modin
Modin: Make your pandas code run faster by changing one line of code.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a compute engine to be installed separately; Python 3.9 or later required.
- 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).
License · maintenance · safety
Apache 2 (permissive) — 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.
last release 2025-10-02 (316 days) · last repo commit 2026-02-10 · 10,392 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,441,131 downloads/mo, #3,891 on PyPI
Alternatives
Verify before relying
pip install modin[ray]
import modin.pandas as pd
df = pd.read_csv('large_file.csv')
result = df.groupby('column').sum()- 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.
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 on this page. Verify before relying on it.
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
modin on PyPI
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.
Requires a compute engine to be installed separately; Python 3.9 or later required.
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()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 the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagespandaspackagingnumpyfsspecpsutiltyping-extensions |
| Maintenance | Aging 316 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,441,131 / month, #3,891 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: modin-0.37.1-py3-none-any.whl
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