[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

asv_bench/benchmarks/frame_methods.py

@@ -517,7 +517,7 @@ def setup(self):
         self.df = DataFrame(np.random.randn(1000, 100))
 
         self.s = Series(np.arange(1028.0))
-        self.df2 = DataFrame({i: self.s for i in range(1028)})
+        self.df2 = DataFrame(dict.fromkeys(range(1028), self.s))
         self.df3 = DataFrame(np.random.randn(1000, 3), columns=list("ABC"))
 
     def time_apply_user_func(self):

pandas/_config/config.py

@@ -775,7 +775,7 @@ def inner(key: str, *args, **kwds):
             pkey = f"{prefix}.{key}"
             return func(pkey, *args, **kwds)
 
-        return cast(F, inner)
+        return cast("F", inner)
 
     _register_option = register_option
     _get_option = get_option

pandas/_config/localization.py

@@ -156,7 +156,9 @@ def get_locales(
         out_locales = []
         for x in split_raw_locales:
             try:
-                out_locales.append(str(x, encoding=cast(str, options.display.encoding)))
+                out_locales.append(
+                    str(x, encoding=cast("str", options.display.encoding))
+                )
             except UnicodeError:
                 # 'locale -a' is used to populated 'raw_locales' and on
                 # Redhat 7 Linux (and maybe others) prints locale names

pandas/_testing/_warnings.py

@@ -121,7 +121,7 @@ class for all warnings. To raise multiple types of exceptions,
                         )
                 else:
                     expected_warning = cast(
-                        Union[type[Warning], tuple[type[Warning], ...]],
+                        "Union[type[Warning], tuple[type[Warning], ...]]",
                         expected_warning,
                     )
                     match = (
@@ -241,7 +241,7 @@ def _is_unexpected_warning(
     """Check if the actual warning issued is unexpected."""
     if actual_warning and not expected_warning:
         return True
-    expected_warning = cast(type[Warning], expected_warning)
+    expected_warning = cast("type[Warning]", expected_warning)
     return bool(not issubclass(actual_warning.category, expected_warning))
 
 

pandas/_testing/asserters.py

@@ -283,7 +283,7 @@ def _check_types(left, right, obj: str = "Index") -> None:
 
     # MultiIndex special comparison for little-friendly error messages
     if isinstance(left, MultiIndex):
-        right = cast(MultiIndex, right)
+        right = cast("MultiIndex", right)
 
         for level in range(left.nlevels):
             lobj = f"{obj} level [{level}]"
@@ -776,11 +776,11 @@ def assert_extension_array_equal(
         # GH 52449
         if not check_dtype and left.dtype.kind in "mM":
             if not isinstance(left.dtype, np.dtype):
-                l_unit = cast(DatetimeTZDtype, left.dtype).unit
+                l_unit = cast("DatetimeTZDtype", left.dtype).unit
             else:
                 l_unit = np.datetime_data(left.dtype)[0]
             if not isinstance(right.dtype, np.dtype):
-                r_unit = cast(DatetimeTZDtype, right.dtype).unit
+                r_unit = cast("DatetimeTZDtype", right.dtype).unit
             else:
                 r_unit = np.datetime_data(right.dtype)[0]
             if (

pandas/compat/numpy/function.py

@@ -169,7 +169,7 @@ def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs)
         ascending = True
 
     validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
-    ascending = cast(bool, ascending)
+    ascending = cast("bool", ascending)
     return ascending
 
 

pandas/core/algorithms.py

@@ -172,12 +172,12 @@ def _ensure_data(values: ArrayLike) -> np.ndarray:
         return np.asarray(values)
 
     elif is_complex_dtype(values.dtype):
-        return cast(np.ndarray, values)
+        return cast("np.ndarray", values)
 
     # datetimelike
     elif needs_i8_conversion(values.dtype):
         npvalues = values.view("i8")
-        npvalues = cast(np.ndarray, npvalues)
+        npvalues = cast("np.ndarray", npvalues)
         return npvalues
 
