental mathematical optimization techniques. You’ll use SciPy and PuLP to solve linear programming problems.

Pickle’s Nine Flaws – “Python’s pickle module is a very convenient way to serialize and de-serialize objects. It needs no schema, and can handle arbitrary Python objects. But it has problems. This post briefly explains the problems.”

Advanced pytest Techniques I Learned While Contributing to pandas – Contributing to open-source projects is a great way to learn new techniques and level up your skills. Martin Winkel shares five advanced pytest techniques he learned while contributing to the pandas project.

PySimpleGUI: The Simple Way to Create a GUI With Python – In this step-by-step tutorial, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application.

How to Trick a Neural Network in Python 3 – Is that a corgi or a goldfish?

Projects:

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Thinking in Pandas: Python Data Analysis the Right Way

Fri, 03 Jul 2020 12:00:00 +0000

Are you using the Python library Pandas the right way? Do you wonder about getting better performance, or how to optimize your data for analysis? What does normalization mean? This week on the show we have Hannah Stepanek to discuss her new book “Thinking in Pandas”.

The inspiration behind Hannah’s book came out of her talk at PyCon US 2019 titled “Thinking Like a Panda: Everything You Need to Know to Use Pandas the Right Way.” We discuss several core concepts covered in the book. She shares techniques for getting more performance when working with your data in Pandas. We also talk about her recent PyCon US 2020 online presentation about databases and migration.

Course Spotlight: Finding the Perfect Python Code Editor

Find your perfect Python development setup with this review of Python IDEs and code editors. With this course you’ll get an overview of the most common Python coding environments to help you make an informed decision.

Topics:

00:00:00 – Introduction
00:01:36 – Working for New Relic
00:03:14 – Thinking in Pandas book release
00:03:27 – Who is the intended reader?
00:05:27 – What is the underlying tech for Pandas?
00:09:04 – Why you shouldn’t use apply?
00:13:00 – When you have to use apply
00:16:06 – Normalizing your data
00:17:05 – Do you have a preferred format for a dataframe?
00:18:17 – More on multi-index dataframes
00:24:50 – Creating NumPy types
00:28:30 – Loading in your data
00:30:33 – Video Course Spotlight
00:31:41 – Pivoting data
00:34:34 – Considering outside libraries and performance
00:35:41 – What topic were you eager to share in the book?
00:37:52 – What resources did you use to learn pandas?
00:40:53 – PyCon 2020 talk about databases and migration
00:45:34 – Delving into migration and Alembic
00:53:15 – Speaking opportunities
00:56:13 – What are you excited about in the world of Python?
00:57:32 – What do you want to learn next?
00:58:49 – Do you read source code to learn?
01:00:16 – Is there a particularly well-written library?
01:01:28 – Final Thanks

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Python Regular Expressions, Views vs Copies in Pandas, and More

Fri, 26 Jun 2020 12:00:00 +0000

Have you wanted to learn Regular Expressions in Python, but don’t know where to start? Have you stumbled into the dreaded pink SettingWithCopyWarning in Pandas? This week on the show, we have David Amos from the Real Python team to discuss a recent two-part series on Regex in Python. We also talk about another recent article on the site about views vs copies in Pandas. David also brings a few other articles and projects from the wider Python community for us to discuss.

David searches for the latest Python news, links, and articles to produce PyCoder’s Weekly with Dan Bader. PyCoder’s Weekly is a free email newsletter for those interested in Python development. Along with the previously mentioned Real Python articles, we also discuss articles from the community about: getting machine learning to production, combining Flask and Vue, space science with Python, and the fastest way to flatten a list in Python.

Course Spotlight: Reading and Writing Files in Python

This course will get you up to speed with reading and writing files in Python. You’ll cover everything from what a file is made up of, to which libraries can help you along that way. You’ll also take a look at some basic scenarios of file usage as well as some advanced techniques.

