DOC: Add SeriesGroupBy and DataFrameGroupBy reference pages

pandas-dev · mroeschke · Sep 15, 2022 · Sep 9, 2022 · Sep 9, 2022 · Sep 15, 2022
commit f36c897a6638df9fe9f1522d067c98a547700445
diff --git a/doc/source/reference/groupby.rst b/doc/source/reference/groupby.rst
@@ -14,10 +14,14 @@ Indexing, iteration
 .. autosummary::
    :toctree: api/
 
-   GroupBy.__iter__
-   GroupBy.groups
-   GroupBy.indices
-   GroupBy.get_group
+   DataFrameGroupBy.__iter__
+   SeriesGroupBy.__iter__
+   DataFrameGroupBy.groups
+   SeriesGroupBy.groups
+   DataFrameGroupBy.indices
+   SeriesGroupBy.indices
+   DataFrameGroupBy.get_group
+   SeriesGroupBy.get_group
 
 .. currentmodule:: pandas
 
@@ -34,57 +38,21 @@ Function application
 .. autosummary::
    :toctree: api/
 
-   GroupBy.apply
-   GroupBy.agg
+   SeriesGroupBy.apply
+   DataFrameGroupBy.apply
+   SeriesGroupBy.agg
+   DataFrameGroupBy.agg
    SeriesGroupBy.aggregate
    DataFrameGroupBy.aggregate
    SeriesGroupBy.transform
    DataFrameGroupBy.transform
-   GroupBy.pipe
-
-Computations / descriptive stats
---------------------------------
-.. autosummary::
-   :toctree: api/
-
-   GroupBy.all
-   GroupBy.any
-   GroupBy.bfill
-   GroupBy.backfill
-   GroupBy.count
-   GroupBy.cumcount
-   GroupBy.cummax
-   GroupBy.cummin
-   GroupBy.cumprod
-   GroupBy.cumsum
-   GroupBy.ffill
-   GroupBy.first
-   GroupBy.head
-   GroupBy.last
-   GroupBy.max
-   GroupBy.mean
-   GroupBy.median
-   GroupBy.min
-   GroupBy.ngroup
-   GroupBy.nth
-   GroupBy.ohlc
-   GroupBy.pad
-   GroupBy.prod
-   GroupBy.rank
-   GroupBy.pct_change
-   GroupBy.size
-   GroupBy.sem
-   GroupBy.std
-   GroupBy.sum
-   GroupBy.var
-   GroupBy.tail
-
-The following methods are available in both ``SeriesGroupBy`` and
-``DataFrameGroupBy`` objects, but may differ slightly, usually in that
-the ``DataFrameGroupBy`` version usually permits the specification of an
-axis argument, and often an argument indicating whether to restrict
-application to columns of a specific data type.
+   SeriesGroupBy.pipe
+   DataFrameGroupBy.pipe
+   DataFrameGroupBy.filter
+   SeriesGroupBy.filter
 
+``DataFrameGroupBy`` computations / descriptive stats
+-----------------------------------------------------
 .. autosummary::
    :toctree: api/
 
@@ -93,6 +61,7 @@ application to columns of a specific data type.
    DataFrameGroupBy.backfill
    DataFrameGroupBy.bfill
    DataFrameGroupBy.corr
+   DataFrameGroupBy.corrwith
    DataFrameGroupBy.count
    DataFrameGroupBy.cov
    DataFrameGroupBy.cumcount
@@ -104,42 +73,105 @@ application to columns of a specific data type.
    DataFrameGroupBy.diff
    DataFrameGroupBy.ffill
    DataFrameGroupBy.fillna
-   DataFrameGroupBy.filter
-   DataFrameGroupBy.hist
+   DataFrameGroupBy.first
+   DataFrameGroupBy.head
    DataFrameGroupBy.idxmax
    DataFrameGroupBy.idxmin
+   DataFrameGroupBy.last
    DataFrameGroupBy.mad
+   DataFrameGroupBy.max
+   DataFrameGroupBy.mean
+   DataFrameGroupBy.median
+   DataFrameGroupBy.min
+   DataFrameGroupBy.ngroup
+   DataFrameGroupBy.nth
    DataFrameGroupBy.nunique
+   DataFrameGroupBy.ohlc
    DataFrameGroupBy.pad
    DataFrameGroupBy.pct_change
-   DataFrameGroupBy.plot
+   DataFrameGroupBy.prod
    DataFrameGroupBy.quantile
    DataFrameGroupBy.rank
    DataFrameGroupBy.resample
    DataFrameGroupBy.sample
+   DataFrameGroupBy.sem
    DataFrameGroupBy.shift
    DataFrameGroupBy.size
    DataFrameGroupBy.skew
+   DataFrameGroupBy.std
+   DataFrameGroupBy.sum
+   DataFrameGroupBy.var
+   DataFrameGroupBy.tail
    DataFrameGroupBy.take
    DataFrameGroupBy.tshift
    DataFrameGroupBy.value_counts
 
-The following methods are available only for ``SeriesGroupBy`` objects.
-
+``SeriesGroupBy`` computations / descriptive stats
+--------------------------------------------------
 .. autosummary::
    :toctree: api/
 
