PERF: slow tests

pandas/core/arrays/datetimelike.py

@@ -35,6 +35,7 @@
     Timestamp,
     delta_to_nanoseconds,
     iNaT,
+    ints_to_pydatetime,
     to_offset,
 )
 from pandas._libs.tslibs.fields import (
@@ -332,8 +333,9 @@ def __getitem__(
         only handle list-likes, slices, and integer scalars
         """
         # Use cast as we know we will get back a DatetimeLikeArray or DTScalar
+        # GH#44624 skip evaluating the Union at runtime for performance.
         result = cast(
-            Union[DatetimeLikeArrayT, DTScalarOrNaT], super().__getitem__(key)
+            "Union[DatetimeLikeArrayT, DTScalarOrNaT]", super().__getitem__(key)
         )
         if lib.is_scalar(result):
             return result
@@ -403,6 +405,15 @@ def astype(self, dtype, copy: bool = True):
         dtype = pandas_dtype(dtype)
 
         if is_object_dtype(dtype):
+            if self.dtype.kind == "M":
+                # *much* faster than self._box_values
+                #  for e.g. test_get_loc_tuple_monotonic_above_size_cutoff
+                i8data = self.asi8.ravel()
+                converted = ints_to_pydatetime(
+                    i8data, tz=self.tz, freq=self.freq, box="timestamp"
+                )
+                return converted.reshape(self.shape)
+
             return self._box_values(self.asi8.ravel()).reshape(self.shape)
 
         elif isinstance(dtype, ExtensionDtype):

pandas/core/dtypes/missing.py

@@ -33,7 +33,6 @@
     is_integer_dtype,
     is_object_dtype,
     is_scalar,
-    is_string_dtype,
     needs_i8_conversion,
 )
 from pandas.core.dtypes.dtypes import (
@@ -242,7 +241,7 @@ def _isna_array(values: ArrayLike, inf_as_na: bool = False):
             result = libmissing.isnaobj(values.to_numpy(), inf_as_na=inf_as_na)
         else:
             result = values.isna()
-    elif is_string_dtype(dtype):
+    elif _is_string_dtype(dtype):
         result = _isna_string_dtype(values, inf_as_na=inf_as_na)
     elif needs_i8_conversion(dtype):
         # this is the NaT pattern
@@ -376,7 +375,10 @@ def isna_compat(arr, fill_value=np.nan) -> bool:
 
 
 def array_equivalent(
-    left, right, strict_nan: bool = False, dtype_equal: bool = False
+    left: np.ndarray,
+    right: np.ndarray,
+    strict_nan: bool = False,
+    dtype_equal: bool = False,
 ) -> bool:
     """
     True if two arrays, left and right, have equal non-NaN elements, and NaNs
@@ -421,13 +423,13 @@ def array_equivalent(
 
     if dtype_equal:
         # fastpath when we require that the dtypes match (Block.equals)
-        if is_float_dtype(left.dtype) or is_complex_dtype(left.dtype):
+        if left.dtype.kind in ["f", "c"]:
             return _array_equivalent_float(left, right)
         elif is_datetimelike_v_numeric(left.dtype, right.dtype):
             return False
         elif needs_i8_conversion(left.dtype):
             return _array_equivalent_datetimelike(left, right)
-        elif is_string_dtype(left.dtype):
+        elif _is_string_dtype(left.dtype):
             # TODO: fastpath for pandas' StringDtype
             return _array_equivalent_object(left, right, strict_nan)
         else:
@@ -644,3 +646,8 @@ def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
 
     # fallback, default to allowing NaN, None, NA, NaT
     return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+
+def _is_string_dtype(dtype: np.dtype) -> bool:
+    # Faster implementation of dtypes.common.is_string_dtype
+    return dtype == object or dtype.kind in "SU"