# modin-project/modin

Modin: Scale your Pandas workflows by changing a single line of code

Repository: https://github.com/modin-project/modin
Canonical: https://ross.abutalabs.com/products/modin
Homepage: http://modin.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: dataframe, pandas, distributed, datascience, modin, sql, python, analytics, data-science
Last push: 2026-02-10T20:52:42+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 66, release rhythm 50, longevity 100
- inputs: {"age_days": 2995, "days_push": 204, "days_rel": 335, "gap_med": 20.0, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10390, forks 680 (observed 2026-08-28T04:10:41.851368+00:00)

## What it is
Modin is a drop-in replacement for pandas that scales DataFrame operations across all CPU cores using Ray, Dask, or Unidist as execution engines. Users speed up existing pandas workflows by changing only the import statement, with support for larger-than-memory datasets.

## Use cases
- speed up slow pandas scripts by parallelizing across cores
- work with datasets too large to fit in memory with pandas
- migrate existing pandas notebooks to distributed execution with minimal code changes
- process large CSV files faster than pandas
- run pandas-style analytics on a cluster without rewriting code

## When to choose
- you have existing pandas code that is slow or memory-bound
- you want pandas API compatibility without learning a new framework
- you need to scale from a laptop to a cluster with the same code

## When to avoid
- your datasets are small and pandas performance is adequate
- you need APIs or behaviors not yet implemented in Modin
- you require fine-grained control over distributed execution semantics

## Facets
- artifact type: library
- maturity: active
- function: data-science, etl, analytics, streaming
- domain: data-science, big-data, analytics
- platform: python, windows, cloud
- tags: pandas, dataframe, distributed-computing, ray, dask, drop-in-replacement, parallel-computing, data-engineering, linux, macos

## Member repositories
- modin-project/modin (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:41.851368+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 2026-08-29T17:19:05.376561+00:00, confidence not recorded.
  - readme: https://github.com/modin-project/modin (fetched 2026-08-28T04:10:41.851368+00:00, sha a5bafe0c3e6f)
  - homepage: http://modin.readthedocs.io (fetched 2026-08-29T08:18:15.945927+00:00, sha f151042314a6)
  - registry_pypi: https://pypi.org/pypi/modin/json (fetched 2026-08-29T08:18:15.955249+00:00, sha 9de3183d3713)
- Data as of 2026-08-30T08:39:29.467469+00:00.