PERF: use fastpaths for is_period_dtype checks

pandas/_testing.py

@@ -1519,11 +1519,13 @@ def box_expected(expected, box_cls, transpose=True):
 
 def to_array(obj):
     # temporary implementation until we get pd.array in place
-    if is_period_dtype(obj):
+    dtype = getattr(obj, "dtype", None)
+
+    if is_period_dtype(dtype):
         return period_array(obj)
-    elif is_datetime64_dtype(obj) or is_datetime64tz_dtype(obj):
+    elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
         return DatetimeArray._from_sequence(obj)
-    elif is_timedelta64_dtype(obj):
+    elif is_timedelta64_dtype(dtype):
         return TimedeltaArray._from_sequence(obj)
     else:
         return np.array(obj)

pandas/core/algorithms.py

@@ -11,7 +11,7 @@
 
 from pandas._libs import Timestamp, algos, hashtable as htable, lib
 from pandas._libs.tslib import iNaT
-from pandas._typing import AnyArrayLike
+from pandas._typing import AnyArrayLike, DtypeObj
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.cast import (
@@ -126,20 +126,21 @@ def _ensure_data(values, dtype=None):
         return ensure_object(values), "object"
 
     # datetimelike
-    if needs_i8_conversion(values) or needs_i8_conversion(dtype):
-        if is_period_dtype(values) or is_period_dtype(dtype):
+    vals_dtype = getattr(values, "dtype", None)
+    if needs_i8_conversion(vals_dtype) or needs_i8_conversion(dtype):
+        if is_period_dtype(vals_dtype) or is_period_dtype(dtype):
             from pandas import PeriodIndex
 
             values = PeriodIndex(values)
             dtype = values.dtype
-        elif is_timedelta64_dtype(values) or is_timedelta64_dtype(dtype):
+        elif is_timedelta64_dtype(vals_dtype) or is_timedelta64_dtype(dtype):
             from pandas import TimedeltaIndex
 
             values = TimedeltaIndex(values)
             dtype = values.dtype
         else:
             # Datetime
-            if values.ndim > 1 and is_datetime64_ns_dtype(values):
+            if values.ndim > 1 and is_datetime64_ns_dtype(vals_dtype):
                 # Avoid calling the DatetimeIndex constructor as it is 1D only
                 # Note: this is reached by DataFrame.rank calls GH#27027
                 # TODO(EA2D): special case not needed with 2D EAs
@@ -154,7 +155,7 @@ def _ensure_data(values, dtype=None):
 
         return values.asi8, dtype
 
-    elif is_categorical_dtype(values) and (
+    elif is_categorical_dtype(vals_dtype) and (
         is_categorical_dtype(dtype) or dtype is None
     ):
         values = values.codes
@@ -1080,7 +1081,7 @@ def nsmallest(self):
         return self.compute("nsmallest")
 
     @staticmethod
-    def is_valid_dtype_n_method(dtype) -> bool:
+    def is_valid_dtype_n_method(dtype: DtypeObj) -> bool:
         """
         Helper function to determine if dtype is valid for
         nsmallest/nlargest methods
@@ -1863,7 +1864,7 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
 
     is_timedelta = False
     is_bool = False
-    if needs_i8_conversion(arr):
+    if needs_i8_conversion(arr.dtype):
         dtype = np.float64
         arr = arr.view("i8")
         na = iNaT

pandas/core/arrays/categorical.py

@@ -1424,7 +1424,7 @@ def _internal_get_values(self):
             Index if datetime / periods.
         """
         # if we are a datetime and period index, return Index to keep metadata
-        if needs_i8_conversion(self.categories):
+        if needs_i8_conversion(self.categories.dtype):
             return self.categories.take(self._codes, fill_value=np.nan)
         elif is_integer_dtype(self.categories) and -1 in self._codes:
             return self.categories.astype("object").take(self._codes, fill_value=np.nan)

pandas/core/arrays/datetimelike.py

@@ -782,7 +782,7 @@ def _validate_searchsorted_value(self, value):
         elif is_list_like(value) and not isinstance(value, type(self)):
             value = array(value)
 
