TYP: use DTypeLike alias from numpy.typing

pandas/_typing.py

@@ -11,7 +11,7 @@
 )
 from mmap import mmap
 from os import PathLike
-from typing import (
+from typing import (  # noqa: F401
     IO,
     TYPE_CHECKING,
     Any,
@@ -32,6 +32,13 @@
 
 import numpy as np
 
+try:
+    from numpy.typing import DTypeLike as NpDtype
+except ImportError:
+    # error: Name 'NpDtype' already defined (possibly by an import)
+    NpDtype: Any = None  # type: ignore[no-redef]
+
+
 # To prevent import cycles place any internal imports in the branch below
 # and use a string literal forward reference to it in subsequent types
 # https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
@@ -122,10 +129,7 @@
 Axes = Collection[Any]
 
 # dtypes
-NpDtype = Union[str, np.dtype]
-Dtype = Union[
-    "ExtensionDtype", NpDtype, type_t[Union[str, float, int, complex, bool, object]]
-]
+Dtype = Union["ExtensionDtype", NpDtype]
 # DtypeArg specifies all allowable dtypes in a functions its dtype argument
 DtypeArg = Union[Dtype, Dict[Hashable, Dtype]]
 DtypeObj = Union[np.dtype, "ExtensionDtype"]

pandas/core/apply.py

@@ -1049,11 +1049,7 @@ def apply_standard(self) -> FrameOrSeriesUnion:
 
         with np.errstate(all="ignore"):
             if isinstance(f, np.ufunc):
-                # error: Argument 1 to "__call__" of "ufunc" has incompatible type
-                # "Series"; expected "Union[Union[int, float, complex, str, bytes,
-                # generic], Sequence[Union[int, float, complex, str, bytes, generic]],
-                # Sequence[Sequence[Any]], _SupportsArray]"
-                return f(obj)  # type: ignore[arg-type]
+                return f(obj)
 
             # row-wise access
             if is_extension_array_dtype(obj.dtype) and hasattr(obj._values, "map"):

pandas/core/arrays/masked.py

@@ -280,9 +280,7 @@ def to_numpy(  # type: ignore[override]
         if na_value is lib.no_default:
             na_value = libmissing.NA
         if dtype is None:
-            # error: Incompatible types in assignment (expression has type
-            # "Type[object]", variable has type "Union[str, dtype[Any], None]")
-            dtype = object  # type: ignore[assignment]
+            dtype = object
         if self._hasna:
             if (
                 not is_object_dtype(dtype)
@@ -409,9 +407,7 @@ def isin(self, values) -> BooleanArray:  # type: ignore[override]
                 result += self._mask
             else:
                 result *= np.invert(self._mask)
-        # error: No overload variant of "zeros_like" matches argument types
-        # "BaseMaskedArray", "Type[bool]"
-        mask = np.zeros_like(self, dtype=bool)  # type: ignore[call-overload]
+        mask = np.zeros_like(self, dtype=bool)
         return BooleanArray(result, mask, copy=False)
 
     def copy(self: BaseMaskedArrayT) -> BaseMaskedArrayT:

pandas/core/arrays/sparse/array.py

@@ -523,9 +523,7 @@ def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
             try:
                 dtype = np.result_type(self.sp_values.dtype, type(fill_value))
             except TypeError:
-                # error: Incompatible types in assignment (expression has type
-                # "Type[object]", variable has type "Union[str, dtype[Any], None]")
-                dtype = object  # type: ignore[assignment]
+                dtype = object
 
         out = np.full(self.shape, fill_value, dtype=dtype)
         out[self.sp_index.to_int_index().indices] = self.sp_values
@@ -1449,11 +1447,7 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
             return type(self)(result)
 
     def __abs__(self):
-        # error: Argument 1 to "__call__" of "ufunc" has incompatible type
-        # "SparseArray"; expected "Union[Union[int, float, complex, str, bytes,
-        # generic], Sequence[Union[int, float, complex, str, bytes, generic]],
-        # Sequence[Sequence[Any]], _SupportsArray]"
-        return np.abs(self)  # type: ignore[arg-type]
+        return np.abs(self)
 
     # ------------------------------------------------------------------------
     # Ops

pandas/core/arrays/string_arrow.py

@@ -420,13 +420,7 @@ def fillna(self, value=None, method=None, limit=None):
         if mask.any():
             if method is not None:
                 func = missing.get_fill_func(method)
-                # error: Argument 1 to "to_numpy" of "ArrowStringArray" has incompatible
-                # type "Type[object]"; expected "Union[str, dtype[Any], None]"
-                new_values, _ = func(
-                    self.to_numpy(object),  # type: ignore[arg-type]
-                    limit=limit,
-                    mask=mask,
-                )
+                new_values, _ = func(self.to_numpy(object), limit=limit, mask=mask)
                 new_values = self._from_sequence(new_values)
             else:
                 # fill with value

pandas/core/common.py

@@ -229,12 +229,7 @@ def asarray_tuplesafe(values, dtype: NpDtype | None = None) -> np.ndarray:
         # expected "ndarray")
         return values._values  # type: ignore[return-value]
 
