What Can I Do With Python?

Web Development

Web application development with Python is in high demand. In this field, you’ll find several well-established Python frameworks, libraries, and tools for developing cool web applications, APIs, and more.

Here are some of the most popular Python web frameworks:

To get started with web development in Python, check out the following resources:

• Django for Web Development (Learning Path)
• Flask by Example (Learning Path)
• Using FastAPI to Build Python Web APIs
• Python and REST APIs: Interacting With Web Services
• Python Web Development Tutorials
• Python API Tutorials

If you want some practical project ideas for applying your web development skills right away, check out these tutorials:

• Python Web Applications: Deploy Your Script as a Flask App
• Get Started With FastAPI
• Get Started With Django: Build a Portfolio App
• Build a Personal Diary With Django and Python
• Build a Blog From Scratch With Django
• Python REST APIs With Flask, Connexion, and SQLAlchemy
• Build a Code Image Generator With Python
• Build a Flashcards App With Django
• Build a URL Shortener With FastAPI and Python

With so many Python-related career opportunities out there, it’s a great idea to have a personal portfolio. Feel free to give some of these projects a try. You don’t need to know anything about Django to get started with these step-by-step tutorials. They’re perfect if you’re itching to get hands-on with web development in Python.

CLI Development

Another field in which Python shines is in command-line interface (CLI) application development. CLI applications are everywhere and allow you to automate repetitive and boring tasks in your daily work by creating small or large tools for your command line.

In Python, you’ll find several CLI libraries and frameworks that can simplify your work and help you build command-line tools quickly:

To get started with CLI development, check out the following resources:

• Build Command-Line Interfaces With Python’s argparse
• Click and Python: Build Extensible and Composable CLI Apps

Additionally, if you want to jump into building a CLI project, then you can start with the following:

• Build a Command-Line To-Do App With Python and Typer
• Build a Python Directory Tree Generator for the Command Line
• Build Conway’s Game of Life With Python
• Build a Site Connectivity Checker in Python

Creating applications with a user-friendly and intuitive command-line interface is a valuable skill for any Python developer.

TUI Development

Text-based user interfaces (TUIs) let you build interactive apps that run in the terminal. They’re fast to start, keyboard-friendly, and great for dashboards, AI agents, and remote servers. Modern Python libraries make TUI development feel friendly by providing layout systems, widgets, color, styles, and even mouse support.

Here are popular Python options for building TUI apps:

To get started with TUI development, check out the following resources:

• Python Textual: Build Beautiful UIs in the Terminal
• The Python Rich Package: Unleash the Power of Console Text

When you’re ready for practical projects, you can explore these tutorials:

• Build a Contact Book App With Python, Textual, and SQLite
• Build a Wordle Clone With Python and Rich

In this series of tutorials, you’ll create fast, polished TUI apps that feel like lightweight desktop apps and run right in your terminal.

GUI Development

Creating traditional graphical user interface (GUI) applications for desktop environments is also an attractive option in Python. If you’re interested in building this kind of application, then Python has you covered with a wide range of GUI libraries, frameworks, and toolkits to choose from:

A quick way to start building your GUI applications is to use tkinter. This module is included in the Python standard library. Practice using tkinter and watch your vision materialize on the screen. Once you’ve got your feet wet, you can branch out and start working with other Python GUI toolkits.

To learn more about GUI programming with Python, check out the resources below:

• Create Graphical User Interfaces (Learning Path)
• GUI Programming With PyQt (Learning Path)
• Python GUI Programming Tutorials

If you want to start right away by creating a real-world GUI application, then you can build a calculator using PyQt. Completing this calculator project will help you grasp the fundamentals of this full-featured GUI framework, so you can start building impressive apps for your desktop immediately.

You can also work through other practical projects to help you out with your GUI programming journey. Take a look at the following resources:

• Build a Bulk File Rename Tool With Python and PyQt
• Build a Contact Book With Python, PyQt, and SQLite
• Build a Mobile Application With the Kivy Python Framework

These projects will guide you through the process of building GUI applications with PyQt and other Python GUI frameworks. They’ll also help you integrate a wide variety of other skills so you can create fully functional, real-world applications.