Topics:

00:00:00 – Introduction
00:01:40 – SettingWithCopyWarning in Pandas
00:06:17 – Fastest Way to Flaten a List in Python
00:12:12 – Combining Flask and Vue
00:15:33 – Regular Expressions in Python
00:23:08 – Video Course Spotlight
00:24:00 – Space Science With Python
00:30:13 – Getting Machine Learning to Production
00:37:15 – genetic-drawing
00:39:42 – MicroscoPy
00:43:55 – Thanks and Goodbye

Show Links:

SettingWithCopyWarning in Pandas: Views vs Copies – In this tutorial, you’ll learn about views and copies in NumPy and Pandas. You’ll see why the SettingWithCopyWarning occurs in Pandas and how to properly write code that avoids it.

Fastest Way to Flatten a List in Python – Explore six different was to flatten a list of lists in Python and how their performance compares. The fastest of the six methods mentioned might surprise you!

Combining Flask and Vue – Learn about three ways to combine Flask and Vue, the pros and cons of each, and some guidelines for when to use each method.

Regular Expressions: Regexes in Python (Part 2) – In the previous tutorial in this series, you learned how to perform sophisticated pattern matching using regular expressions, or regexes, in Python. This tutorial explores more regex tools and techniques that are available in Python.

Space Science With Python – Explore and analyze the wonders and mysteries of space… with Python!

Getting Machine Learning to Production – Millions of web apps get deployed to production every day. But machine learning models aren’t web apps. And very few people are talking about deployment. Learn how tools like Streamlit can help take the edge off deploying your machine learning models.

Projects:

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Going Serverless with Python

Fri, 19 Jun 2020 12:00:00 +0000

Would you like to run your Python code in the cloud without having to become an infrastructure engineer? Do you want to have Python functions that run when triggered by specific events? This week on the show we have Anthony Chu to discuss serverless computing and running python functions in the cloud. Anthony Chu is program manager for Microsoft’s Azure Functions.

We discuss the advantages of serverless computing over virtual machines, containers, and other infrastructure options for running your Python code in the cloud. Anthony also talks about the types of projects suited for this type of platform, including data science, machine learning, and creating APIs.

Course Spotlight: A Beginner’s Guide to Pip

Topics:

00:00:00 – Introduction
00:01:22 – Microsoft Build 2020
00:02:16 – What is serverless computing?
00:06:59 – Using VSCode for serverless development
00:08:54 – What is blob storage?
00:12:08 – Adding Python to Azure Functions
00:16:25 – What are common serverless projects?
00:20:32 – Serverless containers
00:24:00 – Video Course Spotlight
00:25:28 – Accessing from a CLI
00:29:31 – Versions of Python available
00:32:50 – Running from your own Kubernetes cluster
00:36:03 – Advantages and disadvantages to using serverless
00:38:31 – Other services
00:39:25 – Durable functions for Python
00:51:04 – What are you excited about in the world of Python?
00:52:42 – What do you want to learn next in Python?
00:53:56 – Thanks and goodbye

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PDFs in Python and Projects on the Raspberry Pi

Fri, 12 Jun 2020 12:00:00 +0000

Have you wanted to work with PDF files in Python? Maybe you want to extract text, merge and concatenate files, or even create PDFs from scratch. Are you interested in building hardware projects using a Raspberry Pi? This week on the show we have David Amos from the Real Python team to discuss his recent article on working with PDFs. David also brings a few other articles from the wider Python community for us to discuss.

David searches for the latest Python news, links, and articles to produce PyCoder’s Weekly with Dan Bader. PyCoder’s Weekly is a free email newsletter for those interested in Python development. Along with David’s article on PDFs, we discuss another recent Real Python article about building physical projects with the Raspberry Pi. We also discuss articles from the community about: the PEPs of Python 3.9, why you should stop using datetime.now, Python dependency tools, and several ways to pass code to Python from the terminal.

Course Spotlight: Cool New Features in Python 3.8

This course will get you up to speed with the new features of the latest release of Python. You’ll learn about using assignment expressions, how to enforce postional-only arguments, more precise type hints, and using f-strings for simpler debugging. It’s a worthy investment of your time to understand what the most recent release of Python provides before moving on to the next version this fall.

