# nschloe/perfplot

:chart_with_upwards_trend: Performance analysis for Python snippets

Repository: https://github.com/nschloe/perfplot
Canonical: https://ross.abutalabs.com/products/perfplot
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: python, performance-analysis
Last push: 2025-09-08T00:07:46+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 40, release rhythm 8, longevity 100
- inputs: {"age_days": 3480, "days_push": 360, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1387, forks 64 (observed 2026-08-28T04:04:35.182533+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: benchmarking, data-visualization, testing
- domain: developer-tools, performance, data-visualization
- platform: python, cross-platform, cli
- tags: timeit, profiling, matplotlib, numpy, plotting, python

## Member repositories
- nschloe/perfplot (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:35.182533+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:50.655872+00:00, confidence not recorded.
  - readme: https://github.com/nschloe/perfplot (fetched 2026-08-28T04:04:35.182533+00:00, sha 2df0fcfe86dd)
  - registry_pypi: https://pypi.org/pypi/perfplot/json (fetched 2026-08-29T11:55:13.718040+00:00, sha 74ff819c049b)
- Data as of 2026-08-30T08:39:29.467469+00:00.