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

To know about latest improvements, please check changelog.

$ 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

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

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. Do not use swifter to apply a function that modifies external variables. Under the hood, swifter does sample applies to optimize performance. These sample applies will modify the external variable in addition to the final apply. Thus, you will end up with an erroneously modified external variable.

4. 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.