# jmcarpenter2/swifter

A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

Repository: https://github.com/jmcarpenter2/swifter
Canonical: https://ross.abutalabs.com/products/jmcarpenter2-swifter
Language: Python
License: MIT
License Family: permissive
Topics: pandas, pandas-dataframe, parallel-computing, parallelization, dask, modin
Last push: 2024-03-20T17:02:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3070, "days_push": 896, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2635, forks 104 (observed 2026-08-28T04:07:06.217363+00:00)

## What it is
Swifter is a Python library that speeds up pandas apply operations by automatically choosing the fastest execution strategy, whether vectorization, Dask parallelization, or standard apply. It works with pandas and modin dataframes and series with a simple one-line API.

## Use cases
- speed up pandas apply on large dataframes
- parallelize row-wise function application across cores
- automatically vectorize pandas operations when possible
- apply functions to pandas series faster
- speed up groupby.apply in pandas
- use swifter with modin dataframes

## When to choose
- you have slow pandas .apply calls on large datasets
- you want automatic selection between vectorized and parallel execution
- you use pandas or modin and want a drop-in apply speedup

## When to avoid
- your dataframes are small and apply is already fast
- you need fine-grained control over parallelization strategy
- your workload is better served by rewriting logic in vectorized pandas or polars directly

## Facets
- artifact type: library
- maturity: active
- function: concurrency, benchmarking, data-science
- domain: data-science, developer-tools, performance
- platform: python
- tags: pandas, dask, parallel-apply, vectorization, modin

## Member repositories
- jmcarpenter2/swifter (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:06.217363+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 2026-08-30T02:19:20.649621+00:00, confidence not recorded.
  - readme: https://github.com/jmcarpenter2/swifter (fetched 2026-08-28T04:07:06.217363+00:00, sha f5370a3f180b)
  - registry_pypi: https://pypi.org/pypi/swifter/json (fetched 2026-08-29T10:02:22.869161+00:00, sha 4c16796d9b72)
- Data as of 2026-08-30T08:39:29.467469+00:00.