Topics:

00:00:00 – Introduction
00:02:06 – Ways to Pass Code to Python From the Terminal
00:05:54 – The PEPs of Python 3.9
00:10:54 – Creating and Modifying PDF Files in Python
00:18:51 – Video Course Spotlight
00:19:56 – An Overview of Python Dependency Tools
00:26:55 – Stop Using datetime.now
00:31:44 – Build Physical Projects With Python on the Raspberry Pi
00:38:18 – What are you excited about in the world of Python?
00:42:29 – What do you want to learn next in Python?
00:44:31 – Thanks and Good Bye

Topic Links:

PyCoder’s Weekly

The Many Ways to Pass Code to Python From the Terminal – You might know about pointing Python to a file path, or using -m to execute a module. But did you know that Python can execute a directory? Or a .zip file?

The PEPs of Python 3.9 – The first Python 3.9 beta release is upon us! Learn what to expect in the final October release by taking a tour of the Python Enhancement Proposals (PEPs) that were accepted for Python 3.9.

Creating and Modifying PDF Files in Python – Explore the different ways of creating and modifying PDF files in Python. You’ll learn how to read and extract text, merge and concatenate files, crop and rotate pages, encrypt and decrypt files, and even create PDFs from scratch.

Overview of Python Dependency Management Tools – While pip is often considered the de facto Python package manager, the dependency management ecosystem has really grown over that last few years. Learn about the different tools available and how they fit into this ecosystem.

Stop Using datetime.now! (With Dependency Injection) – How do you test a function that relies on datetime.now() or date.today()? You could use libraries like FreezeGun or libfaketime, but not every project can afford the luxury of reaching for third-party solutions. Learn how dependency injection can help you write code that is more testable, maintainable, and practical.

Build Physical Projects With Python on the Raspberry Pi – In this tutorial, you’ll learn to use Python on the Raspberry Pi. The Raspberry Pi is one of the leading physical computing boards on the market and a great way to get started using Python to interact with the physical world.

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Web Scraping in Python: Tools, Techniques, and Legality

Fri, 05 Jun 2020 12:00:00 +0000

Do you want to get started with web scraping using Python? Are you concerned about the potential legal implications? What are the tools required and what are some of the best practices? This week on the show we have Kimberly Fessel to discuss her excellent tutorial created for PyCon 2020 online titled “It’s Officially Legal so Let’s Scrape the Web.”

We discuss getting started with web scraping, and cover tools and techniques. Kimberly gives advice on finding elements inside of the html, and techniques for cleaning your data. She also notes a recent change to the legal landscape regarding scraping the web.

Kimberly is a Senior Data Scientist at Metis Data Science Bootcamp in New York City. She holds a Ph.D. in applied mathematics. We talk about her switch from academia to data science, and discuss her passion for data storytelling and visualizations.

Course Spotlight: Defining Main Functions in Python

This course will get you up to speed with defining a starting point for the execution of a program, and helps you to understand what goes into the main() function. Prepare for a deep dive as you go through the sections. It’s a worthy investment of your time to understand this vital entry point for your Python scripts and applications!

Topics:

00:00:00 – Introduction
00:01:31 – Kimberly’s background and Metis Data Science Bootcamp
00:02:19 – NLP and work in advertising
00:03:27 – Changes in the legality of web scraping
00:06:12 – What are good projects for web scraping?
00:06:56 – Tools to start web scraping
00:07:51 – How to find the elements you want?
00:09:00 – How much HTML should you know?
00:10:49 – Inspecting elements in the browser
00:14:30 – What are good sites to practice on?
00:16:20 – Pausing between requests
00:19:02 – Saving as you go
00:20:54 – Real Python Video Course Spotlight
00:21:55 – Navigating the DOM
00:23:10 – Data cleaning and formatting
00:28:26 – Dynamic sites and Selenium
00:32:16 – Scrapy
00:33:55 – PyOhio 2020
00:35:40 – Transition out of academia
00:38:40 – What are you excited about in the world of Python?
00:41:05 – What do you want to learn next in Python?
00:48:00 – What is a less known Python tip or trick?
00:49:17 – Thanks and Goodbye

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Advice on Getting Started With Testing in Python

Fri, 29 May 2020 12:00:00 +0000

Have you wanted to get started with testing in Python? Maybe you feel a little nervous about diving in deeper than just confirming your code runs. What are the tools needed and what would be the next steps to level up your Python testing? This week on the show we have Anthony Shaw to discuss his article on this subject. Anthony is a member of the Real Python team and has written several articles for the site.

