Managing Feature Flags & Comparing Python Visualization Libraries

Sep 26, 2025

Christopher Trudeau, a regular contributor to PyCoder's Weekly, shares his insights into managing feature flags effectively. He discusses the benefits of targeting specific user groups and the challenges of growing functionality tests. The conversation also covers a detailed comparison of six popular Python visualization libraries, providing code examples to aid selection. Additionally, Trudeau highlights community projects, including a news roundup and a fun Unicode cipher, showcasing the vibrant Python ecosystem.

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INSIGHT

Matplotlib Is Supreme For Custom Static Plots

Matplotlib offers unmatched customization and publication-quality static plots.
Expect a steep learning curve and manual preprocessing for complex visualizations.

ADVICE

Choose Seaborn For Quick Statistical Plots

Use Seaborn when you want elegant statistical plots with minimal code.
Rely on Seaborn's built-in aggregations instead of manual preprocessing where possible.

ADVICE

Pick PyGal For Lightweight SVG Charts

Pick PyGal when you need lightweight, scalable SVG charts embeddable in HTML.
Use it for small projects that benefit from default hover tooltips and minimal dependencies.

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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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