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pandas-select: Supercharged DataFrame indexing

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pandas-select is a collection of DataFrame selectors that facilitates indexing and selecting data, fully compatible with pandas vanilla indexing.

The selector functions can choose variables based on their name <https://pandas-select.readthedocs.io/en/latest/reference/label_selectors.html>, data type <https://pandas-select.readthedocs.io/en/latest/reference/label_selection.html#data-type-selectors>, arbitrary conditions <https://pandas-select.readthedocs.io/en/latest/reference/api/pandas_select.label.LabelMask.htmlk>, or any combination of these <https://pandas-select.readthedocs.io/en/latest/reference/label_selection.html#logical-operators>.

pandas-select is inspired by the excellent R library tidyselect <https://tidyselect.r-lib.org/reference/language.html>_.

.. installation-start

Installation

pandas-select is a Python-only package hosted on PyPI <https://pypi.org/project/pandas-select/>. It can be installed via pip <https://pip.pypa.io/en/stable/>:

.. code-block:: console

pip install pandas-select

.. installation-end

Design goals

• Fully compatible with the pandas.DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>_ [] operator and the pandas.DataFrame.loc <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html?highlight=loc#pandas.DataFrame.loc>_ accessor.

• Emphasise readability and conciseness by cutting boilerplate:

.. code-block:: python

pandas-select

df[AllNumeric()]

vanilla

df.select_dtypes("number").columns

pandas-select

df[StartsWith("Type") | "Legendary"]

vanilla

df.loc[:, df.columns.str.startswith("Type") | (df.columns == "Legendary")]

• Ease the challenges of indexing with hierarchical index <https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-indexing-with-hierarchical-index>_
  and offers an alternative to slicers <https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-mi-slicers>_
  when the labels cannot be listed manually.

.. code-block:: python

# pandas-select
df_mi.loc[Contains("Jeff", axis="index", level="Name")]

# vanilla
df_mi.loc[df_mi.index.get_level_values("Name").str.contains("Jeff")]

• Play well with machine learning applications.

  • Respect the columns order.

  • Allow deferred selection when the DataFrame's columns are not known in advance, for example in automated machine learning applications.

  • Offer integration with sklearn <https://scikit-learn.org/stable/>_.

    .. code-block:: python

    from pandas_select import AnyOf, AllBool, AllNominal, AllNumeric, ColumnSelector
    from sklearn.compose import make_column_transformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    
    ct = make_column_transformer(
       (StandardScaler(), ColumnSelector(AllNumeric() & ~AnyOf("Generation"))),
       (OneHotEncoder(), ColumnSelector(AllNominal() | AllBool() | "Generation")),
    )
    ct.fit_transform(df)
    

Project Information

pandas-select is released under the BS3 <https://choosealicense.com/licenses/bsd-3-clause/>_ license, its documentation lives at Read the Docs <https://pandas-select.readthedocs.io/>, the code on GitHub <https://github.com/jeffzi/pandas-select>, and the latest release on PyPI <https://pypi.org/project/pandas-select/>_. It is tested on Python 3.6+.