Game Development

Creating computer games is a great way to learn how to program, not just in Python but in any other programming language. To develop games, you’ll need to use variables, loops, conditional statements, functions, object-oriented programming, and more. Game development is an excellent way to synthesize multiple skills and interests, such as design, music, and programming.

Computer games have played an important role in programming. Many people get into programming because they love games and want to re-create their favorite games or build their own. Developing computer games can be a fun and rewarding experience, allowing you to enjoy the thrill of playing the game you just created.

You’ll find several tools, libraries, and frameworks for creating games quickly in the Python ecosystem. Here’s a small sample of them:

To get started with game programming, check out the following resources:

• Python Game Development Tutorials
• Top Python Game Engines
• Pygame: A Primer on Game Programming in Python

You can use Python to create arcade games, adventure games, and puzzle games that you can deploy within a few hours. You can also code classic games, such as hangman, tic-tac-toe, rock paper scissors, and more.

If you want to dive into building your first game, then you can start with some of the following:

• Build a Hangman Game for the Command Line in Python
• Build a Tic-Tac-Toe Game With Python and Tkinter
• Build an Asteroids Game With Python and Pygame
• Build a Platform Game in Python With Arcade

With these resources, you’ll be able to start creating your own games with Python. You’ll start with command-line games, then move on to GUI-based games, and finally explore games that take advantage of libraries specifically designed for game development.

Machine Learning

Machine learning can be the first step for someone interested in artificial intelligence. Machine learning studies algorithms that learn through experience. These algorithms build models based on samples of training data to make predictions and decisions.

Machine learning can be an intimidating field to get started with because the space is fast-moving and ever-changing. Here’s a summary of some of the most popular tools for doing machine learning with Python:

To get started with machine learning, check out the Python Machine Learning Tutorials.

Generative AI

Generative AI systems can create new content, including text, images, audio, video, and code, by learning patterns from data and producing novel outputs from natural language prompts. In practice, Python is the glue language that ties together language model APIs, orchestration frameworks, vector databases, and deployment stacks. This is why many tools in this field ship with first-class Python support.

Here are some of the most popular Python libraries and SDKs for building generative AI applications:

To get started with generative AI in Python, begin by exploring Hugging Face Transformers: Leverage Open-Source AI in Python or build a practical real-world project in Build an LLM RAG Chatbot With LangChain.

Vibe Coding and AI Agents

Two fast-moving trends are reshaping how Python developers work today:

• Vibe coding: A coding philosophy where you describe goals in natural language and lean on AI coding tools and agents to quickly iterate from idea to working code. It’s spreading in teams because it accelerates prototyping and boosts productivity. However, it still needs code review, testing, and guardrails.

• AI agent creation: A branch of AI engineering that goes beyond prompting LLMs to build stateful tools (agents) capable of planning, executing actions, leveraging external tools, and coordinating work.

Here are modern tools you can use in Python to start vibe coding and create your own AI agents:

To get started with AI-assisted coding and AI agent creation, check out the following resources:

• Python Coding With AI
• AI Coding Tools Glossary
• GitHub Copilot: Fly With Python at the Speed of Thought
• Write Unit Tests for Your Python Code With ChatGPT
• LangGraph: Build Stateful AI Agents in Python

Working through these resources helps you learn Python efficiently and with real-world focus. The official documentation for these libraries and tools includes up-to-date tutorials and hands-on projects so you can apply what you learn right away.

Scientific Computing

Another field in which Python plays a significant role is scientific computing. Scientists use advanced computing capabilities available through supercomputers, clusters of computers, and even desktop and laptop computers to understand and solve complex problems.

Here are some of the libraries you can use for scientific computing in Python:

To get started with scientific computing, check out the following resources:

• Math for Data Science (Learning Path)
• Python Data Science Tutorials
• NumPy, SciPy, and pandas: Correlation With Python
• SimPy: Simulating Real-World Processes With Python

These libraries and tools are fundamental to data science in Python. Some are core components of higher-level libraries for machine learning, data analysis, and other tasks.

