---
id: pandarallel
version: "1.6.5"
license: BSD
license_treatment: permissive
maintenance: abandoned
---

# pandarallel — An easy to use library to speed up computation (by parallelizing on multi CPUs) with pandas.

License: permissive · Maintenance: abandoned · Downloads: 513.1K/mo

## What it is and what it does

Pandarallel is a library that speeds up pandas DataFrame operations by distributing them across multiple CPU cores. Instead of rewriting your code, you initialize the library once and then swap standard pandas method calls (like `apply`) for their parallel equivalents (like `parallel_apply`). It also displays progress bars during execution.

The package is designed for data scientists and analysts who work with large DataFrames and want to leverage multicore systems without learning distributed computing frameworks. However, the project is no longer actively maintained—the last release was in May 2023, and there have been no updates for over 1200 days. This means new pandas versions, Python releases, or bug reports will not receive fixes.

Use it for:

- Speed up row-wise or column-wise transformations on large DataFrames by distributing work across available CPU cores.
- Monitor long-running pandas operations with built-in progress bars while parallelizing the computation.
- Quickly prototype parallel data processing workflows without rewriting existing pandas code to use a distributed framework.
- Process data-science pipelines that apply custom functions to millions of rows more efficiently on multi-CPU machines.

## Worth the install?

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

Pandarallel parallelizes pandas DataFrame operations across multiple CPU cores by replacing standard pandas methods with parallel equivalents, requiring only a one-line code change.

Yes, if you have large pandas DataFrames and need quick parallelization without framework overhead—but only if you can accept an abandoned package. The library is stable for its current scope, has no known vulnerabilities, and works with supported Python versions. However, do not rely on it for production systems where you need active maintenance or compatibility with future pandas/Python releases. Consider alternatives if you need ongoing support.

## Install

pip install pandarallel
uv add pandarallel
poetry add pandarallel

## Installing pandarallel

Before you install: Installation has high friction due to compiled dependencies or system-level requirements. The package is abandoned as of 1200 days since its last release, with no active maintenance—use only if you accept the risk of unpatched issues and no future updates.

License in practice: BSD is a permissive license; you can use, modify, and distribute this package freely with minimal restrictions, though you must retain the license notice.

Quickstart:

from pandarallel import pandarallel

pandarallel.initialize(progress_bar=True)
df.parallel_apply(func)

Requires Python >= 3.7; parallelization behavior differs between Mac/Linux and Windows platforms.

Verify before relying:

- Whether the package works reliably with recent pandas versions (last release was 2023-05-02).
- Whether high install friction is due to compiled dependencies or environment setup requirements.
- Compatibility status with current Python 3.x minor versions beyond the stated >= 3.7 requirement.

## Package facts

- License: BSD (permissive)
- Python support: supports_current
- Install friction: high
- Maintenance: abandoned
- Downloads: 513.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas parallel apply, multicore dataframe operations, speed up pandas with multiple cpus, parallel pandas computation, pandas parallelization library, distributed pandas operations, pandas progress bar parallel, pandas-acceleration, multiprocessing

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