DEPR: DataFrameGroupBy.apply operating on the group keys

doc/source/user_guide/cookbook.rst

@@ -459,7 +459,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
    df
 
    # List the size of the animals with the highest weight.
-   df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()])
+   df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()], include_groups=False)
 
 `Using get_group
 <https://stackoverflow.com/questions/14734533/how-to-access-pandas-groupby-dataframe-by-key>`__
@@ -482,7 +482,7 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
        return pd.Series(["L", avg_weight, True], index=["size", "weight", "adult"])
 
 
-   expected_df = gb.apply(GrowUp)
+   expected_df = gb.apply(GrowUp, include_groups=False)
    expected_df
 
 `Expanding apply

doc/source/user_guide/groupby.rst

@@ -420,6 +420,12 @@ This is mainly syntactic sugar for the alternative, which is much more verbose:
 Additionally, this method avoids recomputing the internal grouping information
 derived from the passed key.
 
+You can also include the grouping columns if you want to operate on them.
+
+.. ipython:: python
+
+   grouped[["A", "B"]].sum()
+
 .. _groupby.iterating-label:
 
 Iterating through groups
@@ -1053,7 +1059,7 @@ missing values with the ``ffill()`` method.
    ).set_index("date")
    df_re
 
-   df_re.groupby("group").resample("1D").ffill()
+   df_re.groupby("group").resample("1D", include_groups=False).ffill()
 
 .. _groupby.filter:
 
@@ -1219,13 +1225,13 @@ the argument ``group_keys`` which defaults to ``True``. Compare
 
 .. ipython:: python
 
-    df.groupby("A", group_keys=True).apply(lambda x: x)
+    df.groupby("A", group_keys=True).apply(lambda x: x, include_groups=False)
 
 with
 
 .. ipython:: python
 
-    df.groupby("A", group_keys=False).apply(lambda x: x)
+    df.groupby("A", group_keys=False).apply(lambda x: x, include_groups=False)
 
 
 Numba Accelerated Routines
@@ -1709,7 +1715,7 @@ column index name will be used as the name of the inserted column:
        result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()}
        return pd.Series(result, name="metrics")
 
-   result = df.groupby("a").apply(compute_metrics)
+   result = df.groupby("a").apply(compute_metrics, include_groups=False)
 
    result
 

doc/source/whatsnew/v0.14.0.rst

@@ -328,13 +328,24 @@ More consistent behavior for some groupby methods:
 
 - groupby ``head`` and ``tail`` now act more like ``filter`` rather than an aggregation:
 
-  .. ipython:: python
+  .. code-block:: ipython
 
-     df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
-     g = df.groupby('A')
-     g.head(1)  # filters DataFrame
+     In [1]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
 
-     g.apply(lambda x: x.head(1))  # used to simply fall-through
+     In [2]: g = df.groupby('A')
+
+     In [3]: g.head(1)  # filters DataFrame
+     Out[3]:
+        A  B
+     0  1  2
+     2  5  6
+
+     In [4]: g.apply(lambda x: x.head(1))  # used to simply fall-through
+     Out[4]:
+          A  B
+     A
+     1 0  1  2
+     5 2  5  6
 
 - groupby head and tail respect column selection:
 

doc/source/whatsnew/v0.18.1.rst

@@ -77,9 +77,52 @@ Previously you would have to do this to get a rolling window mean per-group:
    df = pd.DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
    df
 
-.. ipython:: python
+.. code-block:: ipython
 
-   df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
+   In [1]: df.groupby("A").apply(lambda x: x.rolling(4).B.mean())
+   Out[1]:
+   A
+   1  0      NaN
+      1      NaN
+      2      NaN
+      3      1.5
+      4      2.5
+      5      3.5
+      6      4.5
+      7      5.5
+      8      6.5
+      9      7.5
+      10     8.5
+      11     9.5
+      12    10.5
+      13    11.5
+      14    12.5
+      15    13.5
+      16    14.5
+      17    15.5
+      18    16.5
+      19    17.5
+   2  20     NaN
+      21     NaN
+      22     NaN
+      23    21.5
+      24    22.5
+      25    23.5
+      26    24.5
+      27    25.5
+      28    26.5
+      29    27.5
+      30    28.5
+      31    29.5
+   3  32     NaN
+      33     NaN
+      34     NaN
+      35    33.5
+      36    34.5
+      37    35.5
+      38    36.5
+      39    37.5
+   Name: B, dtype: float64
 
 Now you can do:
 
