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Updating docstrings and adding groupby examples (#751)
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‎docs/source/_templates/_api_ref.pandas.groupby_templ.rst‎

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.. _api_ref.pandas.groupby:
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.. include:: ./../ext_links.txt
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Pandas Groupby
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=============
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.. currentmodule:: pandas.core.groupby
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Indexing, iteration
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--------------------------------
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.. sdc_toctree
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GroupBy.__iter__
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GroupBy.groups
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GroupBy.indices
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GroupBy.get_group
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Grouper
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Function application
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-----------------------------------
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.. sdc_toctree
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GroupBy.apply
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GroupBy.agg
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GroupBy.aggregate
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GroupBy.transform
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GroupBy.pipe
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Computations / descriptive stats
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----------------------------------------------
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.. sdc_toctree
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GroupBy.all
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GroupBy.any
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GroupBy.bfill
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GroupBy.count
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GroupBy.cumcount
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GroupBy.cummax
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GroupBy.cummin
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GroupBy.cumprod
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GroupBy.cumsum
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GroupBy.ffill
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GroupBy.first
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GroupBy.head
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GroupBy.last
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GroupBy.max
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GroupBy.mean
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GroupBy.median
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GroupBy.min
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GroupBy.ngroup
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GroupBy.nth
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GroupBy.ohlc
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GroupBy.prod
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GroupBy.rank
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GroupBy.pct_change
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GroupBy.size
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GroupBy.sem
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GroupBy.std
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GroupBy.sum
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GroupBy.var
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GroupBy.tail
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The following methods are available in both ``SeriesGroupBy`` and ``DataFrameGroupBy`` objects, but may differ slightly,
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usually in that the ``DataFrameGroupBy`` version usually permits the specification of an axis argument,
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and often an argument indicating whether to restrict application to columns of a specific data type.
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.. sdc_toctree
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DataFrameGroupBy.all
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DataFrameGroupBy.any
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DataFrameGroupBy.bfill
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DataFrameGroupBy.corr
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DataFrameGroupBy.count
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DataFrameGroupBy.cov
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DataFrameGroupBy.cummax
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DataFrameGroupBy.cummin
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DataFrameGroupBy.cumprod
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DataFrameGroupBy.cumsum
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DataFrameGroupBy.describe
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DataFrameGroupBy.diff
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DataFrameGroupBy.ffill
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DataFrameGroupBy.fillna
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DataFrameGroupBy.filter
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DataFrameGroupBy.hist
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DataFrameGroupBy.idxmax
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DataFrameGroupBy.idxmin
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DataFrameGroupBy.mad
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DataFrameGroupBy.nunique
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DataFrameGroupBy.pct_change
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DataFrameGroupBy.plot
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DataFrameGroupBy.quantile
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DataFrameGroupBy.rank
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DataFrameGroupBy.resample
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DataFrameGroupBy.shift
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DataFrameGroupBy.size
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DataFrameGroupBy.skew
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DataFrameGroupBy.take
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DataFrameGroupBy.tshift
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The following methods are available only for ``SeriesGroupBy`` objects.
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.. sdc_toctree
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SeriesGroupBy.nlargest
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SeriesGroupBy.nsmallest
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SeriesGroupBy.nunique
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SeriesGroupBy.unique
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SeriesGroupBy.value_counts
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SeriesGroupBy.is_monotonic_increasing
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SeriesGroupBy.is_monotonic_decreasing
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The following methods are available only for DataFrameGroupBy objects.
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.. sdc_toctree
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DataFrameGroupBy.corrwith
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DataFrameGroupBy.boxplot

‎docs/source/apireference.rst‎

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Series: Columnar Data Structure <./_api_ref/api_ref.pandas.series.rst>
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Dataframe: Tabular Data Structure <./_api_ref/api_ref.pandas.dataframe.rst>
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Window <./_api_ref/api_ref.pandas.window.rst>
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GroupBy <./_api_ref/api_ref.pandas.groupby.rst>

‎docs/source/buildscripts/apiref_generator.py‎

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'./_templates/_api_ref.pandas.series_templ.rst',
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'./_templates/_api_ref.pandas.dataframe_templ.rst',
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'./_templates/_api_ref.pandas.window_templ.rst',
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'./_templates/_api_ref.pandas.groupby_templ.rst',
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]
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‎examples/dataframe/groupby/dataframe_groupby_count.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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import numpy as np
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from numba import njit
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@njit
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def df_groupby_count():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, np.nan, np.nan, 2, 4, 3, 2, np.inf],
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'C': [np.nan, 2, 3, np.nan, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').count()
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# Expect DataFrame of
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# {'B': [1, 3, 3], 'C': [0, 3, 4} with index=[1, 2, 3]
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return out_df
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print(df_groupby_count())

‎examples/dataframe/groupby/dataframe_groupby_max.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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from numba import njit
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@njit
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def df_groupby_max():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, 5, 0, 2, 4, 3, 2, 3],
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'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').max()
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# Expect DataFrame of
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# {'B': [0, 3, 5], 'C': [4, 9, 8} with index=[1, 2, 3]
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return out_df
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print(df_groupby_max())

‎examples/dataframe/groupby/dataframe_groupby_mean.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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from numba import njit
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@njit
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def df_groupby_mean():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, 5, 0, 2, 4, 3, 2, 3],
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'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').mean()
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# Expect DataFrame of
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# {'B': [0.0, 2.0, 3.5], 'C': [2.500000, 5.333333, 6.000000} with index=[1, 2, 3]
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return out_df
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print(df_groupby_mean())

‎examples/dataframe/groupby/dataframe_groupby_median.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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from numba import njit
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@njit
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def df_groupby_median():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, 5, 0, 3, 4, 3, 2, 4],
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'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').median()
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# Expect DataFrame of
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# {'B': [0.0, 3.0, 3.5], 'C': [2.5, 5.0, 6.5} with index=[1, 2, 3]
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return out_df
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print(df_groupby_median())

‎examples/dataframe/groupby/dataframe_groupby_min.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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from numba import njit
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@njit
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def df_groupby_min():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, 5, 0, 2, 4, 3, 2, 3],
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'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').min()
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# Expect DataFrame of
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# {'B': [0, 1, 2], 'C': [1, 2, 3} with index=[1, 2, 3]
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return out_df
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print(df_groupby_min())

‎examples/dataframe/groupby/dataframe_groupby_prod.py‎

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# *****************************************************************************
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# Copyright (c) 2020, Intel Corporation All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
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# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# *****************************************************************************
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import pandas as pd
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from numba import njit
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@njit
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def df_groupby_prod():
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df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
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'B': [0, 1, 5, 0, 2, 4, 3, 2, 3],
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'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
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out_df = df.groupby('A').prod()
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# Expect DataFrame of
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# {'B': [0, 6, 120], 'C': [4, 90, 1008} with index=[1, 2, 3]
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return out_df
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print(df_groupby_prod())

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