Data Analysis and Visualization

Data analysis is a process of collecting, inspecting, cleansing, transforming, and modeling data to discover useful information, make predictions, arrive at conclusions, support decision-making processes, and more. Data analysis is closely related to data visualization, which deals with the graphical representation of data.

In Python, you’ll also find mature and well-established libraries for data analysis and data visualization. Here are some of them:

To get started with data analysis and visualization, check out the following resources:

• pandas for Data Science (Learning Path)
• Data Collection & Storage (Learning Path)
• Data Science With Python Core Skills (Learning Path)
• Data Visualization With Python (Learning Path)
• Python Data Visualization Tutorials
• Develop Data Visualization Interfaces in Python With Dash
• Python Polars: A Lightning-Fast DataFrame Library

If you want to level up your data analysis skills by building a practical project, then you can create a gradebook with Python and pandas. This step-by-step project guides you through the process of creating a Python script that loads the grade data and calculates letter grades for a group of students.

The project involves loading data from a comma-separated values (CSV) file, exploring the data, and calculating and plotting the grades using pandas.

Web Scraping and Automation

The web is a major source of data for data science. Web scraping is the process of collecting and parsing raw data from websites using automated tools or crawlers. Web automation goes a step further by driving a browser to load pages, click elements, and submit forms, which is useful when content requires interaction or is dynamically rendered.

Python provides widely used libraries for both scraping and automation. Here are some of them:

To learn more about web scraping, check out the following resources:

• Python Web Scraping Tutorials
• Python Web Scraping (Learning Path)
• Python’s Requests Library (Guide)

Once you know the basics of web scraping, you can dive into practical projects to improve your skills and target both static and dynamic sites:

• Beautiful Soup: Build a Web Scraper With Python
• Web Scraping With Scrapy and MongoDB
• Modern Web Automation With Python and Selenium

After completing these projects, you’ll be able to apply the same processes and tools to any other static or dynamic websites out there. These skills allow you to extract relevant information and use it in your applications. Go ahead and give it a try!

Thanks to the internet and, increasingly, the internet of things (IoT), you now have access to a vast amount of data that wasn’t available years ago.

DevOps

DevOps is a set of practices that combines software development and IT operations to shorten the software delivery lifecycle and improve reliability. It helps teams manage planning, development, testing, packaging, deployment, monitoring, and ongoing operations.

Python is one of the primary technologies for DevOps. Its flexibility and accessibility make Python ideal for the job, enabling development teams to improve their workflow and become more efficient and productive.

In the Python ecosystem, you’ll find many popular DevOps tools—and you can control most of them with Python. Here are a few:

To get started with DevOps, check out the following resources:

• Python DevOps Tutorials
• DevOps With Python (Learning Path)
• The subprocess Module: Wrapping Programs With Python
• Continuous Integration and Deployment for Python With GitHub Actions
• Build Robust Continuous Integration With Docker and Friends

With these resources, you’ll develop various skills and learn to use tools and technologies that any DevOps engineer working with Python benefits from.

Development Environment

Creating a productive and effective environment for you and your teammates is a fundamental part of software development. Python has a comprehensive set of tools that enable you to isolate your packages, libraries, and Python version in per-project virtual environments.

Here are some of the most popular tools:

To learn more about building an effective development environment, check out the following resources:

• Python Development Tools Tutorials
• Perfect Your Python Development Setup (Learning Path)
• An Effective Python Environment: Making Yourself at Home
• Using Python’s pip to Manage Your Projects’ Dependencies
• Managing Python Projects With uv: An All-in-One Solution
• Managing Multiple Python Versions With pyenv

Learning how to build an effective Python environment for your development projects will push your productivity to the next level, so it’s important to spend time refining this skill.

Concurrency and Async Programming

When your program waits on the network, disk, or other slow I/O, it can feel sluggish even though your CPU is mostly idle. Concurrency lets your code make progress on other work while it waits. In Python, you’ll typically pick from three models:

• Threads or concurrent.futures for many simple I/O-bound tasks
• asyncio for highly concurrent I/O with async and await
• multiprocessing for CPU-bound tasks to bypass the GIL and use multiple cores

A good rule of thumb is to use threads or asyncio for I/O-bound workloads, and multiprocessing for CPU-bound parallelism.