@@ -101,15 +144,53 @@ For ``.resample(..)`` type of operations, previously you would have to:
 
    df
 
-.. ipython:: python
+.. code-block:: ipython
 
-   df.groupby("group").apply(lambda x: x.resample("1D").ffill())
+   In[1]: df.groupby("group").apply(lambda x: x.resample("1D").ffill())
+   Out[1]:
+                     group  val
+   group date
+   1     2016-01-03      1    5
+         2016-01-04      1    5
+         2016-01-05      1    5
+         2016-01-06      1    5
+         2016-01-07      1    5
+         2016-01-08      1    5
+         2016-01-09      1    5
+         2016-01-10      1    6
+   2     2016-01-17      2    7
+         2016-01-18      2    7
+         2016-01-19      2    7
+         2016-01-20      2    7
+         2016-01-21      2    7
+         2016-01-22      2    7
+         2016-01-23      2    7
+         2016-01-24      2    8
 
 Now you can do:
 
-.. ipython:: python
+.. code-block:: ipython
 
-   df.groupby("group").resample("1D").ffill()
+   In[1]: df.groupby("group").resample("1D").ffill()
+   Out[1]:
+                     group  val
+   group date
+   1     2016-01-03      1    5
+         2016-01-04      1    5
+         2016-01-05      1    5
+         2016-01-06      1    5
+         2016-01-07      1    5
+         2016-01-08      1    5
+         2016-01-09      1    5
+         2016-01-10      1    6
+   2     2016-01-17      2    7
+         2016-01-18      2    7
+         2016-01-19      2    7
+         2016-01-20      2    7
+         2016-01-21      2    7
+         2016-01-22      2    7
+         2016-01-23      2    7
+         2016-01-24      2    8
 
 .. _whatsnew_0181.enhancements.method_chain:
 

doc/source/whatsnew/v2.2.0.rst

@@ -146,12 +146,12 @@ Deprecations
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_pickle` except ``path``. (:issue:`54229`)
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_string` except ``buf``. (:issue:`54229`)
 - Deprecated downcasting behavior in :meth:`Series.where`, :meth:`DataFrame.where`, :meth:`Series.mask`, :meth:`DataFrame.mask`, :meth:`Series.clip`, :meth:`DataFrame.clip`; in a future version these will not infer object-dtype columns to non-object dtype, or all-round floats to integer dtype. Call ``result.infer_objects(copy=False)`` on the result for object inference, or explicitly cast floats to ints. To opt in to the future version, use ``pd.set_option("future.downcasting", True)`` (:issue:`53656`)
+- Deprecated including the groups in computations when using :meth:`DataFrameGroupBy.apply` and :meth:`DataFrameGroupBy.resample`; pass ``include_groups=False`` to exclude the groups (:issue:`7155`)
 - Deprecated not passing a tuple to :class:`DataFrameGroupBy.get_group` or :class:`SeriesGroupBy.get_group` when grouping by a length-1 list-like (:issue:`25971`)
 - Deprecated strings ``S``, ``U``, and ``N`` denoting units in :func:`to_timedelta` (:issue:`52536`)
 - Deprecated strings ``T``, ``S``, ``L``, ``U``, and ``N`` denoting frequencies in :class:`Minute`, :class:`Second`, :class:`Milli`, :class:`Micro`, :class:`Nano` (:issue:`52536`)
 - Deprecated strings ``T``, ``S``, ``L``, ``U``, and ``N`` denoting units in :class:`Timedelta` (:issue:`52536`)
 - Deprecated the extension test classes ``BaseNoReduceTests``, ``BaseBooleanReduceTests``, and ``BaseNumericReduceTests``, use ``BaseReduceTests`` instead (:issue:`54663`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_220.performance:

pandas/core/frame.py

@@ -8863,20 +8863,20 @@ def update(
         >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
         ...                               'Parrot', 'Parrot'],
         ...                    'Max Speed': [380., 370., 24., 26.]})
-        >>> df.groupby("Animal", group_keys=True).apply(lambda x: x)
-                  Animal  Max Speed
+        >>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
+                  Max Speed
         Animal
-        Falcon 0  Falcon      380.0
-               1  Falcon      370.0
-        Parrot 2  Parrot       24.0
-               3  Parrot       26.0
-
-        >>> df.groupby("Animal", group_keys=False).apply(lambda x: x)
-           Animal  Max Speed
-        0  Falcon      380.0
-        1  Falcon      370.0
-        2  Parrot       24.0
-        3  Parrot       26.0
+        Falcon 0      380.0
+               1      370.0
+        Parrot 2       24.0
+               3       26.0
+
+        >>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
+           Max Speed
+        0      380.0
+        1      370.0
+        2       24.0
+        3       26.0
         """
         )
     )