add docs

Author: topper-123

doc/source/user_guide/integer_na.rst

@@ -126,13 +126,20 @@ These dtypes can be merged, reshaped & casted.
    pd.concat([df[["A"]], df[["B", "C"]]], axis=1).dtypes
    df["A"].astype(float)
 
-Reduction and groupby operations such as 'sum' work as well.
+Reduction and groupby operations such as :meth:`~DataFrame.sum` work as well.
 
 .. ipython:: python
 
+   df.sum(numeric_only=True)
    df.sum()
    df.groupby("B").A.sum()
 
+.. versionchanged:: 2.1.0
+
+    When doing reduction operations (:meth:`~DataFrame.sum` etc.) on numeric-only data
+    frames the integer array dtype will be maintained. Previously, the dtype of reduction
+    result would have been a numpy numeric dtype.
+
 Scalar NA Value
 ---------------
 

doc/source/user_guide/pyarrow.rst

@@ -152,6 +152,12 @@ The following are just some examples of operations that are accelerated by nativ
    ser_dt = pd.Series([datetime(2022, 1, 1), None], dtype=pa_type)
    ser_dt.dt.strftime("%Y-%m")
 
+.. versionchanged:: 2.1.0
+
+    When doing :class:`DataFrame` reduction operations (:meth:`~DataFrame.sum` etc.) on
+    pyarrow data the dtype now will be maintained when possible. Previously, the dtype
+    of reduction result would have been a numpy numeric dtype.
+
 I/O Reading
 -----------
 

doc/source/whatsnew/v2.1.0.rst

@@ -14,10 +14,43 @@ including other versions of pandas.
 Enhancements
 ~~~~~~~~~~~~
 
-.. _whatsnew_210.enhancements.enhancement1:
+.. _whatsnew_210.enhancements.reduction_extension_dtypes:
 
-enhancement1
-^^^^^^^^^^^^
+Reductions maintain extension dtypes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In previous versions of pandas, the results of DataFrame reductions
+(:meth:`DataFrameG.sum` :meth:`DataFrame.mean` etc.) has numpy dtypes even when the DataFrames
+were of extension dtypes. Pandas can now keep the dtypes when doing reductions over Dataframe
+columns with a common dtype (:issue:``52788`).
+
+*New Behavior*
+
+.. code-block:: ipython
+
+    In [1]: df = pd.DataFrame({"a": [1, 1, 2, 1], "b": [np.nan, 2.0, 3.0, 4.0]}, dtype="Int64")
+    In [2]: df.sum()
+    Out[2]:
+    a    5
+    b    9
+    dtype: int64
+    In [3]: df = df.astype("int64[pyarrow]")
+    In [4]: df.sum()
+    Out[4]:
+    a    5
+    b    9
+    dtype: int64
+
+*New Behavior*
+
+.. ipython:: python
+
+    df = pd.DataFrame({"a": [1, 1, 2, 1], "b": [np.nan, 2.0, 3.0, 4.0]}, dtype="Int64")
+    df.sum()
+    df = df.astype("int64[pyarrow]")
+    df.sum()
+
+Notice that the dtype is now a masked dtype and pyarrow dtype, respectively, while previously it was a numpy integer dtype.
 
 .. _whatsnew_210.enhancements.enhancement2: