PERF: use is_foo_dtype fastpaths

pandas/core/arrays/categorical.py

@@ -134,7 +134,7 @@ def func(self, other):
                     "use 'np.asarray(cat) <op> other'."
                 )
 
-            if isinstance(other, ExtensionArray) and needs_i8_conversion(other):
+            if isinstance(other, ExtensionArray) and needs_i8_conversion(other.dtype):
                 # We would return NotImplemented here, but that messes up
                 #  ExtensionIndex's wrapped methods
                 return op(other, self)

pandas/core/arrays/datetimelike.py

@@ -363,7 +363,7 @@ class TimelikeOps:
 
     def _round(self, freq, mode, ambiguous, nonexistent):
         # round the local times
-        if is_datetime64tz_dtype(self):
+        if is_datetime64tz_dtype(self.dtype):
             # operate on naive timestamps, then convert back to aware
             naive = self.tz_localize(None)
             result = naive._round(freq, mode, ambiguous, nonexistent)
@@ -1032,7 +1032,7 @@ def fillna(self, value=None, method=None, limit=None):
                     values = values.copy()
 
                 new_values = func(values, limit=limit, mask=mask)
-                if is_datetime64tz_dtype(self):
+                if is_datetime64tz_dtype(self.dtype):
                     # we need to pass int64 values to the constructor to avoid
                     #  re-localizing incorrectly
                     new_values = new_values.view("i8")
@@ -1379,6 +1379,7 @@ def _time_shift(self, periods, freq=None):
 
     @unpack_zerodim_and_defer("__add__")
     def __add__(self, other):
+        other_dtype = getattr(other, "dtype", None)
 
         # scalar others
         if other is NaT:
@@ -1398,16 +1399,16 @@ def __add__(self, other):
             result = self._time_shift(other)
 
         # array-like others
-        elif is_timedelta64_dtype(other):
+        elif is_timedelta64_dtype(other_dtype):
             # TimedeltaIndex, ndarray[timedelta64]
             result = self._add_timedelta_arraylike(other)
-        elif is_object_dtype(other):
+        elif is_object_dtype(other_dtype):
             # e.g. Array/Index of DateOffset objects
             result = self._addsub_object_array(other, operator.add)
-        elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
+        elif is_datetime64_dtype(other_dtype) or is_datetime64tz_dtype(other_dtype):
             # DatetimeIndex, ndarray[datetime64]
             return self._add_datetime_arraylike(other)
-        elif is_integer_dtype(other):
+        elif is_integer_dtype(other_dtype):
             if not is_period_dtype(self.dtype):
                 raise integer_op_not_supported(self)
             result = self._addsub_int_array(other, operator.add)
@@ -1419,7 +1420,7 @@ def __add__(self, other):
             #  In remaining cases, this will end up raising TypeError.
             return NotImplemented
 
-        if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
+        if isinstance(result, np.ndarray) and is_timedelta64_dtype(result.dtype):
             from pandas.core.arrays import TimedeltaArray
 
             return TimedeltaArray(result)
@@ -1455,13 +1456,13 @@ def __sub__(self, other):
             result = self._sub_period(other)
 
         # array-like others
-        elif is_timedelta64_dtype(other):
+        elif is_timedelta64_dtype(other_dtype):
             # TimedeltaIndex, ndarray[timedelta64]
             result = self._add_timedelta_arraylike(-other)
-        elif is_object_dtype(other):
+        elif is_object_dtype(other_dtype):
             # e.g. Array/Index of DateOffset objects
             result = self._addsub_object_array(other, operator.sub)
-        elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
+        elif is_datetime64_dtype(other_dtype) or is_datetime64tz_dtype(other_dtype):
             # DatetimeIndex, ndarray[datetime64]
             result = self._sub_datetime_arraylike(other)
         elif is_period_dtype(other_dtype):
@@ -1475,14 +1476,16 @@ def __sub__(self, other):
             # Includes ExtensionArrays, float_dtype
             return NotImplemented
 