-            if not type(self)._is_recognized_dtype(value):
+            if not type(self)._is_recognized_dtype(value.dtype):
                 raise TypeError(
                     "searchsorted requires compatible dtype or scalar, "
                     f"not {type(value).__name__}"
@@ -806,7 +806,7 @@ def _validate_setitem_value(self, value):
                 except ValueError:
                     pass
 
-            if not type(self)._is_recognized_dtype(value):
+            if not type(self)._is_recognized_dtype(value.dtype):
                 raise TypeError(
                     "setitem requires compatible dtype or scalar, "
                     f"not {type(value).__name__}"
@@ -1024,7 +1024,7 @@ def fillna(self, value=None, method=None, limit=None):
                     func = missing.backfill_1d
 
                 values = self._data
-                if not is_period_dtype(self):
+                if not is_period_dtype(self.dtype):
                     # For PeriodArray self._data is i8, which gets copied
                     #  by `func`.  Otherwise we need to make a copy manually
                     # to avoid modifying `self` in-place.
@@ -1109,10 +1109,7 @@ def _validate_frequency(cls, index, freq, **kwargs):
         freq : DateOffset
             The frequency to validate
         """
-        if is_period_dtype(cls):
-            # Frequency validation is not meaningful for Period Array/Index
-            return None
-
+        # TODO: this is not applicable to PeriodArray, move to correct Mixin
         inferred = index.inferred_freq
         if index.size == 0 or inferred == freq.freqstr:
             return None
@@ -1253,7 +1250,7 @@ def _add_nat(self):
         """
         Add pd.NaT to self
         """
-        if is_period_dtype(self):
+        if is_period_dtype(self.dtype):
             raise TypeError(
                 f"Cannot add {type(self).__name__} and {type(NaT).__name__}"
             )
@@ -1293,7 +1290,7 @@ def _sub_period_array(self, other):
         result : np.ndarray[object]
             Array of DateOffset objects; nulls represented by NaT.
         """
-        if not is_period_dtype(self):
+        if not is_period_dtype(self.dtype):
             raise TypeError(
                 f"cannot subtract {other.dtype}-dtype from {type(self).__name__}"
             )
@@ -1398,7 +1395,7 @@ def __add__(self, other):
         elif lib.is_integer(other):
             # This check must come after the check for np.timedelta64
             # as is_integer returns True for these
-            if not is_period_dtype(self):
+            if not is_period_dtype(self.dtype):
                 raise integer_op_not_supported(self)
             result = self._time_shift(other)
 
@@ -1413,7 +1410,7 @@ def __add__(self, other):
             # DatetimeIndex, ndarray[datetime64]
             return self._add_datetime_arraylike(other)
         elif is_integer_dtype(other):
-            if not is_period_dtype(self):
+            if not is_period_dtype(self.dtype):
                 raise integer_op_not_supported(self)
             result = self._addsub_int_array(other, operator.add)
         else:
@@ -1437,6 +1434,8 @@ def __radd__(self, other):
     @unpack_zerodim_and_defer("__sub__")
     def __sub__(self, other):
 
+        other_dtype = getattr(other, "dtype", None)
+
         # scalar others
         if other is NaT:
             result = self._sub_nat()
@@ -1450,7 +1449,7 @@ def __sub__(self, other):
         elif lib.is_integer(other):
             # This check must come after the check for np.timedelta64
             # as is_integer returns True for these
-            if not is_period_dtype(self):
+            if not is_period_dtype(self.dtype):
                 raise integer_op_not_supported(self)
             result = self._time_shift(-other)
 
