nschloe/perfplot
:chart_with_upwards_trend: Performance analysis for Python snippets observed · 2026-08-28
Health v2 · maintenance only
41/100
- Activity 40
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3480
- days_rel: n/a
- days_push: 360
- n_releases_24m: 0
Adoption not part of the score
1387 stars · 64 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python library that extends timeit by benchmarking code snippets across varying input parameters and plotting the timing results. It is useful for comparing the performance of alternative implementations as data size grows.
Use cases
- benchmark python functions with different input sizes
- compare numpy array operation performance
- plot timing results of code snippets
- find the fastest implementation for large inputs
- verify snippets produce equal results while timing them
When to choose
- you need to compare several implementations' runtime scaling with input size
- you want timeit plus automatic plotting of results
When to avoid
- you need line-by-line profiling or memory profiling rather than timing comparisons
- you are benchmarking non-Python code
Facets
library · maturity active
benchmarking data-visualization testing developer-tools performance data-visualization python cross-platform cli timeit profiling matplotlib numpy plotting python
2 sources
- readme: https://github.com/nschloe/perfplot · fetched 2026-08-28 · 2df0fcfe86dd
- registry_pypi: https://pypi.org/pypi/perfplot/json · fetched 2026-08-29 · 74ff819c049b
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| nschloe/perfplot | main | 41 |
For agents
markdown · JSON · MCP: product_card(name="nschloe/perfplot")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem