Swifter

Swifter is an open-source Python library that speeds up pandas apply-style operations on DataFrame and Series objects. It automatically selects an execution engine—vectorized operations, Dask-based parallelism, or multiprocessing—to speed up computations while preserving pandas semantics.

Operation and engine selection: Swifter evaluates the function passed to apply and the input size, routing the task to a fast path when possible. If the function is vectorizable, it uses numpy/pandas vectorization; otherwise it may run in parallel across cores or with Dask for larger datasets. It also provides an optional progress indicator and falls back to the standard pandas apply if speedups are impractical.

API and usage: Swifter integrates with pandas by extending objects with a swifter accessor. Users typically call apply through this accessor, such as by applying a function to a Series or DataFrame column. The library aims to be drop-in compatible and returns results consistent with pandas.

Limitations and considerations: Benefits depend on data size, function complexity, and environment. Small datasets or simple functions can incur overhead. Some functions do not parallelize well or rely on non-thread-safe code. Swifter optionally relies on external backends and extra dependencies, which may require installation and configuration.

Origin and status: Swifter is an open-source project hosted on GitHub and maintained by contributors in the Python data science community.