We discuss getting started with built-in Python features for testing and the advantages of a tool like pytest. Anthony talks about his plug-ins for pytest, and we touch on the next level of testing involving continuous integration.

Anthony recently finished a talk for PyCon 2020 Online, titled “Why is Python Slow?” He had the idea for the talk while he was working on his upcoming book about the CPython source code.

I also want to give an update on last weeks episode with Kyle Stratis, where we discussed Kyle being let go from his job due to the pandemic. Here’s some good news, Kyle will be joining a Boston startup called Vizit, as a senior data engineer. Congratulations Kyle!

Course Spotlight: The Python print() Function: Go Beyond the Basics

This course will get you up to speed with using Python print() effectively. Prepare for a deep dive as you go through the sections. You may be surprised how much print() has to offer!

Topics:

00:00:00 – Introduction
00:01:46 – PyCon 2020 Online Talk - Why is Python slow?
00:04:05 – CPython Internals Book
00:07:08 – Attending Conferences
00:09:01 – Getting Started with Testing in Python
00:12:32 – Unittest
00:17:16 – What does a tool like pytest add?
00:19:53 – pytest plugins
00:21:03 – Anthony’s pytest plugins
00:21:58 – What does coverage mean?
00:25:23 – Test runners
00:27:12 – Testing environments with Tox
00:30:50 – Real Python Video Course Spotlight
00:31:49 – More on continuous integration (CI)
00:37:21 – Recent changes to GitHub
00:38:21 – PSF to move issue tracker to GitHub
00:41:01 – DRY (Don’t Repeat Yourself)
00:43:46 – Benefits of linters and code formatting
00:48:00 – What is a little known part of Python?
00:52:16 – What are you excited about in the world of Python?
00:56:06 – What is something you thought you knew about Python, but were wrong about it?
00:57:27 – Goodbye and thanks

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Python Job Hunting in a Pandemic

Fri, 22 May 2020 12:00:00 +0000

Do you know someone in the Python community who recently was let go from their job due to the pandemic? What does the job landscape currently look like? What are skills and techniques that will help you in your job search? This week we have Kyle Stratis on the show to discuss how he is managing his job search after just being let go from his data engineering job. Kyle is a member of the Real Python team and has written several articles for the site.

We discuss Kyle’s career and the skills that he’s developed, which are currently helping him in his job search. Kyle left academia to work as a data engineer. His background helps him to communicate between teams of scientists and engineers.

We also talk about Kyle’s recent article on combining data in Pandas. Kyle shares a tip on Pandas efficiency, and hints at some lesser known features of Python generators.

Topics:

00:00:00 – Introduction
00:01:27 – Kyle’s background on being let go
00:04:17 – Programming background and building connections
00:10:18 – Becoming a Data Engineer
00:15:59 – Translating between science and data teams
00:20:35 – Every job has different language requirements
00:23:44 – Getting out of your Python language comfort zone
00:27:08 – NASDANQ project - a stockmarket for Memes
00:30:34 – Learning the power of building a network
00:35:13 – Using skills developed in outside projects
00:38:45 – What does the job landscape look like currently?
00:49:52 – Writing for Real Python
00:52:53 – Combining data in Pandas article
00:55:22 – Merging in Pandas
01:03:05 – Feedback and community
01:10:37 – What are you excited ab out in the world of Python?
01:12:12 – What is something you thought you knew about Python but were wrong about it?
01:14:01 – What is a little known Python trick or tip?
01:14:33 – More efficient Pandas
01:15:52 – Using more of the advanced features of generators
01:18:55 – Thanks and Goodbye

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Leveling Up Your Python Literacy and Finding Python Projects to Study

Fri, 15 May 2020 12:00:00 +0000

In your quest to become a better developer, how do you find Python code that is at your reading level? What are good code bases or projects to study? What are the things holding you back from leveling up your Python literacy? This week we have Cecil Phillip on the show to discuss all of these common questions. Cecil is a Senior Cloud Advocate at Microsoft.

