swifter
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an external dataframe library (pandas or modin) to be installed and imported separately before swifter.
- 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.
License · maintenance · safety
(unclear) — 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.
last release 2023-07-31 (1110 days) · last repo commit 2024-03-20 · 2,638 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 10,262,816 downloads/mo, #1,464 on PyPI
Alternatives
Verify before relying
import swifter
df.swifter.apply(lambda x: x**2)- 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.
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 on this page. Verify before relying on it.
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
swifter on PyPI
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.
Requires an external dataframe library (pandas or modin) to be installed and imported separately before swifter.
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)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 | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 1,110 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 10,262,816 / month, #1,464 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: swifter-1.4.0.tar.gz
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See also mapply · modin · pandas · pandas-flavor · pandarallel · dask-expr · ipfn · cudf-cu12 · gspread-dataframe · awkward-pandas