@@ -1467,11 +1466,11 @@ def __sub__(self, other):
         elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
             # DatetimeIndex, ndarray[datetime64]
             result = self._sub_datetime_arraylike(other)
-        elif is_period_dtype(other):
+        elif is_period_dtype(other_dtype):
             # PeriodIndex
             result = self._sub_period_array(other)
-        elif is_integer_dtype(other):
-            if not is_period_dtype(self):
+        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.sub)
         else:
@@ -1520,7 +1519,7 @@ def __iadd__(self, other):
         result = self + other
         self[:] = result[:]
 
-        if not is_period_dtype(self):
+        if not is_period_dtype(self.dtype):
             # restore freq, which is invalidated by setitem
             self._freq = result._freq
         return self
@@ -1529,7 +1528,7 @@ def __isub__(self, other):
         result = self - other
         self[:] = result[:]
 
-        if not is_period_dtype(self):
+        if not is_period_dtype(self.dtype):
             # restore freq, which is invalidated by setitem
             self._freq = result._freq
         return self
@@ -1621,7 +1620,7 @@ def mean(self, skipna=True):
         -----
         mean is only defined for Datetime and Timedelta dtypes, not for Period.
         """
-        if is_period_dtype(self):
+        if is_period_dtype(self.dtype):
             # See discussion in GH#24757
             raise TypeError(
                 f"mean is not implemented for {type(self).__name__} since the "

pandas/core/arrays/period.py

@@ -828,9 +828,11 @@ def period_array(
     ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
     Length: 4, dtype: period[Q-DEC]
     """
-    if is_datetime64_dtype(data):
+    data_dtype = getattr(data, "dtype", None)
+
+    if is_datetime64_dtype(data_dtype):
         return PeriodArray._from_datetime64(data, freq)
-    if is_period_dtype(data):
+    if is_period_dtype(data_dtype):
         return PeriodArray(data, freq)
 
     # other iterable of some kind

pandas/core/dtypes/missing.py

@@ -424,7 +424,7 @@ def array_equivalent(left, right, strict_nan: bool = False) -> bool:
         return True
 
     # NaNs can occur in float and complex arrays.
-    if is_float_dtype(left) or is_complex_dtype(left):
+    if is_float_dtype(left.dtype) or is_complex_dtype(left.dtype):
 
         # empty
         if not (np.prod(left.shape) and np.prod(right.shape)):
@@ -435,7 +435,7 @@ def array_equivalent(left, right, strict_nan: bool = False) -> bool:
         # GH#29553 avoid numpy deprecation warning
         return False
 
-    elif needs_i8_conversion(left) or needs_i8_conversion(right):
+    elif needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype):
         # datetime64, timedelta64, Period
         if not is_dtype_equal(left.dtype, right.dtype):
             return False
@@ -460,7 +460,7 @@ def _infer_fill_value(val):
     if not is_list_like(val):
         val = [val]
     val = np.array(val, copy=False)
-    if needs_i8_conversion(val):
+    if needs_i8_conversion(val.dtype):
         return np.array("NaT", dtype=val.dtype)
     elif is_object_dtype(val.dtype):
         dtype = lib.infer_dtype(ensure_object(val), skipna=False)

pandas/core/frame.py

@@ -5916,7 +5916,7 @@ def extract_values(arr):
             if isinstance(arr, (ABCIndexClass, ABCSeries)):
                 arr = arr._values
 
-            if needs_i8_conversion(arr):
+            if needs_i8_conversion(arr.dtype):
                 if is_extension_array_dtype(arr.dtype):
                     arr = arr.asi8
                 else:

pandas/core/indexes/accessors.py

@@ -49,7 +49,7 @@ def _get_values(self):
         elif is_timedelta64_dtype(data.dtype):
             return TimedeltaIndex(data, copy=False, name=self.name)
 
