DOC: Add SeriesGroupBy and DataFrameGroupBy reference pages (#48500)

* WIP

* DOC: Add SeriesGroupBy and DataFrameGroupBy reference pages

* whatnews, fix docstring of filter

* Reorder docstring sections

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
 
@@ -41,57 +45,21 @@ 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/
 
@@ -100,6 +68,7 @@ application to columns of a specific data type.
    DataFrameGroupBy.backfill
    DataFrameGroupBy.bfill
    DataFrameGroupBy.corr
+   DataFrameGroupBy.corrwith
    DataFrameGroupBy.count
    DataFrameGroupBy.cov
    DataFrameGroupBy.cumcount
@@ -111,42 +80,105 @@ 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

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
 

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

doc/source/whatsnew/v1.0.0.rst

@@ -774,7 +774,7 @@ source, you should no longer need to install Cython into your build environment
 Other API changes
 ^^^^^^^^^^^^^^^^^
 
-- :class:`core.groupby.GroupBy.transform` now raises on invalid operation names (:issue:`27489`)
+- :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` now raises on invalid operation names (:issue:`27489`)
 - :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
 - :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
 - In order to improve tab-completion, pandas does not include most deprecated attributes when introspecting a pandas object using ``dir`` (e.g. ``dir(df)``).
@@ -1232,8 +1232,8 @@ GroupBy/resample/rolling
 - Bug in :meth:`core.groupby.DataFrameGroupBy.agg` with timezone-aware datetime64 column incorrectly casting results to the original dtype (:issue:`29641`)
 - Bug in :meth:`DataFrame.groupby` when using axis=1 and having a single level columns index (:issue:`30208`)
 - Bug in :meth:`DataFrame.groupby` when using nunique on axis=1 (:issue:`30253`)
-- Bug in :meth:`GroupBy.quantile` with multiple list-like q value and integer column names (:issue:`30289`)
-- Bug in :meth:`GroupBy.pct_change` and :meth:`core.groupby.SeriesGroupBy.pct_change` causes ``TypeError`` when ``fill_method`` is ``None`` (:issue:`30463`)
+- Bug in :meth:`.DataFrameGroupBy.quantile` and :meth:`.SeriesGroupBy.quantile` with multiple list-like q value and integer column names (:issue:`30289`)
+- Bug in :meth:`.DataFrameGroupBy.pct_change` and :meth:`.SeriesGroupBy.pct_change` causes ``TypeError`` when ``fill_method`` is ``None`` (:issue:`30463`)
 - Bug in :meth:`Rolling.count` and :meth:`Expanding.count` argument where ``min_periods`` was ignored (:issue:`26996`)
 
 Reshaping

doc/source/whatsnew/v1.0.1.rst

@@ -21,7 +21,7 @@ Fixed regressions
 - Fixed regression in :class:`Series` multiplication when multiplying a numeric :class:`Series` with >10000 elements with a timedelta-like scalar (:issue:`31457`)
 - Fixed regression in ``.groupby().agg()`` raising an ``AssertionError`` for some reductions like ``min`` on object-dtype columns (:issue:`31522`)
 - Fixed regression in ``.groupby()`` aggregations with categorical dtype using Cythonized reduction functions (e.g. ``first``) (:issue:`31450`)
-- Fixed regression in :meth:`GroupBy.apply` if called with a function which returned a non-pandas non-scalar object (e.g. a list or numpy array) (:issue:`31441`)
+- Fixed regression in :meth:`.DataFrameGroupBy.apply` and :meth:`.SeriesGroupBy.apply` if called with a function which returned a non-pandas non-scalar object (e.g. a list or numpy array) (:issue:`31441`)
 - Fixed regression in :meth:`DataFrame.groupby` whereby taking the minimum or maximum of a column with period dtype would raise a ``TypeError``. (:issue:`31471`)
 - Fixed regression in :meth:`DataFrame.groupby` with an empty DataFrame grouping by a level of a MultiIndex (:issue:`31670`).
 - Fixed regression in :meth:`DataFrame.apply` with object dtype and non-reducing function (:issue:`31505`)

doc/source/whatsnew/v1.0.2.rst

@@ -17,12 +17,12 @@ Fixed regressions
 
 **Groupby**
 
-- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` which was failing on frames with :class:`MultiIndex` columns and a custom function (:issue:`31777`)
+- Fixed regression in :meth:`.DataFrameGroupBy.agg` and :meth:`.SeriesGroupBy.agg` which were failing on frames with :class:`MultiIndex` columns and a custom function (:issue:`31777`)
 - Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
 - Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
 - Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
 - Fixed regression in ``DataFrame.groupby`` raising a ``ValueError`` from an internal operation (:issue:`31802`)
-- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` calling a user-provided function an extra time on an empty input (:issue:`31760`)
+- Fixed regression in :meth:`.DataFrameGroupBy.agg` and :meth:`.SeriesGroupBy.agg` calling a user-provided function an extra time on an empty input (:issue:`31760`)
 
