ENH: Add prod to masked_reductions

pandas/core/array_algos/masked_reductions.py

@@ -3,6 +3,8 @@
 for missing values.
 """
 
+from typing import Callable
+
 import numpy as np
 
 from pandas._libs import missing as libmissing
@@ -11,14 +13,19 @@
 from pandas.core.nanops import check_below_min_count
 
 
-def sum(
-    values: np.ndarray, mask: np.ndarray, skipna: bool = True, min_count: int = 0,
+def _sumprod(
+    func: Callable,
+    values: np.ndarray,
+    mask: np.ndarray,
+    skipna: bool = True,
+    min_count: int = 0,
 ):
     """
-    Sum for 1D masked array.
+    Sum or product for 1D masked array.
 
     Parameters
     ----------
+    func : np.sum or np.prod
     values : np.ndarray
         Numpy array with the values (can be of any dtype that support the
         operation).
@@ -31,23 +38,21 @@ def sum(
         ``min_count`` non-NA values are present the result will be NA.
     """
     if not skipna:
-        if mask.any():
+        if mask.any() or check_below_min_count(values.shape, None, min_count):
             return libmissing.NA
         else:
-            if check_below_min_count(values.shape, None, min_count):
-                return libmissing.NA
-            return np.sum(values)
+            return func(values)
     else:
         if check_below_min_count(values.shape, mask, min_count):
             return libmissing.NA
 
         if _np_version_under1p17:
-            return np.sum(values[~mask])
+            return func(values[~mask])
         else:
-            return np.sum(values, where=~mask)
+            return func(values, where=~mask)
 
 
-def _minmax(func, values: np.ndarray, mask: np.ndarray, skipna: bool = True):
+def _minmax(func: Callable, values: np.ndarray, mask: np.ndarray, skipna: bool = True):
     """
     Reduction for 1D masked array.
 
@@ -63,17 +68,13 @@ def _minmax(func, values: np.ndarray, mask: np.ndarray, skipna: bool = True):
         Whether to skip NA.
     """
     if not skipna:
-        if mask.any():
+        if mask.any() or not values.size:
+            # min/max with empty array raise in numpy, pandas returns NA
             return libmissing.NA
         else:
-            if values.size:
-                return func(values)
-            else:
-                # min/max with empty array raise in numpy, pandas returns NA
-                return libmissing.NA
+            return func(values)
     else:
-        subset = values[~mask]
-        if subset.size:
+        if not mask.all():
             return func(values[~mask])
         else:
             # min/max with empty array raise in numpy, pandas returns NA
@@ -86,3 +87,15 @@ def min(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
 
 def max(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
     return _minmax(np.max, values=values, mask=mask, skipna=skipna)
+
+
+def sum(values: np.ndarray, mask: np.ndarray, skipna: bool = True, min_count: int = 0):
+    return _sumprod(
+        np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count
+    )
+
+
+def prod(values: np.ndarray, mask: np.ndarray, skipna: bool = True, min_count: int = 0):
+    return _sumprod(
+        np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count
+    )

pandas/core/arrays/boolean.py

@@ -696,7 +696,7 @@ def _reduce(self, name: str, skipna: bool = True, **kwargs):
         data = self._data
         mask = self._mask
 
-        if name in {"sum", "min", "max"}:
+        if name in {"sum", "prod", "min", "max"}:
             op = getattr(masked_reductions, name)
             return op(data, mask, skipna=skipna, **kwargs)
 
@@ -710,12 +710,6 @@ def _reduce(self, name: str, skipna: bool = True, **kwargs):
         if np.isnan(result):
             return libmissing.NA
 
-        # if we have numeric op that would result in an int, coerce to int if possible
-        if name == "prod" and notna(result):
-            int_result = np.int64(result)
-            if int_result == result:
-                result = int_result
-
         return result
 
     def _maybe_mask_result(self, result, mask, other, op_name: str):

pandas/core/arrays/integer.py

@@ -562,7 +562,7 @@ def _reduce(self, name: str, skipna: bool = True, **kwargs):
         data = self._data
         mask = self._mask
 
-        if name in {"sum", "min", "max"}:
+        if name in {"sum", "prod", "min", "max"}:
             op = getattr(masked_reductions, name)
             return op(data, mask, skipna=skipna, **kwargs)
 
@@ -581,12 +581,6 @@ def _reduce(self, name: str, skipna: bool = True, **kwargs):
         if name in ["any", "all"]:
             pass
 
-        # if we have a preservable numeric op,
-        # provide coercion back to an integer type if possible
-        elif name == "prod":
-            # GH#31409 more performant than casting-then-checking
-            result = com.cast_scalar_indexer(result)
-
         return result
 
     def _maybe_mask_result(self, result, mask, other, op_name: str):

pandas/tests/arrays/integer/test_dtypes.py

@@ -34,7 +34,7 @@ def test_preserve_dtypes(op):
 
     # op
     result = getattr(df.C, op)()
-    if op in {"sum", "min", "max"}:
+    if op in {"sum", "prod", "min", "max"}:
         assert isinstance(result, np.int64)
     else:
         assert isinstance(result, int)