PERF: dtype checks

pandas/core/dtypes/missing.py

@@ -26,15 +26,12 @@
     TD64NS_DTYPE,
     ensure_object,
     is_bool_dtype,
-    is_complex_dtype,
     is_dtype_equal,
     is_extension_array_dtype,
-    is_float_dtype,
     is_integer_dtype,
     is_object_dtype,
     is_scalar,
     is_string_or_object_np_dtype,
-    needs_i8_conversion,
 )
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype,
@@ -291,7 +288,7 @@ def _isna_array(values: ArrayLike, inf_as_na: bool = False):
             result = values.isna()  # type: ignore[assignment]
     elif is_string_or_object_np_dtype(values.dtype):
         result = _isna_string_dtype(values, inf_as_na=inf_as_na)
-    elif needs_i8_conversion(dtype):
+    elif dtype.kind in "mM":
         # this is the NaT pattern
         result = values.view("i8") == iNaT
     else:
@@ -502,7 +499,7 @@ def array_equivalent(
         # fastpath when we require that the dtypes match (Block.equals)
         if left.dtype.kind in "fc":
             return _array_equivalent_float(left, right)
-        elif needs_i8_conversion(left.dtype):
+        elif left.dtype.kind in "mM":
             return _array_equivalent_datetimelike(left, right)
         elif is_string_or_object_np_dtype(left.dtype):
             # TODO: fastpath for pandas' StringDtype
@@ -519,14 +516,14 @@ def array_equivalent(
         return _array_equivalent_object(left, right, strict_nan)
 
     # NaNs can occur in float and complex arrays.
-    if is_float_dtype(left.dtype) or is_complex_dtype(left.dtype):
+    if left.dtype.kind in "fc":
         if not (left.size and right.size):
             return True
         return ((left == right) | (isna(left) & isna(right))).all()
 
-    elif needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype):
+    elif left.dtype.kind in "mM" or right.dtype.kind in "mM":
         # datetime64, timedelta64, Period
-        if not is_dtype_equal(left.dtype, right.dtype):
+        if left.dtype != right.dtype:
             return False
 
         left = left.view("i8")
@@ -541,11 +538,11 @@ def array_equivalent(
     return np.array_equal(left, right)
 
 
-def _array_equivalent_float(left, right) -> bool:
+def _array_equivalent_float(left: np.ndarray, right: np.ndarray) -> bool:
     return bool(((left == right) | (np.isnan(left) & np.isnan(right))).all())
 
 
-def _array_equivalent_datetimelike(left, right):
+def _array_equivalent_datetimelike(left: np.ndarray, right: np.ndarray):
     return np.array_equal(left.view("i8"), right.view("i8"))
 
 
@@ -601,7 +598,7 @@ def infer_fill_value(val):
     if not is_list_like(val):
         val = [val]
     val = np.array(val, copy=False)
-    if needs_i8_conversion(val.dtype):
+    if val.dtype.kind in "mM":
         return np.array("NaT", dtype=val.dtype)
     elif is_object_dtype(val.dtype):
         dtype = lib.infer_dtype(ensure_object(val), skipna=False)
@@ -616,7 +613,7 @@ def maybe_fill(arr: np.ndarray) -> np.ndarray:
     """
     Fill numpy.ndarray with NaN, unless we have a integer or boolean dtype.
     """
-    if arr.dtype.kind not in ("u", "i", "b"):
+    if arr.dtype.kind not in "iub":
         arr.fill(np.nan)
     return arr
 
@@ -650,15 +647,15 @@ def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
 
     if isinstance(dtype, ExtensionDtype):
         return dtype.na_value
-    elif needs_i8_conversion(dtype):
+    elif dtype.kind in "mM":
         return dtype.type("NaT", "ns")
-    elif is_float_dtype(dtype):
+    elif dtype.kind == "f":
         return np.nan
-    elif is_integer_dtype(dtype):
+    elif dtype.kind in "iu":
         if compat:
             return 0
         return np.nan
-    elif is_bool_dtype(dtype):
+    elif dtype.kind == "b":
         if compat:
             return False
         return np.nan

pandas/core/frame.py

@@ -94,7 +94,6 @@
     is_dataclass,
     is_dict_like,
     is_dtype_equal,
-    is_extension_array_dtype,
     is_float,
     is_float_dtype,
     is_hashable,
@@ -3603,7 +3602,9 @@ def transpose(self, *args, copy: bool = False) -> DataFrame:
                 result._mgr.add_references(self._mgr)  # type: ignore[arg-type]
 
         elif (
-            self._is_homogeneous_type and dtypes and is_extension_array_dtype(dtypes[0])
+            self._is_homogeneous_type
+            and dtypes
+            and isinstance(dtypes[0], ExtensionDtype)
         ):
             # We have EAs with the same dtype. We can preserve that dtype in transpose.
             dtype = dtypes[0]
@@ -4184,7 +4185,7 @@ def _set_item(self, key, value) -> None:
         if (
             key in self.columns
             and value.ndim == 1
-            and not is_extension_array_dtype(value)
+            and not isinstance(value.dtype, ExtensionDtype)
         ):
             # broadcast across multiple columns if necessary
             if not self.columns.is_unique or isinstance(self.columns, MultiIndex):

pandas/core/generic.py

@@ -123,7 +123,10 @@
     is_timedelta64_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCSeries,
@@ -4670,7 +4673,7 @@ def _drop_axis(
                 if errors == "raise" and labels_missing:
                     raise KeyError(f"{labels} not found in axis")
 
