---
id: modin
version: "0.37.1"
license: Apache 2
license_treatment: permissive
maintenance: aging
---

# modin — Modin: Make your pandas code run faster by changing one line of code.

License: permissive · Maintenance: aging · Downloads: 1.4M/mo

## What it is and what it does

Modin lets you run pandas code faster by automatically distributing operations across all available CPU cores instead of using a single thread. You import it exactly like pandas—`import modin.pandas as pd` instead of `import pandas as pd`—and your existing code runs unchanged but potentially much faster, especially on datasets larger than available memory or when pandas becomes a bottleneck.

Under the hood, Modin partitions your DataFrames and distributes computation across a cluster. It handles all the complexity of scheduling and data movement, so you don't need to rewrite your logic. The package depends on pandas, numpy, fsspec, psutil, packaging, and typing-extensions, and you choose which compute engine to install separately based on your environment.

Use it for:

- Accelerate data cleaning and transformation pipelines on multi-gigabyte CSV or Parquet files that pandas loads slowly.
- Process datasets larger than available RAM by leveraging distributed memory across multiple cores.
- Speed up exploratory data analysis workflows without rewriting pandas code for a different framework.
- Scale batch analytics jobs that currently timeout or run out of memory with single-threaded pandas.
- Parallelize read operations across multiple workers for faster I/O on large files.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Modin is a drop-in replacement for pandas that distributes DataFrame operations across multiple CPU cores, enabling faster execution on larger datasets that would otherwise exhaust memory or run slowly with single-threaded pandas.

Yes, if you have multi-core hardware and pandas workflows on datasets large enough to benefit from parallelization. The low install friction, permissive license, and drop-in API make it a practical choice for accelerating existing code. However, the 316-day gap since the last release suggests slower maintenance cadence; verify that your specific pandas operations are fully supported before committing to production use, and test actual speedup on your hardware and data size.

## Install

pip install modin
uv add modin
poetry add modin

## Installing modin

Before you install: Low install friction with a pure-Python wheel and six well-maintained runtime dependencies. The package is aging (316 days since last release) but remains actively maintained with a substantial community (10392 stars). Requires Python 3.9 or later.

License in practice: Licensed under Apache 2, a permissive open-source license that allows commercial use, modification, and redistribution with minimal restrictions, making suitable for most production environments.

Quickstart:

pip install modin[ray]

import modin.pandas as pd
df = pd.read_csv('large_file.csv')
result = df.groupby('column').sum()

Requires a compute engine to be installed separately; Python 3.9 or later required.

Verify before relying:

- Actual speedup magnitude and memory savings depend on dataset size, operation type, and hardware; documentation claims improvements but real-world gains vary.
- API coverage completeness: some pandas operations may not be fully supported across all three compute engines.
- Performance overhead of the distribution layer on small datasets or simple operations is not quantified.

## Package facts

- License: Apache 2 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 1.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

parallel pandas dataframe, distributed pandas replacement, speed up pandas workflows, multi-core dataframe processing, scale pandas to multiple cores, pandas performance acceleration, out-of-core dataframe operations, dataframe-acceleration, distributed-computing, pandas-compatible

[View on SkillFed](https://skillfed.io/packages/modin) · [View on PyPI](https://pypi.org/project/modin/)