# nalepae/pandarallel

A simple and efficient tool to parallelize Pandas operations on all available CPUs

Repository: https://github.com/nalepae/pandarallel
Canonical: https://ross.abutalabs.com/products/pandarallel
Homepage: https://nalepae.github.io/pandarallel
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: pandas, python, parallel
Last push: 2024-07-09T18:59:05+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2733, "days_push": 785, "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 3799, forks 213 (observed 2026-08-28T04:08:20.089512+00:00)

## What it is
Pandaral·lel is a Python library that parallelizes pandas operations across all available CPU cores by changing only one line of code (e.g., df.apply becomes df.parallel_apply). It supports apply, applymap, map, groupby, rolling, and expanding APIs, and displays progress bars.

## Use cases
- speed up pandas apply on large dataframes
- parallelize groupby apply across cores
- use all CPUs for pandas map operations
- add progress bars to long pandas computations
- parallelize rolling window apply functions

## When to choose
- you have CPU-bound pandas apply/map/groupby workloads on a multi-core machine
- you want a minimal one-line change instead of rewriting code with multiprocessing or Dask
- you want progress bars for long-running pandas operations

## When to avoid
- your operations are I/O-bound or already vectorized, where parallelization overhead outweighs gains
- you need distributed computing across multiple machines (use Dask or Ray instead)
- you need a project with active development, as it is seeking a new maintainer

## Facets
- artifact type: library
- maturity: maintenance
- function: concurrency, data-science
- domain: data-science, developer-tools, performance
- platform: python, windows, cross-platform
- tags: pandas, parallelization, multiprocessing, dataframe, progress-bar, linux, macos

## Member repositories
- nalepae/pandarallel (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:20.089512+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 2026-08-29T18:26:54.769979+00:00, confidence not recorded.
  - readme: https://github.com/nalepae/pandarallel (fetched 2026-08-28T04:08:20.089512+00:00, sha 881e1d254b40)
  - homepage: https://nalepae.github.io/pandarallel (fetched 2026-08-29T09:21:49.823758+00:00, sha 9e18fb562312)
- Data as of 2026-08-30T08:39:29.467469+00:00.