Cecil has been learning Python in the open on Twitch with Brian Clark. They run a weekly event on Twitch, where they are live-streaming an interactive Python course. Cecil has a background in multiple languages and technologies, and now he’s learning Python, bringing an audience along the way!

We start things off with a listener question and jump into a conversation about building up your Python skills. Then we’ll discuss common Python language stumbling blocks. Next we consider the importance of making personal projects, and documenting that code.

We also touch on some unique skills employers are looking for. And we discuss working through impostor syndrome. Cecil talks about his podcast “Away from the Keyboard” and his plans to start it back up.

In the show notes this week you’ll find links to resources we discuss, and several more that we didn’t have time to cover individually.

Want your question featured on the show? Send us your question at realpython.com/podcast-question and we might feature it on a future episode of the show.

Topics:

00:00:00 – Intro
00:01:52 – Cecil’s role at Microsoft
00:03:35 – Twitch Stream with Brian Clark
00:05:07 – Learning in front of an audience
00:13:05 – Listener’s question
00:14:46 – Finding code that’s at your level
00:20:31 – Understanding more complex syntax in Python
00:23:40 – Breaking down complexity
00:29:17 – Translation of code
00:31:55 – Importance of making projects and comments
00:36:28 – Finding community
00:41:23 – Open source contributing
00:42:25 – Dealing with impostor syndrome
00:49:09 – Looking for that first position
01:00:58 – More project resources in show notes
01:02:55 – Cecil’s podcast - Away from the keyboard
01:08:29 – What are you excited about in the world of Python?
01:10:14 – What is something you thought you knew about Python but were wrong about it?
01:12:01 – What’s the next thing you want to learn in Python?
01:13:37 – Read the actual Python docs
01:15:24 – Thanks and goodbye

Show links:

Docker + Python for Data Science and Machine Learning

Fri, 08 May 2020 12:00:00 +0000

Docker is a common tool for Python developers creating and deploying applications, but what do you need to know if you want to use Docker for data science and machine learning? What are the best practices if you want to start using containers for your scientific projects? This week we have Tania Allard on the show. She is a Sr. Developer Advocate at Microsoft focusing on Machine Learning, scientific computing, research and open source.

Tania has created a talk for the PyCon US 2020 which is now online. The talk is titled “Docker and Python: Making them Play Nicely and Securely for Data Science and ML.” Her talk draws on her expertise in the improvement of processes, reproducibility and transparency in research and data science. We discuss a variety of tools for making your containers more secure and results reproducible.

Tania is passionate about mentoring, open-source, and its community. She is an organizer for Mentored Sprints for Diverse Beginners, and she talks about the upcoming online sprints for PyCon US 2020. We also discuss her plans to start a podcast.

Topics:

00:00:00 – Introduction
00:01:43 – Microsoft Senior Developer Advocate Role
00:04:07 – PyCon 2020 Talk - Docker and Python: making them play nicely
00:05:34 – What is Docker?
00:10:08 – Reproducibility of project results
00:12:03 – What are the challenges of using Docker for machine learning?
00:15:06 – Getting started suggestions
00:16:26 – What metadata should be included?
00:17:48 – Creating images through stages
00:21:16 – What about your data?
00:22:40 – Kubernetes: Orchestrating containers
00:24:37 – Continuing stages into testing
00:25:37 – What are tools for testing security?
00:27:07 – Challenges in using containers for ML
00:28:52 – What types of databases?
00:29:39 – Are you doing initial research on a local machine?
00:30:59 – An example of a recent ML project
00:32:16 – Papermill: parameterizing and executing notebooks
00:33:16 – NLP: Natural Language Processing
00:33:58 – Kaggle: Help us better understand COVID-19
00:34:42 – What are other best practices for data intensive projects?
00:39:13 – Resources to get started in machine learning?
00:40:30 – Mentored Sprints for Diverse Beginners
00:45:34 – Tania’s upcoming podcast
00:48:38 – A visiting fellow at the Alan Turing Institute
00:49:08 – Weight lifting
00:50:16 – Craft beer
00:52:09 – What is something you thought you knew in Python but were wrong about?
00:53:50 – What are excited about in the world of Python?
00:54:42 – Thank you and Goodbye

