ENH: Enabled skipna argument on groupby reduction ops #15675

doc/source/whatsnew/v3.0.0.rst

@@ -43,6 +43,7 @@ Other enhancements
 - :meth:`Styler.format_index_names` can now be used to format the index and column names (:issue:`48936` and :issue:`47489`)
 - :class:`.errors.DtypeWarning` improved to include column names when mixed data types are detected (:issue:`58174`)
 - :func:`DataFrame.to_excel` argument ``merge_cells`` now accepts a value of ``"columns"`` to only merge :class:`MultiIndex` column header header cells (:issue:`35384`)
+- :meth:`.DataFrameGroupBy.sum`, :meth:`.DataFrameGroupBy.prod`, :meth:`.DataFrameGroupBy.min`, :meth:`.DataFrameGroupBy.max`, :meth:`.DataFrameGroupBy.mean`, :meth:`.DataFrameGroupBy.median`, :meth:`.DataFrameGroupBy.sem`, :meth:`.DataFrameGroupBy.std` and :meth:`.DataFrameGroupBy.var` now accept a skipna argument. (:issue:`15675`)
 - :meth:`DataFrame.corrwith` now accepts ``min_periods`` as optional arguments, as in :meth:`DataFrame.corr` and :meth:`Series.corr` (:issue:`9490`)
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.ewm` now allows ``adjust=False`` when ``times`` is provided (:issue:`54328`)

pandas/_libs/groupby.pyi

@@ -13,6 +13,7 @@ def group_median_float64(
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
     is_datetimelike: bool = ...,  # bint
+    skipna: bool = ...,  # bint
 ) -> None: ...
 def group_cumprod(
     out: np.ndarray,  # float64_t[:, ::1]
@@ -66,6 +67,7 @@ def group_sum(
     result_mask: np.ndarray | None = ...,
     min_count: int = ...,
     is_datetimelike: bool = ...,
+    skipna: bool = ...,  # bint
 ) -> None: ...
 def group_prod(
     out: np.ndarray,  # int64float_t[:, ::1]
@@ -75,6 +77,7 @@ def group_prod(
     mask: np.ndarray | None,
     result_mask: np.ndarray | None = ...,
     min_count: int = ...,
+    skipna: bool = ...,  # bint
 ) -> None: ...
 def group_var(
     out: np.ndarray,  # floating[:, ::1]
@@ -86,6 +89,7 @@ def group_var(
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
     is_datetimelike: bool = ...,
+    skipna: bool = ...,  # bint
     name: str = ...,
 ) -> None: ...
 def group_skew(
@@ -104,6 +108,7 @@ def group_mean(
     labels: np.ndarray,  # const intp_t[:]
     min_count: int = ...,  # Py_ssize_t
     is_datetimelike: bool = ...,  # bint
+    skipna: bool = ...,  # bint
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
 ) -> None: ...
@@ -170,6 +175,7 @@ def group_max(
     labels: np.ndarray,  # const int64_t[:]
     min_count: int = ...,
     is_datetimelike: bool = ...,
+    skipna: bool = ...,  # bint
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
 ) -> None: ...
@@ -180,6 +186,7 @@ def group_min(
     labels: np.ndarray,  # const int64_t[:]
     min_count: int = ...,
     is_datetimelike: bool = ...,
+    skipna: bool = ...,  # bint
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
 ) -> None: ...