BUG: masked mean unnecessarily overflowing (#48378)

doc/source/whatsnew/v1.6.0.rst

@@ -200,7 +200,7 @@ Sparse
 
 ExtensionArray
 ^^^^^^^^^^^^^^
--
+- Bug in :meth:`Series.mean` overflowing unnecessarily with nullable integers (:issue:`48378`)
 -
 
 Styler

pandas/core/array_algos/masked_reductions.py

@@ -14,7 +14,7 @@
 from pandas.core.nanops import check_below_min_count
 
 
-def _sumprod(
+def _reductions(
     func: Callable,
     values: np.ndarray,
     mask: npt.NDArray[np.bool_],
@@ -24,7 +24,7 @@ def _sumprod(
     axis: int | None = None,
 ):
     """
-    Sum or product for 1D masked array.
+    Sum, mean or product for 1D masked array.
 
     Parameters
     ----------
@@ -63,7 +63,7 @@ def sum(
     min_count: int = 0,
     axis: int | None = None,
 ):
-    return _sumprod(
+    return _reductions(
         np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
     )
 
@@ -76,7 +76,7 @@ def prod(
     min_count: int = 0,
     axis: int | None = None,
 ):
-    return _sumprod(
+    return _reductions(
         np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
     )
 
@@ -139,11 +139,13 @@ def max(
     return _minmax(np.max, values=values, mask=mask, skipna=skipna, axis=axis)
 
 
-# TODO: axis kwarg
-def mean(values: np.ndarray, mask: npt.NDArray[np.bool_], skipna: bool = True):
+def mean(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: int | None = None,
+):
     if not values.size or mask.all():
         return libmissing.NA
-    _sum = _sumprod(np.sum, values=values, mask=mask, skipna=skipna)
-    count = np.count_nonzero(~mask)
-    mean_value = _sum / count
-    return mean_value
+    return _reductions(np.mean, values=values, mask=mask, skipna=skipna, axis=axis)

pandas/core/arrays/masked.py

@@ -1036,17 +1036,12 @@ def _quantile(
     # Reductions
 
     def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
-        if name in {"any", "all", "min", "max", "sum", "prod"}:
+        if name in {"any", "all", "min", "max", "sum", "prod", "mean"}:
             return getattr(self, name)(skipna=skipna, **kwargs)
 
         data = self._data
         mask = self._mask
 
-        if name in {"mean"}:
-            op = getattr(masked_reductions, name)
-            result = op(data, mask, skipna=skipna, **kwargs)
-            return result
-
         # coerce to a nan-aware float if needed
         # (we explicitly use NaN within reductions)
         if self._hasna:
@@ -1107,6 +1102,18 @@ def prod(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
             "prod", result, skipna=skipna, axis=axis, **kwargs
         )
 
+    def mean(self, *, skipna=True, axis: int | None = 0, **kwargs):
+        nv.validate_mean((), kwargs)
+        result = masked_reductions.mean(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result(
+            "mean", result, skipna=skipna, axis=axis, **kwargs
+        )
+
     def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
         nv.validate_min((), kwargs)
         return masked_reductions.min(

pandas/tests/extension/base/dim2.py

@@ -6,7 +6,13 @@
 
 from pandas._libs.missing import is_matching_na
 
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer_dtype,
+)
+
 import pandas as pd
+import pandas._testing as tm
 from pandas.core.arrays.integer import INT_STR_TO_DTYPE
 from pandas.tests.extension.base.base import BaseExtensionTests
 
@@ -191,7 +197,12 @@ def test_reductions_2d_axis0(self, data, method):
             kwargs["ddof"] = 0
 
         try:
-            result = getattr(arr2d, method)(axis=0, **kwargs)
+            if method == "mean" and hasattr(data, "_mask"):
+                # Empty slices produced by the mask cause RuntimeWarnings by numpy
+                with tm.assert_produces_warning(RuntimeWarning, check_stacklevel=False):
+                    result = getattr(arr2d, method)(axis=0, **kwargs)
+            else:
+                result = getattr(arr2d, method)(axis=0, **kwargs)
         except Exception as err:
             try:
                 getattr(data, method)()
@@ -212,7 +223,7 @@ def get_reduction_result_dtype(dtype):
                 # i.e. dtype.kind == "u"
                 return INT_STR_TO_DTYPE[np.dtype(np.uint).name]
 
-        if method in ["mean", "median", "sum", "prod"]:
+        if method in ["median", "sum", "prod"]:
             # std and var are not dtype-preserving
             expected = data
             if method in ["sum", "prod"] and data.dtype.kind in "iub":
@@ -229,6 +240,10 @@ def get_reduction_result_dtype(dtype):
             self.assert_extension_array_equal(result, expected)
         elif method == "std":
             self.assert_extension_array_equal(result, data - data)
+        elif method == "mean":
+            if is_integer_dtype(data) or is_bool_dtype(data):
+                data = data.astype("Float64")
+            self.assert_extension_array_equal(result, data)
         # punt on method == "var"
 
     @pytest.mark.parametrize("method", ["mean", "median", "var", "std", "sum", "prod"])

pandas/tests/reductions/test_reductions.py

@@ -775,6 +775,18 @@ def test_sum_overflow_float(self, use_bottleneck, dtype):
             result = s.max(skipna=False)
             assert np.allclose(float(result), v[-1])
 
+    def test_mean_masked_overflow(self):
+        # GH#48378
+        val = 100_000_000_000_000_000
+        n_elements = 100
+        na = np.array([val] * n_elements)
+        ser = Series([val] * n_elements, dtype="Int64")
+
+        result_numpy = np.mean(na)
+        result_masked = ser.mean()
+        assert result_masked - result_numpy == 0
+        assert result_masked == 1e17
+
     @pytest.mark.parametrize("dtype", ("m8[ns]", "m8[ns]", "M8[ns]", "M8[ns, UTC]"))
     @pytest.mark.parametrize("skipna", [True, False])
     def test_empty_timeseries_reductions_return_nat(self, dtype, skipna):