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AsyncIO + Music, Origins of Black, and Managing Python Releases

Fri, 01 May 2020 12:00:00 +0000

Want to learn more about AsyncIO in Python, with an example where you can see and hear events being triggered in real-time? This week we have Łukasz Langa on the show. Łukasz has created a talk for PyCon 2020 online about using AsyncIO with Music.

In his talk he shows live examples of coroutines, gathering, the event loop and events being triggered to create a piece of music. We also talk about his role as the release manager for Python 3.8 and 3.9. Łukasz provides background on the origins of his very popular, uncompromising code formatter, Black, and the types of problems it can solve inside of an organization.

Łukasz previously worked for Facebook, which is where he started Black. He talks about recently moving back to Poland. We discuss his current work for Edge DB, building a new generation object-relational database.

Topics:

00:00:00 – Introduction
00:01:32 – Łukasz’s background
00:03:22 – Leaving Facebook and moving back to Poland
00:05:26 – Starting work with EdgeDB
00:06:07 – What is Edge DB?
00:12:28 – AsyncIO + Music PyCon 2020 talk
00:18:56 – More AsyncIO resources
00:23:36 – Comparing the event loop to a game loop
00:27:12 – Coroutines and gather
00:30:00 – A conversation with Glyph
00:33:40 – Bigger ideas for the AsyncIO MIDI sequencer
00:35:41 – Using uvloop as a replacement for the built-in reference AsyncIO loop
00:39:13 – Thoughts on MIDI 2.0
00:46:30 – Origins of Black
00:53:51 – Black grows in popularity
00:58:35 – What is involved in being the Python 3.9 release manager?
01:02:22 – The Python language summit
01:07:44 – Is the beta on schedule?
01:09:27 – How did you get the role of Release Manager?
01:15:09 – What are you excited about in the world of Python?
01:19:02 – If you were learning Python from scratch, what would do differently?
01:22:18 – What is something you thought you knew about Python, but were wrong about?
01:26:05 – Goodbye and Thanks

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Python REST APIs and The Well-Grounded Python Developer

Fri, 24 Apr 2020 12:00:00 +0000

Are you interested in building REST APIs with Flask and SQLAlchemy? This week we have Doug Farrell on the show. We talk about his four-part Real Python article series on Python REST APIs.

We discuss the various Python tools and libraries used in the series. Doug also shares his practices for continuous learning. Doug has worked in process control, embedded systems, and has a long background in software development.

He’s currently a developer at ShutterFly, and discusses developing tools for his internal customers. He also teaches Python to kids at a STEM school near where he lives.

Doug is writing a book for Manning Publications, “The Well-Grounded Python Developer”. The book is currently available in an early access state. And as always please check out all the additional resources and tools that Doug discusses, they are all gathered for you in the show notes.

Topics:

00:00:00 – Introduction
00:01:30 – Doug’s programming background
00:06:16 – Building a Polargraph
00:08:51 – When did you get into Python?
00:10:43 – Working at Shutterfly
00:13:45 – How does Python help at Shutterfly?
00:16:21 – Difficulties for a self-taught developer
00:18:58 – How do you keep honing your skills?
00:20:32 – Writing articles
00:22:04 – Python REST APIs With Flask, Connexion, and SQLAlchemy Series
00:27:54 – Picking tools for REST APIs
00:36:27 – The Well-Ground Python Developer Book
00:39:27 – What topic are you most interested in covering?
00:42:35 – How has working with hardware helped you become a better programmer?
00:45:36 – Something you thought you knew about Python, but were wrong about?
00:46:25 – What’s a good tool to use for profiling?
00:47:34 – Getting up to speed on data science
00:50:45 – What are you excited about in the world of Python?
00:53:26 – Contact info, thank you and sign off

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Exploring CircuitPython

Fri, 17 Apr 2020 12:00:00 +0000

Have you ever wanted to explore using Python with electronics? CircuitPython is a great platform to get started with. This week we have Thea Flowers on the show. Thea has been creating several hardware projects based around CircuitPython, and she talks about getting started on the platform.