-            if is_extension_array_dtype(mask.dtype):
+            if isinstance(mask.dtype, ExtensionDtype):
                 # GH#45860
                 mask = mask.to_numpy(dtype=bool)
 
@@ -5457,7 +5460,7 @@ def _needs_reindex_multi(self, axes, method, level: Level | None) -> bool_t:
             and not (
                 self.ndim == 2
                 and len(self.dtypes) == 1
-                and is_extension_array_dtype(self.dtypes.iloc[0])
+                and isinstance(self.dtypes.iloc[0], ExtensionDtype)
             )
         )
 

pandas/core/groupby/ops.py

@@ -47,12 +47,6 @@
     ensure_platform_int,
     ensure_uint64,
     is_1d_only_ea_dtype,
-    is_bool_dtype,
-    is_complex_dtype,
-    is_float_dtype,
-    is_integer_dtype,
-    is_numeric_dtype,
-    needs_i8_conversion,
 )
 from pandas.core.dtypes.missing import (
     isna,
@@ -248,7 +242,7 @@ def _get_out_dtype(self, dtype: np.dtype) -> np.dtype:
         if how == "rank":
             out_dtype = "float64"
         else:
-            if is_numeric_dtype(dtype):
+            if dtype.kind in "iufcb":
                 out_dtype = f"{dtype.kind}{dtype.itemsize}"
             else:
                 out_dtype = "object"
@@ -274,9 +268,9 @@ def _get_result_dtype(self, dtype: np.dtype) -> np.dtype:
             if dtype == np.dtype(bool):
                 return np.dtype(np.int64)
         elif how in ["mean", "median", "var", "std", "sem"]:
-            if is_float_dtype(dtype) or is_complex_dtype(dtype):
+            if dtype.kind in "fc":
                 return dtype
-            elif is_numeric_dtype(dtype):
+            elif dtype.kind in "iub":
                 return np.dtype(np.float64)
         return dtype
 
@@ -339,14 +333,14 @@ def _call_cython_op(
         orig_values = values
 
         dtype = values.dtype
-        is_numeric = is_numeric_dtype(dtype)
+        is_numeric = dtype.kind in "iufcb"
 
-        is_datetimelike = needs_i8_conversion(dtype)
+        is_datetimelike = dtype.kind in "mM"
 
         if is_datetimelike:
             values = values.view("int64")
             is_numeric = True
-        elif is_bool_dtype(dtype):
+        elif dtype.kind == "b":
             values = values.view("uint8")
         if values.dtype == "float16":
             values = values.astype(np.float32)
@@ -446,7 +440,7 @@ def _call_cython_op(
             # i.e. counts is defined.  Locations where count<min_count
             # need to have the result set to np.nan, which may require casting,
             # see GH#40767
-            if is_integer_dtype(result.dtype) and not is_datetimelike:
+            if result.dtype.kind in "iu" and not is_datetimelike:
                 # if the op keeps the int dtypes, we have to use 0
                 cutoff = max(0 if self.how in ["sum", "prod"] else 1, min_count)
                 empty_groups = counts < cutoff

pandas/core/indexing.py

@@ -35,7 +35,6 @@
 from pandas.core.dtypes.common import (
     is_array_like,
     is_bool_dtype,
-    is_extension_array_dtype,
     is_hashable,
     is_integer,
     is_iterator,
@@ -46,6 +45,7 @@
     is_sequence,
 )
 from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCSeries,
@@ -1128,10 +1128,10 @@ def _validate_key(self, key, axis: Axis):
         # boolean not in slice and with boolean index
         ax = self.obj._get_axis(axis)
         if isinstance(key, bool) and not (
-            is_bool_dtype(ax)
+            is_bool_dtype(ax.dtype)
             or ax.dtype.name == "boolean"
             or isinstance(ax, MultiIndex)
-            and is_bool_dtype(ax.get_level_values(0))
+            and is_bool_dtype(ax.get_level_values(0).dtype)
         ):
             raise KeyError(
                 f"{key}: boolean label can not be used without a boolean index"
@@ -2490,7 +2490,7 @@ def check_bool_indexer(index: Index, key) -> np.ndarray:
         result = result.take(indexer)
 