-   SeriesGroupBy.hist
+   SeriesGroupBy.all
+   SeriesGroupBy.any
+   SeriesGroupBy.backfill
+   SeriesGroupBy.bfill
+   SeriesGroupBy.corr
+   SeriesGroupBy.count
+   SeriesGroupBy.cov
+   SeriesGroupBy.cumcount
+   SeriesGroupBy.cummax
+   SeriesGroupBy.cummin
+   SeriesGroupBy.cumprod
+   SeriesGroupBy.cumsum
+   SeriesGroupBy.describe
+   SeriesGroupBy.diff
+   SeriesGroupBy.ffill
+   SeriesGroupBy.fillna
+   SeriesGroupBy.first
+   SeriesGroupBy.head
+   SeriesGroupBy.last
+   SeriesGroupBy.idxmax
+   SeriesGroupBy.idxmin
+   SeriesGroupBy.is_monotonic_increasing
+   SeriesGroupBy.is_monotonic_decreasing
+   SeriesGroupBy.mad
+   SeriesGroupBy.max
+   SeriesGroupBy.mean
+   SeriesGroupBy.median
+   SeriesGroupBy.min
+   SeriesGroupBy.ngroup
    SeriesGroupBy.nlargest
    SeriesGroupBy.nsmallest
+   SeriesGroupBy.nth
+   SeriesGroupBy.nunique
    SeriesGroupBy.unique
-   SeriesGroupBy.is_monotonic_increasing
-   SeriesGroupBy.is_monotonic_decreasing
-
-The following methods are available only for ``DataFrameGroupBy`` objects.
-
+   SeriesGroupBy.ohlc
+   SeriesGroupBy.pad
+   SeriesGroupBy.pct_change
+   SeriesGroupBy.prod
+   SeriesGroupBy.quantile
+   SeriesGroupBy.rank
+   SeriesGroupBy.resample
+   SeriesGroupBy.sample
+   SeriesGroupBy.sem
+   SeriesGroupBy.shift
+   SeriesGroupBy.size
+   SeriesGroupBy.skew
+   SeriesGroupBy.std
+   SeriesGroupBy.sum
+   SeriesGroupBy.var
+   SeriesGroupBy.tail
+   SeriesGroupBy.take
+   SeriesGroupBy.tshift
+   SeriesGroupBy.value_counts
+
+Plotting and visualization
+--------------------------
 .. autosummary::
    :toctree: api/
 
-   DataFrameGroupBy.corrwith
    DataFrameGroupBy.boxplot
+   DataFrameGroupBy.hist
+   SeriesGroupBy.hist
+   DataFrameGroupBy.plot
+   SeriesGroupBy.plot
diff --git a/doc/source/user_guide/10min.rst b/doc/source/user_guide/10min.rst
@@ -528,15 +528,15 @@ See the :ref:`Grouping section <groupby>`.
    )
    df
 
-Grouping and then applying the :meth:`~pandas.core.groupby.GroupBy.sum` function to the resulting
+Grouping and then applying the :meth:`~pandas.core.groupby.DataFrameGroupBy.sum` function to the resulting
 groups:
 
 .. ipython:: python
 
    df.groupby("A")[["C", "D"]].sum()
 
 Grouping by multiple columns forms a hierarchical index, and again we can
-apply the :meth:`~pandas.core.groupby.GroupBy.sum` function:
+apply the :meth:`~pandas.core.groupby.DataFrameGroupBy.sum` function:
 
 .. ipython:: python
 

diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
@@ -632,7 +632,7 @@ Named aggregation
 .. versionadded:: 0.25.0
 
 To support column-specific aggregation *with control over the output column names*, pandas
-accepts the special syntax in :meth:`GroupBy.agg`, known as "named aggregation", where
+accepts the special syntax in :meth:`DataFrameGroupBy.agg` and :meth:`SeriesGroupBy.agg`, known as "named aggregation", where
 
 - The keywords are the *output* column names
 - The values are tuples whose first element is the column to select

diff --git a/pandas/core/generic.py b/pandas/core/generic.py
@@ -9087,7 +9087,8 @@ def rank(
 
         See Also
         --------
-        core.groupby.GroupBy.rank : Rank of values within each group.
+        core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
+        core.groupby.SeriesGroupBy.rank : Rank of values within each group.
 
         Examples
         --------

@@ -2522,9 +2522,9 @@ def first(self, numeric_only: bool = False, min_count: int = -1):
         --------
         DataFrame.groupby : Apply a function groupby to each row or column of a
             DataFrame.
-        DataFrame.core.groupby.GroupBy.last : Compute the last non-null entry of each
-            column.
-        DataFrame.core.groupby.GroupBy.nth : Take the nth row from each group.
+        pandas.core.groupby.DataFrameGroupBy.last : Compute the last non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
 
         Examples
         --------
@@ -2594,9 +2594,9 @@ def last(self, numeric_only: bool = False, min_count: int = -1):
         --------
         DataFrame.groupby : Apply a function groupby to each row or column of a
             DataFrame.
-        DataFrame.core.groupby.GroupBy.first : Compute the first non-null entry of each
-            column.
-        DataFrame.core.groupby.GroupBy.nth : Take the nth row from each group.
+        pandas.core.groupby.DataFrameGroupBy.first : Compute the first non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
 
         Examples
         --------

diff --git a/scripts/validate_rst_title_capitalization.py b/scripts/validate_rst_title_capitalization.py
@@ -69,6 +69,8 @@
     "CategoricalIndex",
     "Categorical",
     "GroupBy",
+    "DataFrameGroupBy",
+    "SeriesGroupBy",
     "SPSS",
     "ORC",
     "R",