DOC: some clean-up of the apply docs (follow-up #18577) (#19573)

doc/source/basics.rst

@@ -774,9 +774,9 @@ We encourage you to view the source code of :meth:`~DataFrame.pipe`.
 Row or Column-wise Function Application
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Arbitrary functions can be applied along the axes of a DataFrame or Panel
+Arbitrary functions can be applied along the axes of a DataFrame
 using the :meth:`~DataFrame.apply` method, which, like the descriptive
-statistics methods, take an optional ``axis`` argument:
+statistics methods, takes an optional ``axis`` argument:
 
 .. ipython:: python
 
@@ -794,13 +794,15 @@ The :meth:`~DataFrame.apply` method will also dispatch on a string method name.
    df.apply('mean', axis=1)
 
 The return type of the function passed to :meth:`~DataFrame.apply` affects the
-type of the ultimate output from DataFrame.apply
+type of the final output from ``DataFrame.apply`` for the default behaviour:
 
-* If the applied function returns a ``Series``, the ultimate output is a ``DataFrame``.
+* If the applied function returns a ``Series``, the final output is a ``DataFrame``.
   The columns match the index of the ``Series`` returned by the applied function.
-* If the applied function returns any other type, the ultimate output is a ``Series``.
-* A ``result_type`` kwarg is accepted with the options: ``reduce``, ``broadcast``, and ``expand``.
-  These will determine how list-likes return results expand (or not) to a ``DataFrame``.
+* If the applied function returns any other type, the final output is a ``Series``.
+
+This default behaviour can be overridden using the ``result_type``, which
+accepts three options: ``reduce``, ``broadcast``, and ``expand``.
+These will determine how list-likes return values expand (or not) to a ``DataFrame``.
 
 :meth:`~DataFrame.apply` combined with some cleverness can be used to answer many questions
 about a data set. For example, suppose we wanted to extract the date where the

doc/source/whatsnew/v0.23.0.txt

@@ -334,20 +334,20 @@ Convert to an xarray DataArray
 
 .. _whatsnew_0230.api_breaking.apply:
 
-Apply Changes
-~~~~~~~~~~~~~
+Changes to make output of ``DataFrame.apply`` consistent
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 :func:`DataFrame.apply` was inconsistent when applying an arbitrary user-defined-function that returned a list-like with ``axis=1``. Several bugs and inconsistencies
 are resolved. If the applied function returns a Series, then pandas will return a DataFrame; otherwise a Series will be returned, this includes the case
-where a list-like (e.g. ``tuple`` or ``list`` is returned), (:issue:`16353`, :issue:`17437`, :issue:`17970`, :issue:`17348`, :issue:`17892`, :issue:`18573`,
-:issue:`17602`, :issue:`18775`, :issue:`18901`, :issue:`18919`)
+where a list-like (e.g. ``tuple`` or ``list`` is returned) (:issue:`16353`, :issue:`17437`, :issue:`17970`, :issue:`17348`, :issue:`17892`, :issue:`18573`,
+:issue:`17602`, :issue:`18775`, :issue:`18901`, :issue:`18919`).
 
 .. ipython:: python
 
     df = pd.DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1, columns=['A', 'B', 'C'])
     df
 
-Previous Behavior. If the returned shape happened to match the original columns, this would return a ``DataFrame``.
+Previous Behavior: if the returned shape happened to match the length of original columns, this would return a ``DataFrame``.
 If the return shape did not match, a ``Series`` with lists was returned.
 
 .. code-block:: python
@@ -373,7 +373,7 @@ If the return shape did not match, a ``Series`` with lists was returned.
    dtype: object
 
 
-New Behavior. The behavior is consistent. These will *always* return a ``Series``.
+New Behavior: When the applied function returns a list-like, this will now *always* return a ``Series``.
 
 .. ipython:: python
 
@@ -386,8 +386,9 @@ To have expanded columns, you can use ``result_type='expand'``
 
     df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')
 
-To have broadcast the result across, you can use ``result_type='broadcast'``. The shape
-must match the original columns.
+To broadcast the result across the original columns (the old behaviour for
+list-likes of the correct length), you can use ``result_type='broadcast'``.
+The shape must match the original columns.
 
