REF: remove unnecessary case from maybe_downcast_to_dtype

pandas/core/dtypes/cast.py

@@ -246,18 +246,16 @@ def _disallow_mismatched_datetimelike(value, dtype: DtypeObj) -> None:
 
 
 @overload
-def maybe_downcast_to_dtype(
-    result: np.ndarray, dtype: str | np.dtype
-) -> np.ndarray: ...
+def maybe_downcast_to_dtype(result: np.ndarray, dtype: np.dtype) -> np.ndarray: ...
 
 
 @overload
 def maybe_downcast_to_dtype(
-    result: ExtensionArray, dtype: str | np.dtype
-) -> ArrayLike: ...
+    result: ExtensionArray, dtype: np.dtype
+) -> ExtensionArray: ...
 
 
-def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLike:
+def maybe_downcast_to_dtype(result: ArrayLike, dtype: np.dtype) -> ArrayLike:
     """
     try to cast to the specified dtype (e.g. convert back to bool/int
     or could be an astype of float64->float32
@@ -266,30 +264,6 @@ def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLi
         result = result._values
     do_round = False
 
-    if isinstance(dtype, str):
-        if dtype == "infer":
-            inferred_type = lib.infer_dtype(result, skipna=False)
-            if inferred_type == "boolean":
-                dtype = "bool"
-            elif inferred_type == "integer":
-                dtype = "int64"
-            elif inferred_type == "datetime64":
-                dtype = "datetime64[ns]"
-            elif inferred_type in ["timedelta", "timedelta64"]:
-                dtype = "timedelta64[ns]"
-
-            # try to upcast here
-            elif inferred_type == "floating":
-                dtype = "int64"
-                if issubclass(result.dtype.type, np.number):
-                    do_round = True
-
-            else:
-                # TODO: complex?  what if result is already non-object?
-                dtype = "object"
-
-        dtype = np.dtype(dtype)
-
     if not isinstance(dtype, np.dtype):
         # enforce our signature annotation
         raise TypeError(dtype)  # pragma: no cover

pandas/tests/dtypes/cast/test_downcast.py

@@ -7,43 +7,20 @@
 
 from pandas import (
     Series,
-    Timedelta,
 )
 import pandas._testing as tm
 
 
 @pytest.mark.parametrize(
     "arr,dtype,expected",
     [
-        (
-            np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
-            "infer",
-            np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),
-        ),
-        (
-            np.array([8.0, 8.0, 8.0, 8.0, 8.9999999999995]),
-            "infer",
-            np.array([8, 8, 8, 8, 9], dtype=np.int64),
-        ),
-        (
-            np.array([8.0, 8.0, 8.0, 8.0, 9.0000000000005]),
-            "infer",
-            np.array([8, 8, 8, 8, 9], dtype=np.int64),
-        ),
         (
             # This is a judgement call, but we do _not_ downcast Decimal
             #  objects
             np.array([decimal.Decimal("0.0")]),
-            "int64",
+            np.dtype("int64"),
             np.array([decimal.Decimal("0.0")]),
         ),
-        (
-            # GH#45837
-            np.array([Timedelta(days=1), Timedelta(days=2)], dtype=object),
-            "infer",
-            np.array([1, 2], dtype="m8[D]").astype("m8[ns]"),
-        ),
-        # TODO: similar for dt64, dt64tz, Period, Interval?
     ],
 )
 def test_downcast(arr, expected, dtype):
@@ -60,26 +37,6 @@ def test_downcast_booleans():
     tm.assert_numpy_array_equal(result, expected)
 
 
-def test_downcast_conversion_no_nan(any_real_numpy_dtype):
-    dtype = any_real_numpy_dtype
-    expected = np.array([1, 2])
-    arr = np.array([1.0, 2.0], dtype=dtype)
-
-    result = maybe_downcast_to_dtype(arr, "infer")
-    tm.assert_almost_equal(result, expected, check_dtype=False)
-
-
-def test_downcast_conversion_nan(float_numpy_dtype):
-    dtype = float_numpy_dtype
-    data = [1.0, 2.0, np.nan]
-
-    expected = np.array(data, dtype=dtype)
-    arr = np.array(data, dtype=dtype)
-
-    result = maybe_downcast_to_dtype(arr, "infer")
-    tm.assert_almost_equal(result, expected)
-
-
 def test_downcast_conversion_empty(any_real_numpy_dtype):
     dtype = any_real_numpy_dtype
     arr = np.array([], dtype=dtype)
@@ -89,7 +46,7 @@ def test_downcast_conversion_empty(any_real_numpy_dtype):
 
 @pytest.mark.parametrize("klass", [np.datetime64, np.timedelta64])
 def test_datetime_likes_nan(klass):
-    dtype = klass.__name__ + "[ns]"
+    dtype = np.dtype(klass.__name__ + "[ns]")
     arr = np.array([1, 2, np.nan])
 
     exp = np.array([1, 2, klass("NaT")], dtype)