Here are popular options for concurrency and asynchronous programming in Python:

To get started with concurrency and asynchronous programming in Python, check out the following resources:

• Concurrency and Async Programming (Learning Path)
• Speed Up Your Python Program With Concurrency
• Python’s asyncio: A Hands-On Walkthrough
• An Intro to Threading in Python
• Bypassing the GIL for Parallel Processing in Python
• Socket Programming in Python (Guide)

If you want to start with a hands-on project, then try building an async HTTP downloader using asyncio and aiohttp by following the course Hands-On Python 3 Concurrency With the asyncio Module. Then, compare its performance with a ThreadPoolExecutor to understand the trade-offs between the different concurrency models.

Software Packaging and Deployment

Another critical part of your software development cycle is to package, distribute, and deploy your products to your end users or clients. In Python, a quick and popular way to share applications and libraries is to publish them to the Python Package Index (PyPI).

Here are some of the tools you can use for this purpose:

To get started with Python packaging, check out the following resources:

• How to Publish an Open Source Python Package to PyPI
• Using PyInstaller to Easily Distribute Python Applications
• Pipenv: A Guide to the Python Packaging Tool
• Python’s zipapp: Build Executable Zip Applications

With these resources, you can get started with packaging and deploying your Python applications and libraries to your end users, clients, and colleagues. Also, the Python Packaging Authority provides a lot of useful information and tutorials to help you distribute Python packages with modern tools.

Database Systems

Most of the applications you’ll build in your career as a developer will interact with data in some way. This interaction commonly happens through a database management system (DBMS) that allows you to define, create, maintain, and control access to your database or databases.

To connect and manipulate your databases with Python, you have several options that include both standard-library and third-party packages. You also have options for SQL and NoSQL databases in Python.

Object-relational mapping tools (ORMs) are another important type of tool you’ll probably use to work with databases in Python. These tools allow you to use object-oriented programming to create and manipulate your databases.

Here are some Python libraries you can use for connecting and operating databases:

To get started with databases, click the links in the table above and check out the resources below:

• Python Database Tutorials
• Data Collection & Storage (Learning Path)

Creating and working with databases is a powerful way to manage data in your Python applications. Databases add significant functionality and versatility to your programs, enabling you to provide exciting features to your users and clients. Managing databases is a fundamental skill in your developer education.

Software Testing

When you’re starting out with Python or programming, you might begin by creating small programs and scripts that you can run and test manually to make sure they work as expected. However, when your programs grow and get more complex, testing them by hand becomes nearly impossible. This is when automated testing comes into play.

You’ll need a reliable testing process to help you identify bugs and prevent them from reaching production. Testing can also drive your code’s design and help you check non-functional features such as performance, security, usability, regulatory compliance, and more. Testing, therefore, is an important component of software development.

Python has great tools for code testing. You can use these tools to write consistent tests and run them automatically. Here’s a brief overview of some of these tools:

To get started with testing, check out the following resources:

• Python Testing Tutorials
• Testing and Continuous Integration (Learning Path)

As a developer, you need to produce reliable code that works correctly. This means that you need to test your code every time you change it or add new features. Automated tests are the way to go in these situations.

Beyond testing your code’s functionality, you’ll also want to ensure it follows best practices. To stay on top of this, learn about Python Code Quality: Best Practices and Tools and linters such as Ruff: A Modern Python Linter for Error-Free and Maintainable Code.

Cybersecurity and Ethical Hacking

Python has gained popularity among cybersecurity professionals and ethical hackers with its clean syntax, cross-platform support, and rich ecosystem of specialized libraries. It excels at building tools for network scanning, exploitation, reconnaissance, and automation, making it indispensable in building and testing digital defenses.

Here are some of the most useful Python libraries and modules for security and ethical hacking:

Only run these and related tools on systems you own or have explicit permission to test. Unauthorized scanning or hacking—even with good intentions—is illegal and unethical.