REF: Rename is_valid_nat_for_dtype -> is_valid_na_for_dtype

pandas/core/arrays/categorical.py

@@ -54,7 +54,7 @@
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
 from pandas.core.dtypes.generic import ABCIndex, ABCSeries
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
+from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, notna
 
 from pandas.core import ops
 from pandas.core.accessor import PandasDelegate, delegate_names
@@ -1284,7 +1284,7 @@ def _validate_fill_value(self, fill_value):
         TypeError
         """
 
-        if is_valid_nat_for_dtype(fill_value, self.categories.dtype):
+        if is_valid_na_for_dtype(fill_value, self.categories.dtype):
             fill_value = -1
         elif fill_value in self.categories:
             fill_value = self._unbox_scalar(fill_value)
@@ -1779,7 +1779,7 @@ def __contains__(self, key) -> bool:
         Returns True if `key` is in this Categorical.
         """
         # if key is a NaN, check if any NaN is in self.
-        if is_valid_nat_for_dtype(key, self.categories.dtype):
+        if is_valid_na_for_dtype(key, self.categories.dtype):
             return self.isna().any()
 
         return contains(self, key, container=self._codes)

pandas/core/arrays/datetimelike.py

@@ -57,7 +57,7 @@
     is_unsigned_integer_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
+from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna
 
 from pandas.core import nanops, ops
 from pandas.core.algorithms import checked_add_with_arr, isin, unique1d
@@ -493,7 +493,7 @@ def _validate_fill_value(self, fill_value):
 
     def _validate_shift_value(self, fill_value):
         # TODO(2.0): once this deprecation is enforced, use _validate_fill_value
-        if is_valid_nat_for_dtype(fill_value, self.dtype):
+        if is_valid_na_for_dtype(fill_value, self.dtype):
             fill_value = NaT
         elif isinstance(fill_value, self._recognized_scalars):
             # pandas\core\arrays\datetimelike.py:746: error: Too many arguments
@@ -557,7 +557,7 @@ def _validate_scalar(
                 msg = self._validation_error_message(value, allow_listlike)
                 raise TypeError(msg) from err
 
-        elif is_valid_nat_for_dtype(value, self.dtype):
+        elif is_valid_na_for_dtype(value, self.dtype):
             # GH#18295
             value = NaT
 

pandas/core/arrays/interval.py

@@ -43,7 +43,7 @@
     ABCPeriodIndex,
     ABCSeries,
 )
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
+from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, notna
 
 from pandas.core.algorithms import isin, take, value_counts
 from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
@@ -979,7 +979,7 @@ def _validate_scalar(self, value):
         if isinstance(value, Interval):
             self._check_closed_matches(value, name="value")
             left, right = value.left, value.right
-        elif is_valid_nat_for_dtype(value, self.left.dtype):
+        elif is_valid_na_for_dtype(value, self.left.dtype):
             # GH#18295
             left = right = value
         else:
@@ -994,7 +994,7 @@ def _validate_fill_value(self, value):
     def _validate_setitem_value(self, value):
         needs_float_conversion = False
 
-        if is_valid_nat_for_dtype(value, self.left.dtype):
+        if is_valid_na_for_dtype(value, self.left.dtype):
             # na value: need special casing to set directly on numpy arrays
             if is_integer_dtype(self.dtype.subtype):
                 # can't set NaN on a numpy integer array

pandas/core/dtypes/cast.py

@@ -87,7 +87,7 @@
     ABCSeries,
 )
 from pandas.core.dtypes.inference import is_list_like
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
+from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, notna
 
 if TYPE_CHECKING:
     from pandas import Series
@@ -159,7 +159,7 @@ def maybe_unbox_datetimelike(value: Scalar, dtype: DtypeObj) -> Scalar:
     -----
     Caller is responsible for checking dtype.kind in ["m", "M"]
     """
-    if is_valid_nat_for_dtype(value, dtype):
+    if is_valid_na_for_dtype(value, dtype):
         # GH#36541: can't fill array directly with pd.NaT
         # > np.empty(10, dtype="datetime64[64]").fill(pd.NaT)
         # ValueError: cannot convert float NaN to integer
@@ -535,7 +535,7 @@ def maybe_promote(dtype, fill_value=np.nan):
             dtype = np.dtype(np.object_)
         elif is_integer(fill_value) or (is_float(fill_value) and not isna(fill_value)):
             dtype = np.dtype(np.object_)
-        elif is_valid_nat_for_dtype(fill_value, dtype):
+        elif is_valid_na_for_dtype(fill_value, dtype):
             # e.g. pd.NA, which is not accepted by Timestamp constructor
             fill_value = np.datetime64("NaT", "ns")
         else:
@@ -551,7 +551,7 @@ def maybe_promote(dtype, fill_value=np.nan):
         ):
             # TODO: What about str that can be a timedelta?
             dtype = np.dtype(np.object_)
-        elif is_valid_nat_for_dtype(fill_value, dtype):
+        elif is_valid_na_for_dtype(fill_value, dtype):
             # e.g pd.NA, which is not accepted by the  Timedelta constructor
             fill_value = np.timedelta64("NaT", "ns")
         else:

pandas/core/dtypes/missing.py

@@ -29,7 +29,6 @@
     is_string_dtype,
     is_string_like_dtype,
     needs_i8_conversion,
-    pandas_dtype,
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -535,7 +534,7 @@ def maybe_fill(arr, fill_value=np.nan):
     return arr
 
