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Using Polars in a Pandas world

Pandas has far more third-party integrations than Polars. Learn how to use those libraries with Polars dataframes.
Article Added by: itamarst //


Python Imaging Library (Fork)
Featured Package // Category Imagery


Rust std fs slower than Python!? No, it's hardware

Article Popular Story //


The Python package installer
Featured Package // Category Package Management

Last 7 Days

The Python Rich Package: Unleash the Power of Console Text

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Bayesian Modeling and Probabilistic Programming in Python
Featured Package // Category Science and Data Analysis

PEP 734: Multiple Interpreters in the Stdlib

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🚀⚡New open-source⚡ VS. old open-source 🦖

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Official repository for Spyder - The Scientific Python Development Environment
Featured Package // Category IDEs

Python Multiprocessing: 7-Day Crash Course

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Dependencies Belong in Version Control

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Securely deploying Swirl in Azure.

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Freeze (package) Python programs into stand-alone executables
Featured Package // Category Distribution


PyZMQ: Python bindings for zeromq
Featured Package // Category Networking

Swirl Security Overview

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Task scheduling library for Python
Featured Package // Category Job Scheduler

Awesome Python Weekly » 391

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Last 30 Days


The Ren'Py Visual Novel Engine
Featured Package // Category Game Development


Declarative statistical visualization library for Python
Featured Package // Category Data Visualization

Two kinds of threads pools, and why you need both

When you’re doing large scale data processing with Python, threads are a good way to achieve parallelism. This is especially true if you’re doing numeric processing, where the global interpreter lock (GIL) is typically not an issue. And if you’re using threading, thread pools are a good way to make sure you don’t use too many resources.

But how many threads should your thread pool have? And do you need just one thread pool, or more than one?
Article Added by: itamarst //