REF: enforce annotation in maybe_downcast_to_dtype

pandas/core/dtypes/cast.py

@@ -4,7 +4,6 @@
 
 from __future__ import annotations
 
-from contextlib import suppress
 from datetime import (
     date,
     datetime,
@@ -29,7 +28,6 @@
     NaT,
     OutOfBoundsDatetime,
     OutOfBoundsTimedelta,
-    Period,
     Timedelta,
     Timestamp,
     conversion,
@@ -87,7 +85,6 @@
     PeriodDtype,
 )
 from pandas.core.dtypes.generic import (
-    ABCDataFrame,
     ABCExtensionArray,
     ABCSeries,
 )
@@ -249,9 +246,6 @@ def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLi
     try to cast to the specified dtype (e.g. convert back to bool/int
     or could be an astype of float64->float32
     """
-    if isinstance(result, ABCDataFrame):
-        # see test_pivot_table_doctest_case
-        return result
     do_round = False
 
     if isinstance(dtype, str):
@@ -278,15 +272,9 @@ def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLi
 
         dtype = np.dtype(dtype)
 
-    elif dtype.type is Period:
-        from pandas.core.arrays import PeriodArray
-
-        with suppress(TypeError):
-            # e.g. TypeError: int() argument must be a string, a
-            #  bytes-like object or a number, not 'Period
-
-            # error: "dtype[Any]" has no attribute "freq"
-            return PeriodArray(result, freq=dtype.freq)  # type: ignore[attr-defined]
+    if not isinstance(dtype, np.dtype):
+        # enforce our signature annotation
+        raise TypeError(dtype)  # pragma: no cover
 
     converted = maybe_downcast_numeric(result, dtype, do_round)
     if converted is not result:
@@ -295,15 +283,7 @@ def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLi
     # a datetimelike
     # GH12821, iNaT is cast to float
     if dtype.kind in ["M", "m"] and result.dtype.kind in ["i", "f"]:
-        if isinstance(dtype, DatetimeTZDtype):
-            # convert to datetime and change timezone
-            i8values = result.astype("i8", copy=False)
-            cls = dtype.construct_array_type()
-            # equiv: DatetimeArray(i8values).tz_localize("UTC").tz_convert(dtype.tz)
-            dt64values = i8values.view("M8[ns]")
-            result = cls._simple_new(dt64values, dtype=dtype)
-        else:
-            result = result.astype(dtype)
+        result = result.astype(dtype)
 
     return result
 

pandas/core/frame.py

@@ -7213,13 +7213,14 @@ def combine(
             else:
                 # if we have different dtypes, possibly promote
                 new_dtype = find_common_type([this_dtype, other_dtype])
-                if not is_dtype_equal(this_dtype, new_dtype):
-                    series = series.astype(new_dtype)
-                if not is_dtype_equal(other_dtype, new_dtype):
-                    otherSeries = otherSeries.astype(new_dtype)
+                series = series.astype(new_dtype, copy=False)
+                otherSeries = otherSeries.astype(new_dtype, copy=False)
 
             arr = func(series, otherSeries)
-            arr = maybe_downcast_to_dtype(arr, new_dtype)
+            if isinstance(new_dtype, np.dtype):
+                # if new_dtype is an EA Dtype, then `func` is expected to return
+                # the correct dtype without any additional casting
+                arr = maybe_downcast_to_dtype(arr, new_dtype)
 
             result[col] = arr
 

pandas/core/reshape/pivot.py

@@ -174,7 +174,15 @@ def __internal_pivot_table(
                 and v in agged
                 and not is_integer_dtype(agged[v])
             ):
-                agged[v] = maybe_downcast_to_dtype(agged[v], data[v].dtype)
+                if isinstance(agged[v], ABCDataFrame):
+                    # exclude DataFrame case bc maybe_downcast_to_dtype expects
+                    #  ArrayLike
+                    # TODO: why does test_pivot_table_doctest_case fail if
+                    # we don't do this apparently-unnecessary setitem?
+                    agged[v] = agged[v]
+                    pass
+                else:
+                    agged[v] = maybe_downcast_to_dtype(agged[v], data[v].dtype)
 
     table = agged
 

pandas/tests/dtypes/cast/test_downcast.py

@@ -5,11 +5,7 @@
 
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
 
-from pandas import (
-    DatetimeIndex,
-    Series,
-    Timestamp,
-)
+from pandas import Series
 import pandas._testing as tm
 
 
@@ -77,7 +73,7 @@ def test_downcast_conversion_nan(float_dtype):
 def test_downcast_conversion_empty(any_real_dtype):
     dtype = any_real_dtype
     arr = np.array([], dtype=dtype)
-    result = maybe_downcast_to_dtype(arr, "int64")
+    result = maybe_downcast_to_dtype(arr, np.dtype("int64"))
     tm.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
 
 
@@ -89,15 +85,3 @@ def test_datetime_likes_nan(klass):
     exp = np.array([1, 2, klass("NaT")], dtype)
     res = maybe_downcast_to_dtype(arr, dtype)
     tm.assert_numpy_array_equal(res, exp)
-
-
-@pytest.mark.parametrize("as_asi", [True, False])
-def test_datetime_with_timezone(as_asi):
-    # see gh-15426
-    ts = Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
-    exp = DatetimeIndex([ts, ts])._data
-
-    obj = exp.asi8 if as_asi else exp
-    res = maybe_downcast_to_dtype(obj, exp.dtype)
-
-    tm.assert_datetime_array_equal(res, exp)