-        if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
+        if isinstance(result, np.ndarray) and is_timedelta64_dtype(result.dtype):
             from pandas.core.arrays import TimedeltaArray
 
             return TimedeltaArray(result)
         return result
 
     def __rsub__(self, other):
-        if is_datetime64_any_dtype(other) and is_timedelta64_dtype(self.dtype):
+        other_dtype = getattr(other, "dtype", None)
+
+        if is_datetime64_any_dtype(other_dtype) and is_timedelta64_dtype(self.dtype):
             # ndarray[datetime64] cannot be subtracted from self, so
             # we need to wrap in DatetimeArray/Index and flip the operation
             if lib.is_scalar(other):
@@ -1504,7 +1507,7 @@ def __rsub__(self, other):
             raise TypeError(
                 f"cannot subtract {type(self).__name__} from {type(other).__name__}"
             )
-        elif is_period_dtype(self.dtype) and is_timedelta64_dtype(other):
+        elif is_period_dtype(self.dtype) and is_timedelta64_dtype(other_dtype):
             # TODO: Can we simplify/generalize these cases at all?
             raise TypeError(f"cannot subtract {type(self).__name__} from {other.dtype}")
         elif is_timedelta64_dtype(self.dtype):

pandas/core/arrays/datetimes.py

@@ -631,7 +631,9 @@ def _has_same_tz(self, other):
     def _assert_tzawareness_compat(self, other):
         # adapted from _Timestamp._assert_tzawareness_compat
         other_tz = getattr(other, "tzinfo", None)
-        if is_datetime64tz_dtype(other):
+        other_dtype = getattr(other, "dtype", None)
+
+        if is_datetime64tz_dtype(other_dtype):
             # Get tzinfo from Series dtype
             other_tz = other.dtype.tz
         if other is NaT:
@@ -1913,8 +1915,9 @@ def sequence_to_dt64ns(
 
     # By this point we are assured to have either a numpy array or Index
     data, copy = maybe_convert_dtype(data, copy)
+    data_dtype = getattr(data, "dtype", None)
 
-    if is_object_dtype(data) or is_string_dtype(data):
+    if is_object_dtype(data_dtype) or is_string_dtype(data_dtype):
         # TODO: We do not have tests specific to string-dtypes,
         #  also complex or categorical or other extension
         copy = False
@@ -1927,15 +1930,16 @@ def sequence_to_dt64ns(
                 data, dayfirst=dayfirst, yearfirst=yearfirst
             )
             tz = maybe_infer_tz(tz, inferred_tz)
+        data_dtype = data.dtype
 
     # `data` may have originally been a Categorical[datetime64[ns, tz]],
     # so we need to handle these types.
-    if is_datetime64tz_dtype(data):
+    if is_datetime64tz_dtype(data_dtype):
         # DatetimeArray -> ndarray
         tz = maybe_infer_tz(tz, data.tz)
         result = data._data
 
-    elif is_datetime64_dtype(data):
+    elif is_datetime64_dtype(data_dtype):
         # tz-naive DatetimeArray or ndarray[datetime64]
         data = getattr(data, "_data", data)
         if data.dtype != DT64NS_DTYPE:

pandas/core/arrays/masked.py

@@ -8,7 +8,12 @@
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.base import ExtensionDtype
-from pandas.core.dtypes.common import is_integer, is_object_dtype, is_string_dtype
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
 from pandas.core.dtypes.missing import isna, notna
 
 from pandas.core.algorithms import _factorize_array, take
@@ -156,7 +161,9 @@ def to_numpy(
         if na_value is lib.no_default:
             na_value = libmissing.NA
         if dtype is None:
-            dtype = object
+            dtype = np.dtype(object)
+        else:
+            dtype = pandas_dtype(dtype)
         if self._hasna:
             if (
                 not (is_object_dtype(dtype) or is_string_dtype(dtype))

