PERF: avoid com.is_datetime_or_timedelta_dtype

pandas/core/arrays/datetimelike.py

@@ -84,7 +84,6 @@
 from pandas.core.dtypes.common import (
     is_all_strings,
     is_datetime64_any_dtype,
-    is_datetime_or_timedelta_dtype,
     is_dtype_equal,
     is_float_dtype,
     is_integer_dtype,
@@ -474,10 +473,7 @@ def astype(self, dtype, copy: bool = True):
             if copy:
                 values = values.copy()
             return values
-        elif (
-            is_datetime_or_timedelta_dtype(dtype)
-            and not is_dtype_equal(self.dtype, dtype)
-        ) or is_float_dtype(dtype):
+        elif (dtype.kind in "mM" and self.dtype != dtype) or is_float_dtype(dtype):
             # disallow conversion between datetime/timedelta,
             # and conversions for any datetimelike to float
             msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"

pandas/core/dtypes/common.py

@@ -958,44 +958,6 @@ def is_timedelta64_ns_dtype(arr_or_dtype) -> bool:
     return _is_dtype(arr_or_dtype, lambda dtype: dtype == TD64NS_DTYPE)
 
 
-def is_datetime_or_timedelta_dtype(arr_or_dtype) -> bool:
-    """
-    Check whether the provided array or dtype is of
-    a timedelta64 or datetime64 dtype.
-
-    Parameters
-    ----------
-    arr_or_dtype : array-like or dtype
-        The array or dtype to check.
-
-    Returns
-    -------
-    boolean
-        Whether or not the array or dtype is of a timedelta64,
-        or datetime64 dtype.
-
-    Examples
-    --------
-    >>> is_datetime_or_timedelta_dtype(str)
-    False
-    >>> is_datetime_or_timedelta_dtype(int)
-    False
-    >>> is_datetime_or_timedelta_dtype(np.datetime64)
-    True
-    >>> is_datetime_or_timedelta_dtype(np.timedelta64)
-    True
-    >>> is_datetime_or_timedelta_dtype(np.array(['a', 'b']))
-    False
-    >>> is_datetime_or_timedelta_dtype(pd.Series([1, 2]))
-    False
-    >>> is_datetime_or_timedelta_dtype(np.array([], dtype=np.timedelta64))
-    True
-    >>> is_datetime_or_timedelta_dtype(np.array([], dtype=np.datetime64))
-    True
-    """
-    return _is_dtype_type(arr_or_dtype, classes(np.datetime64, np.timedelta64))
-
-
 # This exists to silence numpy deprecation warnings, see GH#29553
 def is_numeric_v_string_like(a: ArrayLike, b) -> bool:
     """
@@ -1693,7 +1655,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_datetime64_dtype",
     "is_datetime64_ns_dtype",
     "is_datetime64tz_dtype",
-    "is_datetime_or_timedelta_dtype",
     "is_decimal",
     "is_dict_like",
     "is_dtype_equal",

pandas/core/indexes/interval.py

@@ -43,7 +43,6 @@
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
-    is_datetime_or_timedelta_dtype,
     is_dtype_equal,
     is_float,
     is_float_dtype,
@@ -53,6 +52,7 @@
     is_number,
     is_object_dtype,
     is_scalar,
+    pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import (
     DatetimeTZDtype,
@@ -115,8 +115,10 @@
 def _get_next_label(label):
     dtype = getattr(label, "dtype", type(label))
     if isinstance(label, (Timestamp, Timedelta)):
-        dtype = "datetime64"
-    if is_datetime_or_timedelta_dtype(dtype) or isinstance(dtype, DatetimeTZDtype):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
         return label + np.timedelta64(1, "ns")
     elif is_integer_dtype(dtype):
         return label + 1
@@ -129,8 +131,10 @@ def _get_next_label(label):
 def _get_prev_label(label):
     dtype = getattr(label, "dtype", type(label))
     if isinstance(label, (Timestamp, Timedelta)):
-        dtype = "datetime64"
-    if is_datetime_or_timedelta_dtype(dtype) or isinstance(dtype, DatetimeTZDtype):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
         return label - np.timedelta64(1, "ns")
     elif is_integer_dtype(dtype):
         return label - 1

