colesbury/pyperformance — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2021-09-17
Run a standardized benchmark suite to see if a Python change made things faster or slower.
Compare performance across CPython, PyPy, and other Python implementations fairly.
Get a baseline performance measurement for your own Python setup.
Catch performance regressions automatically as part of continuous testing.
| colesbury/pyperformance | a-bissell/unleash-lite | abhiinnovates/whatsapp-hr-assistant | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Last pushed | 2021-09-17 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | moderate | hard | hard |
| Complexity | 3/5 | 4/5 | 3/5 |
| Audience | researcher | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires installing multiple Python versions or implementations to get useful comparisons.
This is a collection of performance tests designed to measure how fast Python runs. Instead of artificial tests that only measure one thing in isolation, pyperformance focuses on real-world scenarios, like running actual applications or common programming tasks, to see how different versions or implementations of Python perform in practice. Think of it like a standardized speedway where you can test different cars under the same conditions. A developer or team maintaining Python (or an alternative Python implementation) can run these benchmarks to see if changes they made actually made the language faster or slower. It's a fair, reproducible way to measure performance across different setups and Python versions. The tool is meant to be the go-to source that the entire Python community trusts. Because Python has multiple implementations (CPython is the most common, but there's also PyPy, Jython, and others), having one agreed-upon set of benchmarks helps everyone compare fairly. You can install pyperformance as a package and run it on your machine to get a baseline of how your Python setup performs, or use it as part of continuous testing to catch performance regressions before they ship. Someone working on the Python runtime itself, a performance engineer optimizing Python for their company, or a researcher comparing Python implementations would all use this suite. The README doesn't detail what specific benchmarks are included, but the philosophy is clear: real applications matter more than toy problems when it comes to understanding real-world speed.
A standardized suite of real-world Python benchmarks that lets you measure and compare how fast different Python versions or implementations actually run.
Mainly Python. The stack also includes Python.
Dormant — no commits in 2+ years (last push 2021-09-17).
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.