She also answers questions about how she taught herself to design and prototype printed circuit boards. Thea discusses several of her open source projects, including Nox, ConductHotline, and getting involved with CircuitPython.

Thea was the conference co-chair for PyCascades, and we talk about how someone could get involved in volunteering for conferences. We also discuss building diversity in the community.

This episode was initially recorded at an earlier date, so we asked Thea to come back for a few minutes to discuss updates on her projects and about a recent honor she received.

Topics:

00:00:00 – Introduction
00:01:25 – Thea’s programming background
00:02:45 – Working with Google Cloud Platform
00:04:10 – Flutter developer relations
00:04:52 – Learning Python
00:06:07 – Working on open source projects
00:06:33 – Nox - Automated Python testing
00:07:03 – ConductHotline
00:07:38 – Contributing to CircuitPython
00:07:53 – More background on Nox and Tox
00:10:03 – Getting involved with CircuitPython
00:12:38 – MicroPython and CircuitPython
00:14:20 – Suggestions for starter board or kit
00:15:49 – What are you excited about in CircuitPython?
00:16:31 – Nina Zakharenko CircuitPython project
00:17:47 – Things you’d like to see improved in CircuitPython?
00:21:30 – Working toward consensus in open source projects?
00:25:41 – Winterbloom - Big Honking Button
00:30:25 – Creating circuit boards
00:34:32 – Winterbloom - Sol
00:38:49 – Code editor for CircuitPython
00:40:08 – Something you thought you knew about Python, but were wrong about?
00:42:14 – What are you excited about in the world of Python?
00:44:21 – Do you listen to music when coding?
00:45:29 – Being an organizer for PyCascades
00:46:53 – Getting involved and volunteering for events
00:48:16 – Ways to increase diversity
00:53:51 – Extended episode conversation
00:54:25 – Updates on the WInterbloom projects
00:55:24 – 2020 Q1 PSF Fellow Member!
00:56:32 – PyCon 2020 moves to online only
00:58:45 – How would you learn Python if starting from scratch?
01:02:10 – Thanks and ending

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Learning Python Through Errors

Fri, 10 Apr 2020 12:00:00 +0000

Do you get upset and frustrated when you experience errors running your Python code? This week we have Martin Breuss on the show. We discuss how to learn Python through errors, and how errors really are your friends.

Martin is a video course creator here at Real Python, and we talk briefly about several courses he’s created. We focus on his course about getting started with Django, as a jumping off point for the discussion.

Martin talks about his work with Coding Nomads, and teaching Python around the world. He also provides some tips on debugging and writing good questions.

This episode was recorded at an earlier date, and because of recent events Martin came back to discuss a new #StayAtHome Mentorship Program he’s working on. The program is meant not only for learners but also for those who want to try their hand at being a mentor. We also answer our first listener submitted question.

Topics:

00:00:00 – Introduction
00:01:18 – Martin Breuss - Introduction
00:04:52 – Programming background and MOOCs
00:10:17 – Creating Courses for Real Python
00:12:02 – Real Python - Django Course
00:14:50 – How can errors teach you?
00:18:27 – Reading errors from Django
00:22:31 – Working with Coding Nomads
00:24:16 – Common frustrations for students
00:26:52 – Comments and forums
00:29:46 – Asking good questions
00:34:24 – Debugging tips
00:36:37 – Course: Finding the right python code editor
00:42:46 – What are you excited about?
00:46:05 – MacOS Catalina Python issue
00:47:30 – Music for programming
00:48:51 – Extended episode details
00:49:29 – #StayAtHome Mentorship Program
00:58:48 – Listener submitted question
00:59:17 – How would you learn Python from scratch?
01:09:39 – Final thanks and links

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Effective Python and Python at Google Scale

Fri, 03 Apr 2020 12:00:00 +0000

Have you been using Python for a while, but want to be more effective with your code? This week we have Brett Slatkin on the show. We talk about the 2nd edition of his book Effective Python.