-    # error: Non-overlapping container check (element type: "Union[str, dtype[Any],
-    # None]", container item type: "type")
-    if isinstance(values, list) and dtype in [  # type: ignore[comparison-overlap]
-        np.object_,
-        object,
-    ]:
+    if isinstance(values, list) and dtype in [np.object_, object]:
         return construct_1d_object_array_from_listlike(values)
 
     result = np.asarray(values, dtype=dtype)

pandas/core/construction.py

@@ -593,9 +593,7 @@ def _sanitize_ndim(
         if is_object_dtype(dtype) and isinstance(dtype, ExtensionDtype):
             # i.e. PandasDtype("O")
 
-            # error: Argument "dtype" to "asarray_tuplesafe" has incompatible type
-            # "Type[object]"; expected "Union[str, dtype[Any], None]"
-            result = com.asarray_tuplesafe(data, dtype=object)  # type: ignore[arg-type]
+            result = com.asarray_tuplesafe(data, dtype=object)
             cls = dtype.construct_array_type()
             result = cls._from_sequence(result, dtype=dtype)
         else:

pandas/core/dtypes/dtypes.py

@@ -1295,7 +1295,7 @@ def __init__(self, dtype: NpDtype | PandasDtype | None):
         if isinstance(dtype, PandasDtype):
             # make constructor univalent
             dtype = dtype.numpy_dtype
-        self._dtype = np.dtype(dtype)
+        self._dtype: np.dtype = np.dtype(dtype)
 
     def __repr__(self) -> str:
         return f"PandasDtype({repr(self.name)})"

pandas/core/generic.py

@@ -10048,11 +10048,7 @@ def abs(self: FrameOrSeries) -> FrameOrSeries:
         2    6   30  -30
         3    7   40  -50
         """
-        # error: Argument 1 to "__call__" of "ufunc" has incompatible type
-        # "FrameOrSeries"; expected "Union[Union[int, float, complex, str, bytes,
-        # generic], Sequence[Union[int, float, complex, str, bytes, generic]],
-        # Sequence[Sequence[Any]], _SupportsArray]"
-        return np.abs(self)  # type: ignore[arg-type]
+        return np.abs(self)
 
     @final
     def describe(

pandas/core/indexes/range.py

@@ -111,12 +111,7 @@ def __new__(
         name: Hashable = None,
     ) -> RangeIndex:
 
-        # error: Argument 1 to "_validate_dtype" of "NumericIndex" has incompatible type
-        # "Union[ExtensionDtype, str, dtype[Any], Type[str], Type[float], Type[int],
-        # Type[complex], Type[bool], Type[object], None]"; expected
-        # "Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float],
-        # Type[int], Type[complex], Type[bool], Type[object]]"
-        cls._validate_dtype(dtype)  # type: ignore[arg-type]
+        cls._validate_dtype(dtype)
         name = maybe_extract_name(name, start, cls)
 
         # RangeIndex
@@ -160,12 +155,7 @@ def from_range(
                 f"range, {repr(data)} was passed"
             )
 
-        # error: Argument 1 to "_validate_dtype" of "NumericIndex" has incompatible type
-        # "Union[ExtensionDtype, str, dtype[Any], Type[str], Type[float], Type[int],
-        # Type[complex], Type[bool], Type[object], None]"; expected
-        # "Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float],
-        # Type[int], Type[complex], Type[bool], Type[object]]"
-        cls._validate_dtype(dtype)  # type: ignore[arg-type]
+        cls._validate_dtype(dtype)
         return cls._simple_new(data, name=name)
 
     @classmethod

pandas/io/json/_json.py

@@ -933,10 +933,9 @@ def _try_convert_data(self, name, data, use_dtypes=True, convert_dates=True):
                 )
                 if dtype is not None:
                     try:
-                        # error: Argument 1 to "dtype" has incompatible type
-                        # "Union[ExtensionDtype, str, dtype[Any], Type[object]]";
-                        # expected "Type[Any]"
-                        dtype = np.dtype(dtype)  # type: ignore[arg-type]
+                        # error: No overload variant of "dtype" matches argument type
+                        # "object"
+                        dtype = np.dtype(dtype)  # type: ignore[call-overload]
                         return data.astype(dtype), True
                     except (TypeError, ValueError):
                         return data, False

pandas/io/sql.py

@@ -1159,7 +1159,9 @@ def _sqlalchemy_type(self, col):
         if is_dict_like(dtype):
             dtype = cast(dict, dtype)
             if col.name in dtype:
-                return dtype[col.name]
+                # error: TypedDict key must be a string literal; expected one of
+                # ('names', 'formats', 'offsets', 'titles', 'itemsize', ...)
+                return dtype[col.name]  # type: ignore[misc]
 
         # Infer type of column, while ignoring missing values.
         # Needed for inserting typed data containing NULLs, GH 8778.
@@ -1841,7 +1843,9 @@ def _sql_type_name(self, col):
         if is_dict_like(dtype):
             dtype = cast(dict, dtype)
             if col.name in dtype:
-                return dtype[col.name]
+                # error: TypedDict key must be a string literal; expected one of
+                # ('names', 'formats', 'offsets', 'titles', 'itemsize', ...)
+                return dtype[col.name]  # type: ignore[misc]
 
         # Infer type of column, while ignoring missing values.
         # Needed for inserting typed data containing NULLs, GH 8778.