     # we have failed, return object
@@ -1289,9 +1289,9 @@ def searchsorted(
 
         if is_integer(value):
             # We know that value is int
-            value = cast(int, dtype.type(value))
+            value = cast("int", dtype.type(value))
         else:
-            value = pd_array(cast(ArrayLike, value), dtype=dtype)
+            value = pd_array(cast("ArrayLike", value), dtype=dtype)
     else:
         # E.g. if `arr` is an array with dtype='datetime64[ns]'
         # and `value` is a pd.Timestamp, we may need to convert value

pandas/core/apply.py

@@ -322,19 +322,19 @@ def transform(self) -> DataFrame | Series:
             return obj.T.transform(func, 0, *args, **kwargs).T
 
         if is_list_like(func) and not is_dict_like(func):
-            func = cast(list[AggFuncTypeBase], func)
+            func = cast("list[AggFuncTypeBase]", func)
             # Convert func equivalent dict
             if is_series:
                 func = {com.get_callable_name(v) or v: v for v in func}
             else:
-                func = {col: func for col in obj}
+                func = dict.fromkeys(obj, func)
 
         if is_dict_like(func):
-            func = cast(AggFuncTypeDict, func)
+            func = cast("AggFuncTypeDict", func)
             return self.transform_dict_like(func)
 
         # func is either str or callable
-        func = cast(AggFuncTypeBase, func)
+        func = cast("AggFuncTypeBase", func)
         try:
             result = self.transform_str_or_callable(func)
         except TypeError:
@@ -434,7 +434,7 @@ def compute_list_like(
             Data for result. When aggregating with a Series, this can contain any
             Python objects.
         """
-        func = cast(list[AggFuncTypeBase], self.func)
+        func = cast("list[AggFuncTypeBase]", self.func)
         obj = self.obj
 
         results = []
@@ -541,7 +541,7 @@ def compute_dict_like(
 
         obj = self.obj
         is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
-        func = cast(AggFuncTypeDict, self.func)
+        func = cast("AggFuncTypeDict", self.func)
         func = self.normalize_dictlike_arg(op_name, selected_obj, func)
 
         is_non_unique_col = (
@@ -666,7 +666,7 @@ def apply_str(self) -> DataFrame | Series:
         result: Series or DataFrame
         """
         # Caller is responsible for checking isinstance(self.f, str)
-        func = cast(str, self.func)
+        func = cast("str", self.func)
 
         obj = self.obj
 
@@ -1262,7 +1262,7 @@ def numba_func(values, col_names, df_index, *args):
         return numba_func
 
     def apply_with_numba(self) -> dict[int, Any]:
-        func = cast(Callable, self.func)
+        func = cast("Callable", self.func)
         args, kwargs = prepare_function_arguments(
             func, self.args, self.kwargs, num_required_args=1
         )
@@ -1404,7 +1404,7 @@ def numba_func(values, col_names_index, index, *args):
         return numba_func
 
     def apply_with_numba(self) -> dict[int, Any]:
-        func = cast(Callable, self.func)
+        func = cast("Callable", self.func)
         args, kwargs = prepare_function_arguments(
             func, self.args, self.kwargs, num_required_args=1
         )
@@ -1551,7 +1551,7 @@ def apply_compat(self):
 
     def apply_standard(self) -> DataFrame | Series:
         # caller is responsible for ensuring that f is Callable
-        func = cast(Callable, self.func)
+        func = cast("Callable", self.func)
         obj = self.obj
 
         if isinstance(func, np.ufunc):

pandas/core/arrays/_mixins.py

@@ -85,7 +85,7 @@ def method(self, *args, **kwargs):
         order = "F" if flags.f_contiguous else "C"
         return result.reshape(self.shape, order=order)
 
-    return cast(F, method)
+    return cast("F", method)
 
 
 # error: Definition of "delete/ravel/T/repeat/copy" in base class "NDArrayBacked"

pandas/core/arrays/arrow/accessors.py

@@ -445,7 +445,7 @@ def get_name(
                 while level_name_or_index:
                     # we need the cast, otherwise mypy complains about
                     # getting ints, bytes, or str here, which isn't possible.
-                    level_name_or_index = cast(list, level_name_or_index)
+                    level_name_or_index = cast("list", level_name_or_index)
                     name_or_index = level_name_or_index.pop()
                     name = get_name(name_or_index, selected)
                     selected = selected.type.field(selected.type.get_field_index(name))