 **I/O**
 
@@ -104,7 +104,7 @@ Bug fixes
 - Fixed bug in :meth:`DataFrame.convert_dtypes` for series with mix of integers and strings (:issue:`32117`)
 - Fixed bug in :meth:`DataFrame.convert_dtypes` where ``BooleanDtype`` columns were converted to ``Int64`` (:issue:`32287`)
 - Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
-- Fixed bug where :meth:`pandas.core.groupby.GroupBy.first` and :meth:`pandas.core.groupby.GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
+- Fixed bug where :meth:`.DataFrameGroupBy.first`, :meth:`.SeriesGroupBy.first`, :meth:`.DataFrameGroupBy.last`, and :meth:`.SeriesGroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)
 - Fixed bug where :meth:`DataFrameGroupBy.mean`, :meth:`DataFrameGroupBy.median`, :meth:`DataFrameGroupBy.var`, and :meth:`DataFrameGroupBy.std` would raise a ``TypeError`` on ``Int64`` dtype columns (:issue:`32219`)
 
 **Strings**

doc/source/whatsnew/v1.0.4.rst

@@ -16,7 +16,7 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Fix regression where :meth:`Series.isna` and :meth:`DataFrame.isna` would raise for categorical dtype when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`33594`)
-- Fix regression in :meth:`GroupBy.first` and :meth:`GroupBy.last` where None is not preserved in object dtype (:issue:`32800`)
+- Fix regression in :meth:`.DataFrameGroupBy.first`, :meth:`.SeriesGroupBy.first`, :meth:`.DataFrameGroupBy.last`, and :meth:`.SeriesGroupBy.last` where None is not preserved in object dtype (:issue:`32800`)
 - Fix regression in DataFrame reductions using ``numeric_only=True`` and ExtensionArrays (:issue:`33256`).
 - Fix performance regression in ``memory_usage(deep=True)`` for object dtype (:issue:`33012`)
 - Fix regression where :meth:`Categorical.replace` would replace with ``NaN`` whenever the new value and replacement value were equal (:issue:`33288`)
@@ -26,7 +26,7 @@ Fixed regressions
 - Fix regression in :meth:`DataFrame.describe` raising ``TypeError: unhashable type: 'dict'`` (:issue:`32409`)
 - Fix regression in :meth:`DataFrame.replace` casts columns to ``object`` dtype if items in ``to_replace`` not in values (:issue:`32988`)
 - Fix regression in :meth:`Series.groupby` would raise ``ValueError`` when grouping by :class:`PeriodIndex` level (:issue:`34010`)
-- Fix regression in :meth:`GroupBy.rolling.apply` ignores args and kwargs parameters (:issue:`33433`)
+- Fix regression in :meth:`DataFrameGroupBy.rolling.apply` and :meth:`SeriesGroupBy.rolling.apply` ignoring args and kwargs parameters (:issue:`33433`)
 - Fix regression in error message with ``np.min`` or ``np.max`` on unordered :class:`Categorical` (:issue:`33115`)
 - Fix regression in :meth:`DataFrame.loc` and :meth:`Series.loc` throwing an error when a ``datetime64[ns, tz]`` value is provided (:issue:`32395`)
 
@@ -40,7 +40,7 @@ Bug fixes
 - Bug in :meth:`~DataFrame.to_csv` was silently failing when writing to an invalid s3 bucket. (:issue:`32486`)
 - Bug in :meth:`read_parquet` was raising a ``FileNotFoundError`` when passed an s3 directory path. (:issue:`26388`)
 - Bug in :meth:`~DataFrame.to_parquet` was throwing an ``AttributeError`` when writing a partitioned parquet file to s3 (:issue:`27596`)
-- Bug in :meth:`GroupBy.quantile` causes the quantiles to be shifted when the ``by`` axis contains ``NaN`` (:issue:`33200`, :issue:`33569`)
+- Bug in :meth:`.DataFrameGroupBy.quantile` and :meth:`.SeriesGroupBy.quantile` causes the quantiles to be shifted when the ``by`` axis contains ``NaN`` (:issue:`33200`, :issue:`33569`)
 