Brett talks about the revisions he made for the book, and updating it for the newest versions of Python 3. He answers questions about who is the intended developer for the book.

Brett also discusses working on Google App Engine, and what it’s like to develop and maintain Python applications at Google Scale. Brett mentions a brief anecdote about working with Guido van Rossum, while they both worked at Google. He also provides advice about maintaining a large and aging Python code base.

Topics:

00:00:00 – Introduction
00:01:13 – Brett Slatkin - Programming background
00:02:00 – Python background and start at Google
00:03:22 – Working on Google infrastructure
00:05:36 – Is Python a good tool for infrastructure?
00:07:28 – PubSubHubbub
00:09:06 – Lobste.rs
00:10:03 – Starting to write Effective Python
00:11:12 – Who is the intended developer for the book?
00:12:45 – About the Effective series book structure
00:14:39 – What were the sections you were excited to rewrite?
00:18:41 – Moving away from Metaclasses in modern Python
00:20:43 – Python 3.8 in the book
00:21:03 – Using the walrus operator to build a switch/case statement
00:23:22 – Why did you feel Python made you a more productive developer?
00:28:02 – Working with Guido van Rossum
00:31:15 – What’s it like to work on the same code base for years?
00:33:35 – What is code rot?
00:35:10 – What would you put in a book about refactoring?
00:37:06 – What’s something you thought you knew about Python, but were wrong?
00:38:42 – What are you excited about in the world of Python?
00:40:24 – Do you listen to music when you code?
00:42:00 – End Credits

Show links:

What are you excited about in the world of Python?

Brett’s Pick:

PEP 554 – Multiple Interpreters in the Stdlib

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Learn Python Skills While Creating Games

Fri, 27 Mar 2020 12:00:00 +0000

Is game programming a good way to develop your Python programming skills? This week we have Jon Fincher on the show. Jon is an author on the Real Python team, and we talk about his recent articles on PyGame and Arcade.

Jon talks about his background working at Microsoft. We discuss if a game would make a good portfolio piece. We compare and contrasts the two popular Python game libraries of Arcade and PyGame. Jon also reveals ways to find assets for your own creations.

Want your question featured on the show? Here’s a new thing we’re trying out. Send us your question at realpython.com/podcast-question and we might feature it on a future episode of the show.

Topics:

00:00:00 – Introduction
00:00:56 – Jon Fincher
00:05:17 – Start with Real Python
00:07:08 – Is game programming a good way to learn?
00:13:16 – Setting specific goals and limits
00:19:28 – Is a game a good portfolio piece?
00:21:07 – PyWeek 29
00:22:58 – Al Sweigart books
00:25:00 – Comparing PyGame and Arcade
00:37:34 – Finding game art and other assets
00:41:04 – Licensing game assets
00:44:29 – Packaging your game and sharing
00:47:34 – What are you excited about in the world of Python
00:50:20 – Final Thoughts
00:54:13 – Conclusion

Show links:

What are you excited about in the world of Python?

Jon’s pick:

Chris’s pick:

Real Python Podcast: Submit a Question

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Python Decorators and Writing for Real Python

Fri, 20 Mar 2020 12:00:00 +0000

Do you want to learn more about Python decorators? Have you ever wondered what goes on behind the scenes to create a Real Python article? In this first episode, We have Geir Arne Hjelle from the Real Python team on the show.

You’ll learn about Geir Arne’s background as a Pythonista and PyCon speaker, the tutorials he’s written for the site, how Python decorators can help you write better code, and what Real Python’s tutorial publishing process looks like behind the scenes.

Note: This episode was recorded earlier. We discuss PyCon US 2020 and Geir Arne’s planned tutorial. At the time this episode was released PyCon US 2020 was currently looking at re-scheduling or a potential cancelation. Updates will be provided in upcoming episodes.

Topics: