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DOC: fixed references to DataFrameGroupBy methods in comparison_with_… #12491

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11 changes: 7 additions & 4 deletions doc/source/api.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1694,16 +1694,18 @@ application to columns of a specific data type.
.. autosummary::
:toctree: generated/

DataFrameGroupBy.agg
DataFrameGroupBy.all
DataFrameGroupBy.any
DataFrameGroupBy.bfill
DataFrameGroupBy.corr
DataFrameGroupBy.count
DataFrameGroupBy.cov
DataFrameGroupBy.cummax
DataFrameGroupBy.cummin
DataFrameGroupBy.cumprod
DataFrameGroupBy.cumsum
DataFrameGroupBy.describe
DataFrameGroupBy.all
DataFrameGroupBy.any
DataFrameGroupBy.corr
DataFrameGroupBy.cov
DataFrameGroupBy.diff
DataFrameGroupBy.ffill
DataFrameGroupBy.fillna
Expand All @@ -1717,6 +1719,7 @@ application to columns of a specific data type.
DataFrameGroupBy.rank
DataFrameGroupBy.resample
DataFrameGroupBy.shift
DataFrameGroupBy.size
DataFrameGroupBy.skew
DataFrameGroupBy.take
DataFrameGroupBy.tshift
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15 changes: 8 additions & 7 deletions doc/source/comparison_with_sql.rst
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Expand Up @@ -138,7 +138,7 @@ Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.

GROUP BY
--------
In pandas, SQL's GROUP BY operations performed using the similarly named
In pandas, SQL's GROUP BY operations are performed using the similarly named
:meth:`~pandas.DataFrame.groupby` method. :meth:`~pandas.DataFrame.groupby` typically refers to a
process where we'd like to split a dataset into groups, apply some function (typically aggregation)
, and then combine the groups together.
Expand All @@ -163,23 +163,24 @@ The pandas equivalent would be:

tips.groupby('sex').size()

Notice that in the pandas code we used :meth:`~pandas.DataFrameGroupBy.size` and not
:meth:`~pandas.DataFrameGroupBy.count`. This is because :meth:`~pandas.DataFrameGroupBy.count`
applies the function to each column, returning the number of ``not null`` records within each.
Notice that in the pandas code we used :meth:`~pandas.core.groupby.DataFrameGroupBy.size` and not
:meth:`~pandas.core.groupby.DataFrameGroupBy.count`. This is because
:meth:`~pandas.core.groupby.DataFrameGroupBy.count` applies the function to each column, returning
the number of ``not null`` records within each.

.. ipython:: python

tips.groupby('sex').count()

Alternatively, we could have applied the :meth:`~pandas.DataFrameGroupBy.count` method to an
individual column:
Alternatively, we could have applied the :meth:`~pandas.core.groupby.DataFrameGroupBy.count` method
to an individual column:

.. ipython:: python

tips.groupby('sex')['total_bill'].count()

Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount
differs by day of the week - :meth:`~pandas.DataFrameGroupBy.agg` allows you to pass a dictionary
differs by day of the week - :meth:`~pandas.core.groupby.DataFrameGroupBy.agg` allows you to pass a dictionary
to your grouped DataFrame, indicating which functions to apply to specific columns.

.. code-block:: sql
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