         # fall through for boolean
-        if not is_extension_array_dtype(result.dtype):
+        if not isinstance(result.dtype, ExtensionDtype):
             return result.astype(bool)._values
 
     if is_object_dtype(key):

pandas/core/missing.py

@@ -455,7 +455,7 @@ def _interpolate_1d(
     # sort preserve_nans and convert to list
     preserve_nans = sorted(preserve_nans)
 
-    is_datetimelike = needs_i8_conversion(yvalues.dtype)
+    is_datetimelike = yvalues.dtype.kind in "mM"
 
     if is_datetimelike:
         yvalues = yvalues.view("i8")

pandas/core/nanops.py

@@ -34,7 +34,6 @@
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
-    is_any_int_dtype,
     is_complex,
     is_float,
     is_float_dtype,
@@ -247,7 +246,7 @@ def _maybe_get_mask(
             # Boolean data cannot contain nulls, so signal via mask being None
             return None
 
-        if skipna or needs_i8_conversion(values.dtype):
+        if skipna or values.dtype.kind in "mM":
             mask = isna(values)
 
     return mask
@@ -300,7 +299,7 @@ def _get_values(
     dtype = values.dtype
 
     datetimelike = False
-    if needs_i8_conversion(values.dtype):
+    if values.dtype.kind in "mM":
         # changing timedelta64/datetime64 to int64 needs to happen after
         #  finding `mask` above
         values = np.asarray(values.view("i8"))
@@ -433,7 +432,7 @@ def _na_for_min_count(values: np.ndarray, axis: AxisInt | None) -> Scalar | np.n
         For 2-D values, returns a 1-D array where each element is missing.
     """
     # we either return np.nan or pd.NaT
-    if is_numeric_dtype(values.dtype):
+    if values.dtype.kind in "iufcb":
         values = values.astype("float64")
     fill_value = na_value_for_dtype(values.dtype)
 
@@ -521,7 +520,7 @@ def nanany(
         # expected "bool")
         return values.any(axis)  # type: ignore[return-value]
 
-    if needs_i8_conversion(values.dtype) and values.dtype.kind != "m":
+    if values.dtype.kind == "M":
         # GH#34479
         warnings.warn(
             "'any' with datetime64 dtypes is deprecated and will raise in a "
@@ -582,7 +581,7 @@ def nanall(
         # expected "bool")
         return values.all(axis)  # type: ignore[return-value]
 
-    if needs_i8_conversion(values.dtype) and values.dtype.kind != "m":
+    if values.dtype.kind == "M":
         # GH#34479
         warnings.warn(
             "'all' with datetime64 dtypes is deprecated and will raise in a "
@@ -976,12 +975,12 @@ def nanvar(
     """
     dtype = values.dtype
     mask = _maybe_get_mask(values, skipna, mask)
-    if is_any_int_dtype(dtype):
+    if dtype.kind in "iu":
         values = values.astype("f8")
         if mask is not None:
             values[mask] = np.nan
 
-    if is_float_dtype(values.dtype):
+    if values.dtype.kind == "f":
         count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
     else:
         count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
@@ -1007,7 +1006,7 @@ def nanvar(
     # Return variance as np.float64 (the datatype used in the accumulator),
     # unless we were dealing with a float array, in which case use the same
     # precision as the original values array.
-    if is_float_dtype(dtype):
+    if dtype.kind == "f":
         result = result.astype(dtype, copy=False)
     return result
 

pandas/core/series.py

@@ -64,7 +64,6 @@
 )
 from pandas.core.dtypes.common import (
     is_dict_like,
-    is_extension_array_dtype,
     is_integer,
     is_iterator,
     is_list_like,
@@ -73,6 +72,7 @@
     pandas_dtype,
     validate_all_hashable,
 )
+from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import ABCDataFrame
 from pandas.core.dtypes.inference import is_hashable
 from pandas.core.dtypes.missing import (
@@ -1872,7 +1872,7 @@ def to_dict(self, into: type[dict] = dict) -> dict:
         # GH16122
         into_c = com.standardize_mapping(into)
 
-        if is_object_dtype(self) or is_extension_array_dtype(self):
+        if is_object_dtype(self.dtype) or isinstance(self.dtype, ExtensionDtype):
             return into_c((k, maybe_box_native(v)) for k, v in self.items())
         else:
             # Not an object dtype => all types will be the same so let the default
@@ -4175,7 +4175,7 @@ def explode(self, ignore_index: bool = False) -> Series:
         3      4
         dtype: object
         """
-        if not len(self) or not is_object_dtype(self):
+        if not len(self) or not is_object_dtype(self.dtype):
             result = self.copy()
             return result.reset_index(drop=True) if ignore_index else result
 
@@ -5376,7 +5376,7 @@ def _convert_dtypes(
         input_series = self
         if infer_objects:
             input_series = input_series.infer_objects()
-            if is_object_dtype(input_series):
+            if is_object_dtype(input_series.dtype):
                 input_series = input_series.copy(deep=None)
 
         if convert_string or convert_integer or convert_boolean or convert_floating: