REGR: Performance of DataFrame axis=1 reduction ops with EA

doc/source/whatsnew/v2.0.0.rst

@@ -1197,7 +1197,6 @@ Numeric
 - Bug in :meth:`Series.__add__` casting to object for list and masked :class:`Series` (:issue:`22962`)
 - Bug in :meth:`~arrays.ArrowExtensionArray.mode` where ``dropna=False`` was not respected when there was ``NA`` values (:issue:`50982`)
 - Bug in :meth:`DataFrame.query` with ``engine="numexpr"`` and column names are ``min`` or ``max`` would raise a ``TypeError`` (:issue:`50937`)
-- Bug in :meth:`DataFrame.min` and :meth:`DataFrame.max` with tz-aware data containing ``pd.NaT`` and ``axis=1`` would return incorrect results (:issue:`51242`)
 
 Conversion
 ^^^^^^^^^^

pandas/core/frame.py

@@ -102,6 +102,7 @@
     is_integer_dtype,
     is_iterator,
     is_list_like,
+    is_object_dtype,
     is_scalar,
     is_sequence,
     needs_i8_conversion,
@@ -10917,44 +10918,43 @@ def _get_data() -> DataFrame:
                 data = self._get_bool_data()
             return data
 
-        # Case with EAs see GH#35881
         df = self
         if numeric_only:
             df = _get_data()
         if axis is None:
             return func(df.values)
-        elif axis == 1:
-            if len(df.index) == 0:
-                # Taking a transpose would result in no columns, losing the dtype.
-                # In the empty case, reducing along axis 0 or 1 gives the same
-                # result dtype, so reduce with axis=0 and ignore values
-                result = df._reduce(
-                    op,
-                    name,
-                    axis=0,
-                    skipna=skipna,
-                    numeric_only=False,
-                    filter_type=filter_type,
-                    **kwds,
-                ).iloc[:0]
-                result.index = df.index
-                return result
-            df = df.T
-
-        # After possibly _get_data and transposing, we are now in the
-        #  simple case where we can use BlockManager.reduce
-        res = df._mgr.reduce(blk_func)
-        out = df._constructor(res).iloc[0]
-        if out_dtype is not None:
-            out = out.astype(out_dtype)
-        elif (df._mgr.get_dtypes() == object).any():
-            out = out.astype(object)
-        elif len(self) == 0 and name in ("sum", "prod"):
-            # Even if we are object dtype, follow numpy and return
-            #  float64, see test_apply_funcs_over_empty
-            out = out.astype(np.float64)
+        elif axis == 0:
+            res = df._mgr.reduce(blk_func)
+            out = df._constructor(res).iloc[0]
+            if out_dtype is not None:
+                out = out.astype(out_dtype)
+            elif axis == 0 and len(self) == 0 and name in ["sum", "prod"]:
+                # Even if we are object dtype, follow numpy and return
+                #  float64, see test_apply_funcs_over_empty
+                out = out.astype(np.float64)
+            elif (df._mgr.get_dtypes() == object).any():
+                out = out.astype(object)
+
+            return out
+
+        values = df.values
+        result = func(values)
+
+        if hasattr(result, "dtype"):
+            if filter_type == "bool" and notna(result).all():
+                result = result.astype(np.bool_)
+            elif (df._mgr.get_dtypes() == object).any():
+                result = result.astype(object)
+            elif filter_type is None and is_object_dtype(result.dtype):
+                try:
+                    result = result.astype(np.float64)
+                except (ValueError, TypeError):
+                    # try to coerce to the original dtypes item by item if we can
+                    pass
 
-        return out
+        labels = self._get_agg_axis(axis)
+        result = self._constructor_sliced(result, index=labels)
+        return result
 
     def _reduce_axis1(self, name: str, func, skipna: bool) -> Series:
         """

pandas/tests/frame/test_reductions.py

@@ -1464,6 +1464,7 @@ def test_preserve_timezone(self, initial: str, method):
         result = getattr(df, method)(axis=1)
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.xfail(reason="GH#51955 - avoid perf regression in axis=1 ops")
     @pytest.mark.parametrize("method", ["min", "max"])
     def test_minmax_tzaware_skipna_axis_1(self, method, skipna):
         # GH#51242