January 02, 2026

PyCoder's Weekly 2025 Top Articles & Hidden Gems

32 minutes

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

Featured Links:

Data Validation Libraries for Polars (2025 Edition) – Pointblank - Given that Polars is so hot right now and that data validation is an important part of a data pipeline, this post explores five Python data validation libraries that support Polars DataFrames. Through contrasting and comparison, the post puts forward which of them are best for specific use cases.

Python: Create Temporary Files and Directories in Unittest – Sometimes, tests need temporary files or directories. You can do this in Python’s unittest with the standard library tempfile module. This post looks at some recipes to do so within individual tests and setUp().

Python: Capture Stdout and Stderr in Unittest – When testing code that outputs to the terminal through either standard out (stdout) or standard error (stderr), you might want to capture that output and make assertions on it.

I don’t like NumPy – This opinion piece talks about why NumPy gets difficult fast. Two dimensions to your array? No problem, the calc is mostly self evident. Add a couple more dimensions and it gets messy fast. See also the associated HN discussion, which also includes possible solutions.

Python performance myths and fairy tales – This post summarizes a talk by Antonio Cuni who is a long time contributor to PyPy, the alternate Python interpreter. The talk spoke about the challenges and limits of performance in Python and how the flexibility of dynamic languages comes at a cost. See also the associated HN discussion.

Additional Links:

Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

Episode #272: Michael Kennedy: Managing Your Own Python Infrastructure

Episode #510 - 10 Polars Tools and Techniques To Level Up Your Data Science

waelstow: Python unittest utilities. Includes output capture, test discovery, and directory relocation.

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spylang · GitHub

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