pandas/core/arrays/sparse/array.py

@@ -1517,7 +1517,7 @@ def make_sparse(arr: np.ndarray, kind="block", fill_value=None, dtype=None, copy
         mask = notna(arr)
     else:
         # cast to object comparison to be safe
-        if is_string_dtype(arr):
+        if is_string_dtype(arr.dtype):
             arr = arr.astype(object)
 
         if is_object_dtype(arr.dtype):

pandas/core/dtypes/cast.py

@@ -1366,7 +1366,7 @@ def maybe_cast_to_datetime(value, dtype, errors: str = "raise"):
                             # is solved. String data that is passed with a
                             # datetime64tz is assumed to be naive which should
                             # be localized to the timezone.
-                            is_dt_string = is_string_dtype(value)
+                            is_dt_string = is_string_dtype(value.dtype)
                             value = to_datetime(value, errors=errors).array
                             if is_dt_string:
                                 # Strings here are naive, so directly localize

pandas/core/dtypes/concat.py

@@ -40,14 +40,14 @@ def get_dtype_kinds(l):
         dtype = arr.dtype
         if is_categorical_dtype(dtype):
             typ = "category"
-        elif is_sparse(arr):
+        elif is_sparse(dtype):
             typ = "sparse"
         elif isinstance(arr, ABCRangeIndex):
             typ = "range"
-        elif is_datetime64tz_dtype(arr):
+        elif is_datetime64tz_dtype(dtype):
             # if to_concat contains different tz,
             # the result must be object dtype
-            typ = str(arr.dtype)
+            typ = str(dtype)
         elif is_datetime64_dtype(dtype):
             typ = "datetime"
         elif is_timedelta64_dtype(dtype):
@@ -57,7 +57,7 @@ def get_dtype_kinds(l):
         elif is_bool_dtype(dtype):
             typ = "bool"
         elif is_extension_array_dtype(dtype):
-            typ = str(arr.dtype)
+            typ = str(dtype)
         else:
             typ = dtype.kind
         typs.add(typ)

pandas/core/dtypes/missing.py

@@ -393,7 +393,7 @@ def array_equivalent(left, right, strict_nan: bool = False) -> bool:
 
     # Object arrays can contain None, NaN and NaT.
     # string dtypes must be come to this path for NumPy 1.7.1 compat
-    if is_string_dtype(left) or is_string_dtype(right):
+    if is_string_dtype(left.dtype) or is_string_dtype(right.dtype):
 
         if not strict_nan:
             # isna considers NaN and None to be equivalent.

pandas/core/generic.py

@@ -6907,9 +6907,9 @@ def interpolate(
             index = df.index
             methods = {"index", "values", "nearest", "time"}
             is_numeric_or_datetime = (
-                is_numeric_dtype(index)
-                or is_datetime64_any_dtype(index)
-                or is_timedelta64_dtype(index)
+                is_numeric_dtype(index.dtype)
+                or is_datetime64_any_dtype(index.dtype)
+                or is_timedelta64_dtype(index.dtype)
             )
             if method not in methods and not is_numeric_or_datetime:
                 raise ValueError(
@@ -8588,7 +8588,7 @@ def _align_frame(
             right = right.fillna(method=method, axis=fill_axis, limit=limit)
 
         # if DatetimeIndex have different tz, convert to UTC
-        if is_datetime64tz_dtype(left.index):
+        if is_datetime64tz_dtype(left.index.dtype):
             if left.index.tz != right.index.tz:
                 if join_index is not None:
                     left.index = join_index
@@ -8675,7 +8675,7 @@ def _align_series(
 
