---
id: swifter
version: "1.4.0"
license: unclear
license_treatment: unclear
maintenance: dormant
---

# swifter — A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

License: unclear · Maintenance: dormant · Downloads: 10.3M/mo

## What it is and what it does

Swifter is an extension that intercepts apply operations on DataFrames and Series, then automatically chooses the fastest execution path: vectorization when possible, or parallel processing when not. It wraps the standard apply interface, so you call `.swifter.apply()` instead of `.apply()` and let the package decide whether to run on a single core, multiple cores, or a distributed backend.

The package is designed for data-processing workflows where apply operations are a bottleneck. It includes optional extras for notebook progress bars and groupby-apply support. However, it carries a significant caveat: sample applies are run during optimization, so functions with side effects (modifying external state) will produce incorrect results.

Use it for:

- Speed up element-wise transformations on large Series by automatically vectorizing or parallelizing the operation.
- Apply complex row-wise functions without manually managing parallel backends.
- Optimize groupby().apply() chains when the function cannot be vectorized.
- Migrate existing code to parallel execution with minimal refactoring.
- Benchmark apply performance across different execution strategies in a single call.

## Worth the install?

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

Swifter applies functions to DataFrames and Series using automatic vectorization or parallel processing to speed up operations beyond standard apply.

Yes, if you have large DataFrames and apply-heavy workflows where standard apply is too slow. However, proceed with caution: maintenance is dormant (last release 2023-07-31), license status is unclear, and Python version support is unspecified. Test compatibility with your environment before relying on it in production. Not suitable for functions with side effects.

## Install

pip install swifter
uv add swifter
poetry add swifter

## Installing swifter

Before you install: Installation friction is high with no runtime dependencies listed. Maintenance is dormant—the last release was 2023-07-31, with the last commit in 2024-03-20. No active development signal.

License in practice: License status is unclear; no SPDX identifier or raw license text is available in the metadata. Verify the actual license before use in proprietary or restricted contexts.

Quickstart:

import swifter

df.swifter.apply(lambda x: x**2)

Requires an external dataframe library (pandas or modin) to be installed and imported separately before swifter.

Verify before relying:

- Actual license identifier and terms—metadata shows 'unclear' treatment with no SPDX or raw text.
- Whether dormant maintenance since 2023-07-31 affects compatibility with recent dataframe library versions.
- Python version compatibility—requires_python is empty in metadata.
- Runtime dependencies on dask or other parallel backends for full functionality.

## Package facts

- License: not declared (unclear)
- Python support: unspecified
- Install friction: high
- Maintenance: dormant
- Downloads: 10.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas apply optimization, parallel dataframe operations, fast pandas function apply, vectorize pandas apply, dask pandas acceleration, multicore dataframe apply, pandas performance boost, pandas-acceleration, parallel-processing, data-transformation

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