REF: define reductions non-dynamically

pandas/core/frame.py

@@ -140,7 +140,10 @@
     sanitize_array,
     sanitize_masked_array,
 )
-from pandas.core.generic import NDFrame
+from pandas.core.generic import (
+    NDFrame,
+    make_doc,
+)
 from pandas.core.indexers import check_key_length
 from pandas.core.indexes.api import (
     DatetimeIndex,
@@ -9592,43 +9595,6 @@ def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
 
     agg = aggregate
 
-    # error: Signature of "any" incompatible with supertype "NDFrame"  [override]
-    @overload  # type: ignore[override]
-    def any(
-        self,
-        *,
-        axis: Axis = ...,
-        bool_only: bool | None = ...,
-        skipna: bool = ...,
-        level: None = ...,
-        **kwargs,
-    ) -> Series:
-        ...
-
-    @overload
-    def any(
-        self,
-        *,
-        axis: Axis = ...,
-        bool_only: bool | None = ...,
-        skipna: bool = ...,
-        level: Level,
-        **kwargs,
-    ) -> DataFrame | Series:
-        ...
-
-    # error: Missing return statement
-    @doc(NDFrame.any, **_shared_doc_kwargs)
-    def any(  # type: ignore[empty-body]
-        self,
-        axis: Axis = 0,
-        bool_only: bool | None = None,
-        skipna: bool = True,
-        level: Level = None,
-        **kwargs,
-    ) -> DataFrame | Series:
-        ...
-
     @doc(
         _shared_docs["transform"],
         klass=_shared_doc_kwargs["klass"],
@@ -10920,6 +10886,170 @@ def _reduce_axis1(self, name: str, func, skipna: bool) -> Series:
         res_ser = self._constructor_sliced(result, index=self.index, copy=False)
         return res_ser
 
+    @doc(make_doc("any", ndim=2))
+    # error: Signature of "any" incompatible with supertype "NDFrame"
+    def any(  # type: ignore[override]
+        self,
+        *,
+        axis: Axis = 0,
+        bool_only=None,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series:
+        # error: Incompatible return value type (got "Union[Series, bool]",
+        # expected "Series")
+        return self._logical_func(  # type: ignore[return-value]
+            "any", nanops.nanany, axis, bool_only, skipna, **kwargs
+        )
+
+    @doc(make_doc("all", ndim=2))
+    def all(
+        self,
+        axis: Axis = 0,
+        bool_only=None,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series:
+        # error: Incompatible return value type (got "Union[Series, bool]",
+        # expected "Series")
+        return self._logical_func(  # type: ignore[return-value]
+            "all", nanops.nanall, axis, bool_only, skipna, **kwargs
+        )
+
+    @doc(make_doc("min", ndim=2))
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().min(axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("max", ndim=2))
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().max(axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sum", ndim=2))
+    def sum(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return super().sum(axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("prod", ndim=2))
+    def prod(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return super().prod(axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("mean", ndim=2))
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().mean(axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("median", ndim=2))
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().median(axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sem", ndim=2))
+    def sem(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().sem(axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("var", ndim=2))
+    def var(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().var(axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("std", ndim=2))
+    def std(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().std(axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("skew", ndim=2))
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().skew(axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("kurt", ndim=2))
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return super().kurt(axis, skipna, numeric_only, **kwargs)
+
+    kurtosis = kurt
+    product = prod
+
+    @doc(make_doc("cummin", ndim=2))
+    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cummax", ndim=2))
+    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumsum", ndim=2))
+    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumprod", 2))
+    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
+
     def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
         """
         Count number of distinct elements in specified axis.
@@ -11724,9 +11854,6 @@ def values(self) -> np.ndarray:
         return self._mgr.as_array()
 
 
-DataFrame._add_numeric_operations()
-
-
 def _from_nested_dict(data) -> collections.defaultdict:
     new_data: collections.defaultdict = collections.defaultdict(dict)
     for index, s in data.items():