         # if DatetimeIndex have different tz, convert to UTC
         if is_series or (not is_series and axis == 0):
-            if is_datetime64tz_dtype(left.index):
+            if is_datetime64tz_dtype(left.index.dtype):
                 if left.index.tz != right.index.tz:
                     if join_index is not None:
                         left.index = join_index
@@ -9957,13 +9957,13 @@ def describe_timestamp_1d(data):
             return pd.Series(d, index=stat_index, name=data.name)
 
         def describe_1d(data):
-            if is_bool_dtype(data):
+            if is_bool_dtype(data.dtype):
                 return describe_categorical_1d(data)
             elif is_numeric_dtype(data):
                 return describe_numeric_1d(data)
-            elif is_datetime64_any_dtype(data):
+            elif is_datetime64_any_dtype(data.dtype):
                 return describe_timestamp_1d(data)
-            elif is_timedelta64_dtype(data):
+            elif is_timedelta64_dtype(data.dtype):
                 return describe_numeric_1d(data)
             else:
                 return describe_categorical_1d(data)

pandas/core/groupby/generic.py

@@ -690,7 +690,7 @@ def value_counts(
             lab = lev.take(lab.cat.codes)
             llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]
 
-        if is_interval_dtype(lab):
+        if is_interval_dtype(lab.dtype):
             # TODO: should we do this inside II?
             sorter = np.lexsort((lab.left, lab.right, ids))
         else:

pandas/core/groupby/ops.py

@@ -461,12 +461,12 @@ def _cython_operation(
         # are not setup for dim transforming
         if is_categorical_dtype(values.dtype) or is_sparse(values.dtype):
             raise NotImplementedError(f"{values.dtype} dtype not supported")
-        elif is_datetime64_any_dtype(values):
+        elif is_datetime64_any_dtype(values.dtype):
             if how in ["add", "prod", "cumsum", "cumprod"]:
                 raise NotImplementedError(
                     f"datetime64 type does not support {how} operations"
                 )
-        elif is_timedelta64_dtype(values):
+        elif is_timedelta64_dtype(values.dtype):
             if how in ["prod", "cumprod"]:
                 raise NotImplementedError(
                     f"timedelta64 type does not support {how} operations"

pandas/core/indexes/category.py

@@ -17,6 +17,7 @@
     is_interval_dtype,
     is_list_like,
     is_scalar,
+    pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
@@ -372,6 +373,9 @@ def __contains__(self, key: Any) -> bool:
 
     @doc(Index.astype)
     def astype(self, dtype, copy=True):
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
         if is_interval_dtype(dtype):
             from pandas import IntervalIndex
 

pandas/core/internals/blocks.py

@@ -2714,7 +2714,7 @@ def make_block(values, placement, klass=None, ndim=None, dtype=None):
         dtype = dtype or values.dtype
         klass = get_block_type(values, dtype)
 
-    elif klass is DatetimeTZBlock and not is_datetime64tz_dtype(values):
+    elif klass is DatetimeTZBlock and not is_datetime64tz_dtype(values.dtype):
         # TODO: This is no longer hit internally; does it need to be retained
         #  for e.g. pyarrow?
         values = DatetimeArray._simple_new(values, dtype=dtype)

pandas/core/internals/concat.py

@@ -339,7 +339,7 @@ def _get_empty_dtype_and_na(join_units):
     if len(join_units) == 1:
         blk = join_units[0].block
         if blk is None:
-            return np.float64, np.nan
+            return np.dtype(np.float64), np.nan
 
     if _is_uniform_reindex(join_units):
         # FIXME: integrate property
@@ -424,7 +424,7 @@ def _get_empty_dtype_and_na(join_units):
                 return g, g.type(np.nan)
             elif is_numeric_dtype(g):
                 if has_none_blocks:
-                    return np.float64, np.nan
+                    return np.dtype(np.float64), np.nan
                 else:
                     return g, None
 

pandas/core/nanops.py

@@ -759,12 +759,12 @@ def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
     values = extract_array(values, extract_numpy=True)
     dtype = values.dtype
     mask = _maybe_get_mask(values, skipna, mask)
-    if is_any_int_dtype(values):
+    if is_any_int_dtype(dtype):
         values = values.astype("f8")
         if mask is not None:
             values[mask] = np.nan
 
-    if is_float_dtype(values):
+    if is_float_dtype(values.dtype):
         count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
     else:
         count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)