-        elif is_period_dtype(data):
+        elif is_period_dtype(data.dtype):
             return PeriodArray(data, copy=False)
 
         raise TypeError(
@@ -449,7 +449,7 @@ def __new__(cls, data: "Series"):
             return DatetimeProperties(data, orig)
         elif is_timedelta64_dtype(data.dtype):
             return TimedeltaProperties(data, orig)
-        elif is_period_dtype(data):
+        elif is_period_dtype(data.dtype):
             return PeriodProperties(data, orig)
 
         raise AttributeError("Can only use .dt accessor with datetimelike values")

pandas/core/indexes/datetimelike.py

@@ -118,7 +118,7 @@ def __array_wrap__(self, result, context=None):
             return result
 
         attrs = self._get_attributes_dict()
-        if not is_period_dtype(self) and attrs["freq"]:
+        if not is_period_dtype(self.dtype) and attrs["freq"]:
             # no need to infer if freq is None
             attrs["freq"] = "infer"
         return Index(result, **attrs)
@@ -542,7 +542,7 @@ def delete(self, loc):
         new_i8s = np.delete(self.asi8, loc)
 
         freq = None
-        if is_period_dtype(self):
+        if is_period_dtype(self.dtype):
             freq = self.freq
         elif is_integer(loc):
             if loc in (0, -len(self), -1, len(self) - 1):

pandas/core/internals/blocks.py

@@ -2673,13 +2673,13 @@ def get_block_type(values, dtype=None):
     elif is_categorical_dtype(values.dtype):
         cls = CategoricalBlock
     elif issubclass(vtype, np.datetime64):
-        assert not is_datetime64tz_dtype(values)
+        assert not is_datetime64tz_dtype(values.dtype)
         cls = DatetimeBlock
-    elif is_datetime64tz_dtype(values):
+    elif is_datetime64tz_dtype(values.dtype):
         cls = DatetimeTZBlock
     elif is_interval_dtype(dtype) or is_period_dtype(dtype):
         cls = ObjectValuesExtensionBlock
-    elif is_extension_array_dtype(values):
+    elif is_extension_array_dtype(values.dtype):
         cls = ExtensionBlock
     elif issubclass(vtype, np.floating):
         cls = FloatBlock

pandas/core/missing.py

@@ -266,7 +266,7 @@ def interpolate_1d(
         if method in ("values", "index"):
             inds = np.asarray(xvalues)
             # hack for DatetimeIndex, #1646
-            if needs_i8_conversion(inds.dtype.type):
+            if needs_i8_conversion(inds.dtype):
                 inds = inds.view(np.int64)
             if inds.dtype == np.object_:
                 inds = lib.maybe_convert_objects(inds)

pandas/core/nanops.py

@@ -287,7 +287,7 @@ def _get_values(
 
     dtype = values.dtype
 
-    if needs_i8_conversion(values):
+    if needs_i8_conversion(values.dtype):
         # changing timedelta64/datetime64 to int64 needs to happen after
         #  finding `mask` above
         values = np.asarray(values.view("i8"))

pandas/core/reshape/reshape.py

@@ -232,10 +232,10 @@ def get_new_values(self, values, fill_value=None):
         # we need to convert to a basic dtype
         # and possibly coerce an input to our output dtype
         # e.g. ints -> floats
-        if needs_i8_conversion(values):
+        if needs_i8_conversion(values.dtype):
             sorted_values = sorted_values.view("i8")
             new_values = new_values.view("i8")
-        elif is_bool_dtype(values):
+        elif is_bool_dtype(values.dtype):
             sorted_values = sorted_values.astype("object")
             new_values = new_values.astype("object")
         else:
@@ -253,7 +253,7 @@ def get_new_values(self, values, fill_value=None):
         )
 
         # reconstruct dtype if needed
-        if needs_i8_conversion(values):
+        if needs_i8_conversion(values.dtype):
             new_values = new_values.view(values.dtype)
 
         return new_values, new_mask

pandas/io/json/_json.py

@@ -306,7 +306,7 @@ def __init__(
         if len(timedeltas):
             obj[timedeltas] = obj[timedeltas].applymap(lambda x: x.isoformat())
         # Convert PeriodIndex to datetimes before serializing
-        if is_period_dtype(obj.index):
+        if is_period_dtype(obj.index.dtype):
             obj.index = obj.index.to_timestamp()
 
         # exclude index from obj if index=False