CLN: fix C408 #38138 (#38320)

Co-authored-by: VirosaLi <2EkF8qUgpNkj>

Author: fangchenli

pandas/core/arrays/base.py

@@ -952,7 +952,7 @@ def factorize(self, na_sentinel: int = -1) -> Tuple[np.ndarray, ExtensionArray]:
     @Substitution(klass="ExtensionArray")
     @Appender(_extension_array_shared_docs["repeat"])
     def repeat(self, repeats, axis=None):
-        nv.validate_repeat(tuple(), {"axis": axis})
+        nv.validate_repeat((), {"axis": axis})
         ind = np.arange(len(self)).repeat(repeats)
         return self.take(ind)
 

pandas/core/computation/expressions.py

@@ -22,7 +22,7 @@
     import numexpr as ne
 
 _TEST_MODE = None
-_TEST_RESULT: List[bool] = list()
+_TEST_RESULT: List[bool] = []
 USE_NUMEXPR = NUMEXPR_INSTALLED
 _evaluate = None
 _where = None

pandas/core/dtypes/dtypes.py

@@ -53,7 +53,7 @@ class PandasExtensionDtype(ExtensionDtype):
     subdtype = None
     str: str_type
     num = 100
-    shape: Tuple[int, ...] = tuple()
+    shape: Tuple[int, ...] = ()
     itemsize = 8
     base = None
     isbuiltin = 0

pandas/core/generic.py

@@ -3569,7 +3569,7 @@ class  max_speed
                 stacklevel=2,
             )
 
-        nv.validate_take(tuple(), kwargs)
+        nv.validate_take((), kwargs)
 
         self._consolidate_inplace()
 
@@ -10557,7 +10557,7 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
     def _logical_func(
         self, name: str, func, axis=0, bool_only=None, skipna=True, level=None, **kwargs
     ):
-        nv.validate_logical_func(tuple(), kwargs, fname=name)
+        nv.validate_logical_func((), kwargs, fname=name)
         if level is not None:
             if bool_only is not None:
                 raise NotImplementedError(
@@ -10644,7 +10644,7 @@ def _stat_function_ddof(
         numeric_only=None,
         **kwargs,
     ):
-        nv.validate_stat_ddof_func(tuple(), kwargs, fname=name)
+        nv.validate_stat_ddof_func((), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:
@@ -10690,9 +10690,9 @@ def _stat_function(
         **kwargs,
     ):
         if name == "median":
-            nv.validate_median(tuple(), kwargs)
+            nv.validate_median((), kwargs)
         else:
-            nv.validate_stat_func(tuple(), kwargs, fname=name)
+            nv.validate_stat_func((), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:
@@ -10748,11 +10748,11 @@ def _min_count_stat_function(
         **kwargs,
     ):
         if name == "sum":
-            nv.validate_sum(tuple(), kwargs)
+            nv.validate_sum((), kwargs)
         elif name == "prod":
-            nv.validate_prod(tuple(), kwargs)
+            nv.validate_prod((), kwargs)
         else:
-            nv.validate_stat_func(tuple(), kwargs, fname=name)
+            nv.validate_stat_func((), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:

pandas/core/indexes/base.py

@@ -742,7 +742,7 @@ def astype(self, dtype, copy=True):
     @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
     def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
         if kwargs:
-            nv.validate_take(tuple(), kwargs)
+            nv.validate_take((), kwargs)
         indices = ensure_platform_int(indices)
         allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
 
@@ -817,7 +817,7 @@ def _maybe_disallow_fill(self, allow_fill: bool, fill_value, indices) -> bool:
     @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
     def repeat(self, repeats, axis=None):
         repeats = ensure_platform_int(repeats)
-        nv.validate_repeat(tuple(), {"axis": axis})
+        nv.validate_repeat((), {"axis": axis})
         return self._shallow_copy(self._values.repeat(repeats))
 
     # --------------------------------------------------------------------

pandas/core/indexes/interval.py

@@ -65,17 +65,17 @@
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 
 _index_doc_kwargs.update(
-    dict(
-        klass="IntervalIndex",
-        qualname="IntervalIndex",
-        target_klass="IntervalIndex or list of Intervals",
-        name=textwrap.dedent(
+    {
+        "klass": "IntervalIndex",
+        "qualname": "IntervalIndex",
+        "target_klass": "IntervalIndex or list of Intervals",
+        "name": textwrap.dedent(
             """\
          name : object, optional
               Name to be stored in the index.
          """
         ),
-    )
+    }
 )
 