pandas/core/internals/construction.py

@@ -28,7 +28,6 @@
 from pandas.core.dtypes.common import (
     is_1d_only_ea_dtype,
     is_bool_dtype,
-    is_datetime_or_timedelta_dtype,
     is_dtype_equal,
     is_float_dtype,
     is_integer_dtype,
@@ -349,7 +348,7 @@ def ndarray_to_mgr(
                 for i in range(values.shape[1])
             ]
         else:
-            if is_datetime_or_timedelta_dtype(values.dtype):
+            if lib.is_np_dtype(values.dtype, "mM"):
                 values = ensure_wrapped_if_datetimelike(values)
             arrays = [values[:, i] for i in range(values.shape[1])]
 

pandas/core/reshape/tile.py

@@ -24,7 +24,6 @@
     ensure_platform_int,
     is_bool_dtype,
     is_datetime64_dtype,
-    is_datetime_or_timedelta_dtype,
     is_integer,
     is_list_like,
     is_numeric_dtype,
@@ -561,7 +560,7 @@ def _convert_bin_to_datelike_type(bins, dtype: DtypeObj | None):
     """
     if isinstance(dtype, DatetimeTZDtype):
         bins = to_datetime(bins.astype(np.int64), utc=True).tz_convert(dtype.tz)
-    elif is_datetime_or_timedelta_dtype(dtype):
+    elif lib.is_np_dtype(dtype, "mM"):
         bins = Index(bins.astype(np.int64), dtype=dtype)
     return bins
 

pandas/core/tools/numeric.py

@@ -14,7 +14,6 @@
 from pandas.core.dtypes.common import (
     ensure_object,
     is_bool_dtype,
-    is_datetime_or_timedelta_dtype,
     is_decimal,
     is_integer_dtype,
     is_number,
@@ -213,7 +212,7 @@ def to_numeric(
     new_mask: np.ndarray | None = None
     if is_numeric_dtype(values_dtype):
         pass
-    elif is_datetime_or_timedelta_dtype(values_dtype):
+    elif lib.is_np_dtype(values_dtype, "mM"):
         values = values.view(np.int64)
     else:
         values = ensure_object(values)

pandas/tests/dtypes/cast/test_promote.py

@@ -14,7 +14,6 @@
 from pandas.core.dtypes.common import (
     is_complex_dtype,
     is_datetime64_dtype,
-    is_datetime_or_timedelta_dtype,
     is_float_dtype,
     is_integer_dtype,
     is_object_dtype,
@@ -516,7 +515,7 @@ def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype, nulls_fixture):
         # but *does* cast None to np.nan
         expected_dtype = np.dtype(object)
         exp_val_for_scalar = fill_value
-    elif is_datetime_or_timedelta_dtype(dtype):
+    elif dtype.kind in "mM":
         # datetime / timedelta cast all missing values to dtyped-NaT
         expected_dtype = dtype
         exp_val_for_scalar = dtype.type("NaT", "ns")

pandas/tests/dtypes/test_common.py

@@ -512,20 +512,6 @@ def test_is_timedelta64_ns_dtype():
     assert com.is_timedelta64_ns_dtype(np.array([1, 2], dtype="m8[ns]"))
 
 
-def test_is_datetime_or_timedelta_dtype():
-    assert not com.is_datetime_or_timedelta_dtype(int)
-    assert not com.is_datetime_or_timedelta_dtype(str)
-    assert not com.is_datetime_or_timedelta_dtype(pd.Series([1, 2]))
-    assert not com.is_datetime_or_timedelta_dtype(np.array(["a", "b"]))
-
-    assert not com.is_datetime_or_timedelta_dtype(DatetimeTZDtype("ns", "US/Eastern"))
-
-    assert com.is_datetime_or_timedelta_dtype(np.datetime64)
-    assert com.is_datetime_or_timedelta_dtype(np.timedelta64)
-    assert com.is_datetime_or_timedelta_dtype(np.array([], dtype=np.timedelta64))
-    assert com.is_datetime_or_timedelta_dtype(np.array([], dtype=np.datetime64))
-
-
 def test_is_numeric_v_string_like():
     assert not com.is_numeric_v_string_like(np.array([1]), 1)
     assert not com.is_numeric_v_string_like(np.array([1]), np.array([2]))