CLN: fix flake8 C408 part 2 (#38116)

Author: fangchenli

pandas/_testing.py

@@ -2167,15 +2167,15 @@ def makeCustomIndex(
         names = [names]
 
     # specific 1D index type requested?
-    idx_func = dict(
-        i=makeIntIndex,
-        f=makeFloatIndex,
-        s=makeStringIndex,
-        u=makeUnicodeIndex,
-        dt=makeDateIndex,
-        td=makeTimedeltaIndex,
-        p=makePeriodIndex,
-    ).get(idx_type)
+    idx_func = {
+        "i": makeIntIndex,
+        "f": makeFloatIndex,
+        "s": makeStringIndex,
+        "u": makeUnicodeIndex,
+        "dt": makeDateIndex,
+        "td": makeTimedeltaIndex,
+        "p": makePeriodIndex,
+    }.get(idx_type)
     if idx_func:
         # pandas\_testing.py:2120: error: Cannot call function of unknown type
         idx = idx_func(nentries)  # type: ignore[operator]

pandas/compat/numpy/function.py

@@ -71,7 +71,7 @@ def __call__(
                 raise ValueError(f"invalid validation method '{method}'")
 
 
-ARGMINMAX_DEFAULTS = dict(out=None)
+ARGMINMAX_DEFAULTS = {"out": None}
 validate_argmin = CompatValidator(
     ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
 )
@@ -151,7 +151,7 @@ def validate_argsort_with_ascending(ascending, args, kwargs):
     return ascending
 
 
-CLIP_DEFAULTS: Dict[str, Any] = dict(out=None)
+CLIP_DEFAULTS: Dict[str, Any] = {"out": None}
 validate_clip = CompatValidator(
     CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
 )
@@ -208,28 +208,28 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
 )
 
-LOGICAL_FUNC_DEFAULTS = dict(out=None, keepdims=False)
+LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
 validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
 
-MINMAX_DEFAULTS = dict(axis=None, out=None, keepdims=False)
+MINMAX_DEFAULTS = {"axis": None, "out": None, "keepdims": False}
 validate_min = CompatValidator(
     MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
 )
 validate_max = CompatValidator(
     MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
 )
 
-RESHAPE_DEFAULTS: Dict[str, str] = dict(order="C")
+RESHAPE_DEFAULTS: Dict[str, str] = {"order": "C"}
 validate_reshape = CompatValidator(
     RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
 )
 
-REPEAT_DEFAULTS: Dict[str, Any] = dict(axis=None)
+REPEAT_DEFAULTS: Dict[str, Any] = {"axis": None}
 validate_repeat = CompatValidator(
     REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
 )
 
-ROUND_DEFAULTS: Dict[str, Any] = dict(out=None)
+ROUND_DEFAULTS: Dict[str, Any] = {"out": None}
 validate_round = CompatValidator(
     ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
 )
@@ -300,7 +300,7 @@ def validate_take_with_convert(convert, args, kwargs):
     return convert
 
 
-TRANSPOSE_DEFAULTS = dict(axes=None)
+TRANSPOSE_DEFAULTS = {"axes": None}
 validate_transpose = CompatValidator(
     TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
 )

pandas/core/arrays/_mixins.py

@@ -162,7 +162,7 @@ def repeat(
         --------
         numpy.ndarray.repeat
         """
-        nv.validate_repeat(tuple(), dict(axis=axis))
+        nv.validate_repeat((), {"axis": axis})
         new_data = self._ndarray.repeat(repeats, axis=axis)
         return self._from_backing_data(new_data)
 

pandas/core/arrays/sparse/array.py

@@ -58,7 +58,7 @@
 
 SparseArrayT = TypeVar("SparseArrayT", bound="SparseArray")
 
-_sparray_doc_kwargs = dict(klass="SparseArray")
+_sparray_doc_kwargs = {"klass": "SparseArray"}
 
 
 def _get_fill(arr: "SparseArray") -> np.ndarray:

pandas/core/arrays/timedeltas.py

@@ -373,7 +373,7 @@ def sum(
         min_count: int = 0,
     ):
         nv.validate_sum(
-            (), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims, "initial": initial}
         )
 
         result = nanops.nansum(
@@ -391,7 +391,7 @@ def std(
         skipna: bool = True,
     ):
         nv.validate_stat_ddof_func(
-            (), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
         )
 
         result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)

pandas/core/base.py

@@ -46,13 +46,13 @@
 if TYPE_CHECKING:
     from pandas import Categorical
 
-_shared_docs: Dict[str, str] = dict()
-_indexops_doc_kwargs = dict(
-    klass="IndexOpsMixin",
-    inplace="",
-    unique="IndexOpsMixin",
-    duplicated="IndexOpsMixin",
-)
+_shared_docs: Dict[str, str] = {}
+_indexops_doc_kwargs = {
+    "klass": "IndexOpsMixin",
+    "inplace": "",
+    "unique": "IndexOpsMixin",
+    "duplicated": "IndexOpsMixin",
+}
 
 _T = TypeVar("_T", bound="IndexOpsMixin")
 

pandas/core/dtypes/generic.py

@@ -18,9 +18,9 @@ def create_pandas_abc_type(name, attr, comp):
     def _check(cls, inst) -> bool:
         return getattr(inst, attr, "_typ") in comp
 
-    dct = dict(__instancecheck__=_check, __subclasscheck__=_check)
+    dct = {"__instancecheck__": _check, "__subclasscheck__": _check}
     meta = type("ABCBase", (type,), dct)
-    return meta(name, tuple(), dct)
+    return meta(name, (), dct)
 
 
 ABCInt64Index = create_pandas_abc_type("ABCInt64Index", "_typ", ("int64index",))

pandas/core/groupby/groupby.py

@@ -85,8 +85,8 @@ class providing the base-class of operations.
             to each row or column of a DataFrame.
 """
 
-_apply_docs = dict(
-    template="""
+_apply_docs = {
+    "template": """
     Apply function `func` group-wise and combine the results together.
 
     The function passed to `apply` must take a {input} as its first
@@ -123,7 +123,7 @@ class providing the base-class of operations.
     Series.apply : Apply a function to a Series.
     DataFrame.apply : Apply a function to each row or column of a DataFrame.
     """,
-    dataframe_examples="""
+    "dataframe_examples": """
     >>> df = pd.DataFrame({'A': 'a a b'.split(),
                            'B': [1,2,3],
                            'C': [4,6, 5]})
@@ -163,7 +163,7 @@ class providing the base-class of operations.
     b    2
     dtype: int64
     """,
-    series_examples="""
+    "series_examples": """
     >>> s = pd.Series([0, 1, 2], index='a a b'.split())
     >>> g = s.groupby(s.index)
 
@@ -202,7 +202,7 @@ class providing the base-class of operations.
     --------
     {examples}
     """,
-)
+}
 
 _groupby_agg_method_template = """
 Compute {fname} of group values.

pandas/core/indexes/category.py

@@ -27,7 +27,7 @@
 import pandas.core.missing as missing
 
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
-_index_doc_kwargs.update(dict(target_klass="CategoricalIndex"))
+_index_doc_kwargs.update({"target_klass": "CategoricalIndex"})
 
 
 @inherit_names(

pandas/core/indexes/datetimelike.py

@@ -200,7 +200,7 @@ def __contains__(self, key: Any) -> bool:
 
     @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
     def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
-        nv.validate_take(tuple(), kwargs)
+        nv.validate_take((), kwargs)
         indices = np.asarray(indices, dtype=np.intp)
 
         maybe_slice = lib.maybe_indices_to_slice(indices, len(self))