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A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
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Latest commit 6946bac Aug 15, 2019

README.md

swifter

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

PyPI version CircleCI Code style: black GitHub stars PyPI - Downloads

To know about latest improvements, please check changelog.

Installation:

$ pip install -U pandas # upgrade pandas
$ pip install swifter # first time installation

$ pip install -U swifter # upgrade to latest version if already installed

alternatively, to install on Anaconda:

conda install -c conda-forge swifter

...after installing, import swifter into your code along with pandas using:

import pandas as pd
import swifter

Easy to use

df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 6, 7, 8]})

# runs on single core
df['x2'] = df['x'].apply(lambda x: x**2)
# runs on multiple cores
df['x2'] = df['x'].swifter.apply(lambda x: x**2)

# use swifter apply on whole dataframe
df['agg'] = df.swifter.apply(lambda x: x.sum() - x.min())

# use swifter apply on specific columns
df['outCol'] = df[['inCol1', 'inCol2']].swifter.apply(my_func)
df['outCol'] = df[['inCol1', 'inCol2', 'inCol3']].swifter.apply(my_func,
             positional_arg, keyword_arg=keyword_argval)

Further documentations on swifter is available here.

Check out the examples notebook, along with the speed benchmark notebook

Vectorizes your function, when possible

Alt text Alt text

When vectorization is not possible, automatically decides which is faster: to use dask parallel processing or a simple pandas apply

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Notes

  1. The function is documented in the .py file. In Jupyter Notebooks, you can see the docs by pressing Shift+Tab(x3). Also, check out the complete documentation here along with the changelog.

  2. Please upgrade your version of pandas, as the pandas extension api used in this module is a recent addition to pandas.

  3. It is advised to disable the progress bar if calling swifter from a forked process as the progress bar may get confused between various multiprocessing modules.

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