TYP: fix type-ignores in core (#40423)

Author: jbrockmendel

pandas/core/algorithms.py

@@ -1867,7 +1867,7 @@ def _sort_mixed(values):
     return np.concatenate([nums, np.asarray(strs, dtype=object)])
 
 
-def _sort_tuples(values: np.ndarray):
+def _sort_tuples(values: np.ndarray) -> np.ndarray:
     """
     Convert array of tuples (1d) to array or array (2d).
     We need to keep the columns separately as they contain different types and

pandas/core/arraylike.py

@@ -5,10 +5,7 @@
     ExtensionArray
 """
 import operator
-from typing import (
-    Any,
-    Callable,
-)
+from typing import Any
 import warnings
 
 import numpy as np
@@ -172,7 +169,7 @@ def _is_aligned(frame, other):
         return frame.columns.equals(other.index)
 
 
-def _maybe_fallback(ufunc: Callable, method: str, *inputs: Any, **kwargs: Any):
+def _maybe_fallback(ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
     """
     In the future DataFrame, inputs to ufuncs will be aligned before applying
     the ufunc, but for now we ignore the index but raise a warning if behaviour

pandas/core/arrays/boolean.py

@@ -331,7 +331,7 @@ def map_string(s):
 
     _HANDLED_TYPES = (np.ndarray, numbers.Number, bool, np.bool_)
 
-    def __array_ufunc__(self, ufunc, method: str, *inputs, **kwargs):
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # For BooleanArray inputs, we apply the ufunc to ._data
         # and mask the result.
         if method == "reduce":

pandas/core/arrays/categorical.py

@@ -1388,7 +1388,7 @@ def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
         # ndarray.
         return np.asarray(ret)
 
-    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # for binary ops, use our custom dunder methods
         result = ops.maybe_dispatch_ufunc_to_dunder_op(
             self, ufunc, method, *inputs, **kwargs
@@ -2429,7 +2429,7 @@ def replace(self, to_replace, value, inplace: bool = False):
 
     # ------------------------------------------------------------------------
     # String methods interface
-    def _str_map(self, f, na_value=np.nan, dtype=np.dtype(object)):
+    def _str_map(self, f, na_value=np.nan, dtype=np.dtype("object")):
         # Optimization to apply the callable `f` to the categories once
         # and rebuild the result by `take`ing from the result with the codes.
         # Returns the same type as the object-dtype implementation though.

pandas/core/arrays/numeric.py

@@ -152,7 +152,7 @@ def _arith_method(self, other, op):
 
     _HANDLED_TYPES = (np.ndarray, numbers.Number)
 
-    def __array_ufunc__(self, ufunc, method: str, *inputs, **kwargs):
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # For NumericArray inputs, we apply the ufunc to ._data
         # and mask the result.
         if method == "reduce":

pandas/core/arrays/numpy_.py

@@ -137,7 +137,7 @@ def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
 
     _HANDLED_TYPES = (np.ndarray, numbers.Number)
 
-    def __array_ufunc__(self, ufunc, method: str, *inputs, **kwargs):
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         # Lightly modified version of
         # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
         # The primary modification is not boxing scalar return values

pandas/core/arrays/sparse/array.py

@@ -1396,7 +1396,7 @@ def mean(self, axis=0, *args, **kwargs):
 
     _HANDLED_TYPES = (np.ndarray, numbers.Number)
 
-    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         out = kwargs.get("out", ())
 
         for x in inputs + out:

pandas/core/base.py

@@ -23,6 +23,7 @@
     Dtype,
     DtypeObj,
     IndexLabel,
+    Shape,
 )
 from pandas.compat import PYPY
 from pandas.compat.numpy import function as nv
@@ -389,7 +390,7 @@ def transpose(self: _T, *args, **kwargs) -> _T:
     )
 
     @property
-    def shape(self):
+    def shape(self) -> Shape:
         """
         Return a tuple of the shape of the underlying data.
         """
@@ -511,7 +512,7 @@ def to_numpy(
         copy: bool = False,
         na_value=lib.no_default,
         **kwargs,
-    ):
+    ) -> np.ndarray:
         """
         A NumPy ndarray representing the values in this Series or Index.
 
@@ -852,7 +853,7 @@ def __iter__(self):
             return map(self._values.item, range(self._values.size))
 
     @cache_readonly
-    def hasnans(self):
+    def hasnans(self) -> bool:
         """
         Return if I have any nans; enables various perf speedups.
         """

pandas/core/dtypes/cast.py

@@ -689,15 +689,14 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
                 if fv.tz is None:
                     return dtype, fv.asm8
 
-        # error: Value of type variable "_DTypeScalar" of "dtype" cannot be "object"
-        return np.dtype(object), fill_value  # type: ignore[type-var]
+        return np.dtype("object"), fill_value
 
     elif issubclass(dtype.type, np.timedelta64):
         inferred, fv = infer_dtype_from_scalar(fill_value, pandas_dtype=True)
         if inferred == dtype:
             return dtype, fv
-        # error: Value of type variable "_DTypeScalar" of "dtype" cannot be "object"
-        return np.dtype(object), fill_value  # type: ignore[type-var]
+
+        return np.dtype("object"), fill_value
 
     elif is_float(fill_value):
         if issubclass(dtype.type, np.bool_):

pandas/core/dtypes/missing.py

@@ -648,8 +648,7 @@ def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
         # Numeric
         return obj is not NaT and not isinstance(obj, (np.datetime64, np.timedelta64))
 
-    # error: Value of type variable "_DTypeScalar" of "dtype" cannot be "object"
-    elif dtype == np.dtype(object):  # type: ignore[type-var]
+    elif dtype == np.dtype("object"):
         # This is needed for Categorical, but is kind of weird
         return True