"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"
Maintainable Python relies on clear comments, expressive variable and function names, avoidance of magic numbers, and writing code with future changes in mind. Consistent formatting, small focused functions, and meaningful abstractions reduce cognitive load and simplify refactoring. Automated tooling such as linters and formatters (for example, Ruff) enforces style and catches issues early. Choosing appropriate libraries (Polars versus pandas) improves performance and readability for data work. Generating realistic test data with tools like Faker and customizing the REPL speeds development. Lightweight Django utilities and GUI toolkits can streamline common interface and metadata tasks.
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