 Contributors
 ~~~~~~~~~~~~

doc/source/whatsnew/v1.1.0.rst

@@ -1126,16 +1126,16 @@ GroupBy/resample/rolling
 
 - Using a :class:`pandas.api.indexers.BaseIndexer` with ``count``, ``min``, ``max``, ``median``, ``skew``,  ``cov``, ``corr`` will now return correct results for any monotonic :class:`pandas.api.indexers.BaseIndexer` descendant (:issue:`32865`)
 - :meth:`DataFrameGroupby.mean` and :meth:`SeriesGroupby.mean` (and similarly for :meth:`~DataFrameGroupby.median`, :meth:`~DataFrameGroupby.std` and :meth:`~DataFrameGroupby.var`) now raise a ``TypeError`` if a non-accepted keyword argument is passed into it. Previously an ``UnsupportedFunctionCall`` was raised (``AssertionError`` if ``min_count`` passed into :meth:`~DataFrameGroupby.median`) (:issue:`31485`)
-- Bug in :meth:`GroupBy.apply` raises ``ValueError`` when the ``by`` axis is not sorted, has duplicates, and the applied ``func`` does not mutate passed in objects (:issue:`30667`)
+- Bug in :meth:`.DataFrameGroupBy.apply` and :meth:`.SeriesGroupBy.apply` raising ``ValueError`` when the ``by`` axis is not sorted, has duplicates, and the applied ``func`` does not mutate passed in objects (:issue:`30667`)
 - Bug in :meth:`DataFrameGroupBy.transform` produces an incorrect result with transformation functions (:issue:`30918`)
-- Bug in :meth:`Groupby.transform` was returning the wrong result when grouping by multiple keys of which some were categorical and others not (:issue:`32494`)
-- Bug in :meth:`GroupBy.count` causes segmentation fault when grouped-by columns contain NaNs (:issue:`32841`)
+- Bug in :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` were returning the wrong result when grouping by multiple keys of which some were categorical and others not (:issue:`32494`)
+- Bug in :meth:`.DataFrameGroupBy.count` and :meth:`.SeriesGroupBy.count` causing segmentation fault when grouped-by columns contain NaNs (:issue:`32841`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` produces inconsistent type when aggregating Boolean :class:`Series` (:issue:`32894`)
 - Bug in :meth:`DataFrameGroupBy.sum` and :meth:`SeriesGroupBy.sum` where a large negative number would be returned when the number of non-null values was below ``min_count`` for nullable integer dtypes (:issue:`32861`)
 - Bug in :meth:`SeriesGroupBy.quantile` was raising on nullable integers (:issue:`33136`)
 - Bug in :meth:`DataFrame.resample` where an ``AmbiguousTimeError`` would be raised when the resulting timezone aware :class:`DatetimeIndex` had a DST transition at midnight (:issue:`25758`)
 - Bug in :meth:`DataFrame.groupby` where a ``ValueError`` would be raised when grouping by a categorical column with read-only categories and ``sort=False`` (:issue:`33410`)
-- Bug in :meth:`GroupBy.agg`, :meth:`GroupBy.transform`, and :meth:`GroupBy.resample` where subclasses are not preserved (:issue:`28330`)
+- Bug in :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.DataFrameGroupBy.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.resample`, and :meth:`.SeriesGroupBy.resample` where subclasses are not preserved (:issue:`28330`)
 - Bug in :meth:`SeriesGroupBy.agg` where any column name was accepted in the named aggregation of :class:`SeriesGroupBy` previously. The behaviour now allows only ``str`` and callables else would raise ``TypeError``. (:issue:`34422`)
 - Bug in :meth:`DataFrame.groupby` lost the name of the :class:`Index` when one of the ``agg`` keys referenced an empty list (:issue:`32580`)
 - Bug in :meth:`Rolling.apply` where ``center=True`` was ignored when ``engine='numba'`` was specified (:issue:`34784`)

doc/source/whatsnew/v1.1.4.rst

@@ -41,7 +41,7 @@ Bug fixes
 ~~~~~~~~~
 - Bug causing ``groupby(...).sum()`` and similar to not preserve metadata (:issue:`29442`)
 - Bug in :meth:`Series.isin` and :meth:`DataFrame.isin` raising a ``ValueError`` when the target was read-only (:issue:`37174`)
-- Bug in :meth:`GroupBy.fillna` that introduced a performance regression after 1.0.5 (:issue:`36757`)
+- Bug in :meth:`.DataFrameGroupBy.fillna` and :meth:`.SeriesGroupBy.fillna` that introduced a performance regression after 1.0.5 (:issue:`36757`)
 - Bug in :meth:`DataFrame.info` was raising a ``KeyError`` when the DataFrame has integer column names (:issue:`37245`)
 - Bug in :meth:`DataFrameGroupby.apply` would drop a :class:`CategoricalIndex` when grouped on (:issue:`35792`)
 

doc/source/whatsnew/v1.1.5.rst

@@ -28,7 +28,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.fillna` not filling ``NaN`` after other operations such as :meth:`DataFrame.pivot` (:issue:`36495`).
 - Fixed performance regression in ``df.groupby(..).rolling(..)`` (:issue:`38038`)
 - Fixed regression in :meth:`MultiIndex.intersection` returning duplicates when at least one of the indexes had duplicates (:issue:`36915`)
-- Fixed regression in :meth:`.GroupBy.first` and :meth:`.GroupBy.last` where ``None`` was considered a non-NA value (:issue:`38286`)
+- Fixed regression in :meth:`.DataFrameGroupBy.first`, :meth:`.SeriesGroupBy.first`, :meth:`.DataFrameGroupBy.last`, and :meth:`.SeriesGroupBy.last` where ``None`` was considered a non-NA value (:issue:`38286`)
 
 .. ---------------------------------------------------------------------------