 
-def na_value_for_dtype(dtype, compat: bool = True):
+def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
     """
     Return a dtype compat na value
 
@@ -561,7 +560,6 @@ def na_value_for_dtype(dtype, compat: bool = True):
     >>> na_value_for_dtype(np.dtype('datetime64[ns]'))
     numpy.datetime64('NaT')
     """
-    dtype = pandas_dtype(dtype)
 
     if is_extension_array_dtype(dtype):
         return dtype.na_value
@@ -590,7 +588,7 @@ def remove_na_arraylike(arr):
         return arr[notna(np.asarray(arr))]
 
 
-def is_valid_nat_for_dtype(obj, dtype: DtypeObj) -> bool:
+def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
     """
     isna check that excludes incompatible dtypes
 

pandas/core/indexes/base.py

@@ -88,7 +88,7 @@
     ABCTimedeltaIndex,
 )
 from pandas.core.dtypes.inference import is_dict_like
-from pandas.core.dtypes.missing import array_equivalent, is_valid_nat_for_dtype, isna
+from pandas.core.dtypes.missing import array_equivalent, is_valid_na_for_dtype, isna
 
 from pandas.core import missing, ops
 from pandas.core.accessor import CachedAccessor
@@ -5216,7 +5216,7 @@ def _find_common_type_compat(self, target) -> DtypeObj:
         Implementation of find_common_type that adjusts for Index-specific
         special cases.
         """
-        if is_interval_dtype(self.dtype) and is_valid_nat_for_dtype(target, self.dtype):
+        if is_interval_dtype(self.dtype) and is_valid_na_for_dtype(target, self.dtype):
             # e.g. setting NA value into IntervalArray[int64]
             self = cast("IntervalIndex", self)
             return IntervalDtype(np.float64, closed=self.closed)
@@ -5770,7 +5770,7 @@ def insert(self, loc: int, item):
         # Note: this method is overridden by all ExtensionIndex subclasses,
         #  so self is never backed by an EA.
         item = lib.item_from_zerodim(item)
-        if is_valid_nat_for_dtype(item, self.dtype) and self.dtype != object:
+        if is_valid_na_for_dtype(item, self.dtype) and self.dtype != object:
             item = self._na_value
 
         try:

pandas/core/indexes/category.py

@@ -15,7 +15,7 @@
     is_categorical_dtype,
     is_scalar,
 )
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
+from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, notna
 
 from pandas.core import accessor
 from pandas.core.arrays.categorical import Categorical, contains
@@ -348,7 +348,7 @@ def inferred_type(self) -> str:
     @doc(Index.__contains__)
     def __contains__(self, key: Any) -> bool:
         # if key is a NaN, check if any NaN is in self.
-        if is_valid_nat_for_dtype(key, self.categories.dtype):
+        if is_valid_na_for_dtype(key, self.categories.dtype):
             return self.hasnans
 
         return contains(self, key, container=self._engine)

pandas/core/indexes/datetimes.py

@@ -26,7 +26,7 @@
     is_datetime64tz_dtype,
     is_scalar,
 )
-from pandas.core.dtypes.missing import is_valid_nat_for_dtype
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
 
 from pandas.core.arrays.datetimes import DatetimeArray, tz_to_dtype
 import pandas.core.common as com
@@ -636,7 +636,7 @@ def get_loc(self, key, method=None, tolerance=None):
             raise InvalidIndexError(key)
 
         orig_key = key
-        if is_valid_nat_for_dtype(key, self.dtype):
+        if is_valid_na_for_dtype(key, self.dtype):
             key = NaT
 
         if isinstance(key, self._data._recognized_scalars):

pandas/core/series.py

@@ -60,6 +60,7 @@
     is_list_like,
     is_object_dtype,
     is_scalar,
+    pandas_dtype,
     validate_all_hashable,
 )
 from pandas.core.dtypes.generic import ABCDataFrame
@@ -405,7 +406,7 @@ def _init_dict(self, data, index=None, dtype: Optional[Dtype] = None):
         elif index is not None:
             # fastpath for Series(data=None). Just use broadcasting a scalar
             # instead of reindexing.
-            values = na_value_for_dtype(dtype)
+            values = na_value_for_dtype(pandas_dtype(dtype))
             keys = index
         else:
             keys, values = (), []