Episode 289: Limitations in Human and Automated Code Review

The Real Python Podcast

Mar 27, 2026 51m intermediate ai data-science django

With the mountains of Python code that it’s possible to generate now, how’s your code review going? What are the limitations of human review, and where does machine review excel? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Episode Sponsor:

We discuss a recent piece from Glyph titled, “What Is Code Review For?” We dig into the limitations of human review and where software tools like linters and formatters can help you. We cover the challenges developers and open-source maintainers face with the rise of LLM-generated code and pull requests.

We also share other articles and projects from the Python community, including a collection of recent releases and announcements, creating publication-ready tables from DataFrames, choosing the right Python task queue, mastering context managers, statically checking Python dicts for completeness, an open-source inventory management system, and an ORM-based backend for Django tasks.

This episode is sponsored by SerpApi.

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Topics:

00:00:00 – Introduction
00:02:32 – DuckDB 1.5.0 Released
00:03:11 – PyPy v7.3.21 Released
00:03:30 – Sunsetting Jazzband
00:04:08 – Thoughts on OpenAI acquiring Astral and uv/ruff/ty
00:05:19 – Great Tables: Publication-Ready Tables From DataFrames
00:10:24 – Comparing PDF Table Extraction Tools
00:11:53 – Sponsor: SerpApi
00:12:55 – Choosing the Right Python Task Queue
00:16:57 – Mastering Python Context Managers
00:22:40 – Statically Checking Python Dicts for Completeness
00:25:00 – Spotlight: Intermediate Python Deep Dive
00:26:16 – What Is Code Review For?
00:43:48 – usdatasets: Installable Collection of Datasets on USA
00:45:22 – InvenTree: OSS Inventory Management System
00:48:01 – django-tasks-db: An ORM-based Backend for Django Tasks
00:49:41 – Thanks and goodbye

News:

Show Links:

Great Tables: Publication-Ready Tables From DataFrames – Learn how to create publication-ready tables from Pandas and Polars DataFrames using Great Tables. Format currencies, add sparklines, apply conditional styling, and export to PNG.
Comparing PDF Table Extraction Tools – This article explores three Python tools for PDF table extraction: Docling, Marker, and LlamaParse. Learn which handles merged cells and multi-level headers best.
Choosing the Right Python Task Queue – Python has great options for task queues. Choosing between Celery and RQ isn’t an easy decision. Jump in and learn how each option compares!
Mastering Python Context Managers – Go beyond just using open() and learn how Python context managers work and where they are useful.
Statically Checking Python Dicts for Completeness – To keep code concerns separate, you might have two data structures (like an Enum and a dict) that are supposed to change in sequence: adding a value to the Enum requires you to add a similar value in the dict. This is common when separating business logic from UI code. This article shows you ways of making sure the corresponding changes happen together.

Discussion:

What Is Code Review For? – This post explores just what you should and should not use code reviews for. Learn when to use linters to catch problems vs when human review is important.
Your job is to deliver code you have proven to work

Projects:

Additional Links:

Level Up Your Python Skills With These Courses:

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Asynchronous Tasks With Django and Celery

Creating Asynchronous Tasks With Celery and Django

Ruff: A Modern Linter for Error-Free and Maintainable Code

Modern Python Linting With Ruff

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