 .. ipython:: python
 
@@ -397,7 +398,7 @@ Returning a ``Series`` allows one to control the exact return structure and colu
 
 .. ipython:: python
 
-    df.apply(lambda x: Series([1, 2, 3], index=x.index), axis=1)
+    df.apply(lambda x: Series([1, 2, 3], index=['D', 'E', 'F']]), axis=1)
 
 
 .. _whatsnew_0230.api_breaking.build_changes:
@@ -523,8 +524,8 @@ Deprecations
 - The ``is_copy`` attribute is deprecated and will be removed in a future version (:issue:`18801`).
 - ``IntervalIndex.from_intervals`` is deprecated in favor of the :class:`IntervalIndex` constructor (:issue:`19263`)
 - :func:``DataFrame.from_items`` is deprecated. Use :func:``DataFrame.from_dict()`` instead, or :func:``DataFrame.from_dict(OrderedDict())`` if you wish to preserve the key order (:issue:`17320`)
-- The ``broadcast`` parameter of ``.apply()`` is removed in favor of ``result_type='broadcast'`` (:issue:`18577`)
-- The ``reduce`` parameter of ``.apply()`` is removed in favor of ``result_type='reduce'`` (:issue:`18577`)
+- The ``broadcast`` parameter of ``.apply()`` is deprecated in favor of ``result_type='broadcast'`` (:issue:`18577`)
+- The ``reduce`` parameter of ``.apply()`` is deprecated in favor of ``result_type='reduce'`` (:issue:`18577`)
 
 .. _whatsnew_0230.prior_deprecations:
 

pandas/core/frame.py

@@ -4822,12 +4822,12 @@ def aggregate(self, func, axis=0, *args, **kwargs):
 
     def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
               result_type=None, args=(), **kwds):
-        """Applies function along input axis of DataFrame.
+        """Applies function along an axis of the DataFrame.
 
         Objects passed to functions are Series objects having index
         either the DataFrame's index (axis=0) or the columns (axis=1).
-        Return type depends on whether passed function aggregates, or the
-        reduce argument if the DataFrame is empty.
+        Final return type depends on the return type of the applied function,
+        or on the `result_type` argument.
 
         Parameters
         ----------
@@ -4863,15 +4863,18 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
                by result_type='reduce'.
 
         result_type : {'expand', 'reduce', 'broadcast, None}
-            These only act when axis=1 {columns}
+            These only act when axis=1 {columns}:
+
             * 'expand' : list-like results will be turned into columns.
             * 'reduce' : return a Series if possible rather than expanding
               list-like results. This is the opposite to 'expand'.
             * 'broadcast' : results will be broadcast to the original shape
               of the frame, the original index & columns will be retained.
-            * None : list-like results will be returned as a list
-              in a single column. However if the apply function
-              returns a Series these are expanded to columns.
+
+            The default behaviour (None) depends on the return value of the
+            applied function: list-like results will be returned as a Series
+            of those. However if the apply function returns a Series these
+            are expanded to columns.
 
             .. versionadded:: 0.23.0
 
@@ -4893,8 +4896,8 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
 
         We use this DataFrame to illustrate
 
-        >>> df = DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
-        ...                columns=['A', 'B', 'C'])
+        >>> df = pd.DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
+        ...                   columns=['A', 'B', 'C'])
         >>> df
            A  B  C
         0  1  2  3
@@ -4904,7 +4907,8 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
         4  1  2  3
         5  1  2  3
 
-        Using a ufunc
+        Using a numpy universal function (in this case the same as
+        ``np.sqrt(df)``):
 
         >>> df.apply(np.sqrt)
              A         B         C
@@ -4954,8 +4958,8 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
         4  1  2
         5  1  2
 
-        Return a Series inside the function is similar to passing
-        Passing result_type='expand'. The resulting column names
+        Returning a Series inside the function is similar to passing
+        ``result_type='expand'``. The resulting column names
         will be the Series index.
 
         >>> df.apply(lambda x: Series([1, 2], index=['foo', 'bar']), axis=1)
@@ -4967,10 +4971,10 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
         4    1    2
         5    1    2
 
-
-        Passing result_type='broadcast' will take a same shape
-        result, whether list-like or scalar and broadcast it
-        along the axis. The resulting column names will be the originals.
+        Passing ``result_type='broadcast'`` will ensure the same shape
+        result, whether list-like or scalar is returned by the function,
+        and broadcast it along the axis. The resulting column names will
+        be the originals.
 
         >>> df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')
            A  B  C

pandas/core/sparse/frame.py

@@ -861,14 +861,18 @@ def apply(self, func, axis=0, broadcast=None, reduce=None,
                by result_type='reduce'.
 
         result_type : {'expand', 'reduce', 'broadcast, None}
-            These only act when axis=1 {columns}
-            * 'expand' : list-like results will be turned into columns
+            These only act when axis=1 {columns}:
+
+            * 'expand' : list-like results will be turned into columns.
             * 'reduce' : return a Series if possible rather than expanding
-              list-like results. This is the opposite to 'expand'
-            * 'broadcast' : scalar results will be broadcast to all columns
-            * None : list-like results will be returned as a list
-              in a single column. However if the apply function
-              returns a Series these are expanded to columns.
+              list-like results. This is the opposite to 'expand'.
+            * 'broadcast' : results will be broadcast to the original shape
+              of the frame, the original index & columns will be retained.
+
+            The default behaviour (None) depends on the return value of the
+            applied function: list-like results will be returned as a Series
+            of those. However if the apply function returns a Series these
+            are expanded to columns.
 
             .. versionadded:: 0.23.0