 
@@ -141,14 +141,14 @@ def wrapped(self, other, sort=False):
 
 @Appender(
     _interval_shared_docs["class"]
-    % dict(
-        klass="IntervalIndex",
-        summary="Immutable index of intervals that are closed on the same side.",
-        name=_index_doc_kwargs["name"],
-        versionadded="0.20.0",
-        extra_attributes="is_overlapping\nvalues\n",
-        extra_methods="",
-        examples=textwrap.dedent(
+    % {
+        "klass": "IntervalIndex",
+        "summary": "Immutable index of intervals that are closed on the same side.",
+        "name": _index_doc_kwargs["name"],
+        "versionadded": "0.20.0",
+        "extra_attributes": "is_overlapping\nvalues\n",
+        "extra_methods": "",
+        "examples": textwrap.dedent(
             """\
     Examples
     --------
@@ -168,7 +168,7 @@ def wrapped(self, other, sort=False):
     mentioned constructor methods.
     """
         ),
-    )
+    }
 )
 @inherit_names(["set_closed", "to_tuples"], IntervalArray, wrap=True)
 @inherit_names(["__array__", "overlaps", "contains"], IntervalArray)
@@ -234,9 +234,9 @@ def _simple_new(cls, array: IntervalArray, name: Label = None):
     @classmethod
     @Appender(
         _interval_shared_docs["from_breaks"]
-        % dict(
-            klass="IntervalIndex",
-            examples=textwrap.dedent(
+        % {
+            "klass": "IntervalIndex",
+            "examples": textwrap.dedent(
                 """\
         Examples
         --------
@@ -246,7 +246,7 @@ def _simple_new(cls, array: IntervalArray, name: Label = None):
                       dtype='interval[int64]')
         """
             ),
-        )
+        }
     )
     def from_breaks(
         cls, breaks, closed: str = "right", name=None, copy: bool = False, dtype=None
@@ -260,9 +260,9 @@ def from_breaks(
     @classmethod
     @Appender(
         _interval_shared_docs["from_arrays"]
-        % dict(
-            klass="IntervalIndex",
-            examples=textwrap.dedent(
+        % {
+            "klass": "IntervalIndex",
+            "examples": textwrap.dedent(
                 """\
         Examples
         --------
@@ -272,7 +272,7 @@ def from_breaks(
                       dtype='interval[int64]')
         """
             ),
-        )
+        }
     )
     def from_arrays(
         cls,
@@ -292,9 +292,9 @@ def from_arrays(
     @classmethod
     @Appender(
         _interval_shared_docs["from_tuples"]
-        % dict(
-            klass="IntervalIndex",
-            examples=textwrap.dedent(
+        % {
+            "klass": "IntervalIndex",
+            "examples": textwrap.dedent(
                 """\
         Examples
         --------
@@ -304,7 +304,7 @@ def from_arrays(
                        dtype='interval[int64]')
         """
             ),
-        )
+        }
     )
     def from_tuples(
         cls, data, closed: str = "right", name=None, copy: bool = False, dtype=None
@@ -360,7 +360,7 @@ def __array_wrap__(self, result, context=None):
         return result
 
     def __reduce__(self):
-        d = dict(left=self.left, right=self.right)
+        d = {"left": self.left, "right": self.right}
         d.update(self._get_attributes_dict())
         return _new_IntervalIndex, (type(self), d), None
 

pandas/core/indexes/multi.py

@@ -74,7 +74,7 @@
 
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update(
-    dict(klass="MultiIndex", target_klass="MultiIndex or list of tuples")
+    {"klass": "MultiIndex", "target_klass": "MultiIndex or list of tuples"}
 )
 
 
@@ -2007,12 +2007,12 @@ def remove_unused_levels(self):
 
     def __reduce__(self):
         """Necessary for making this object picklable"""
-        d = dict(
-            levels=list(self.levels),
-            codes=list(self.codes),
-            sortorder=self.sortorder,
-            names=list(self.names),
-        )
+        d = {
+            "levels": list(self.levels),
+            "codes": list(self.codes),
+            "sortorder": self.sortorder,
+            "names": list(self.names),
+        }
         return ibase._new_Index, (type(self), d), None
 
     # --------------------------------------------------------------------
@@ -2052,7 +2052,7 @@ def __getitem__(self, key):
 
     @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 = ensure_platform_int(indices)
 
         # only fill if we are passing a non-None fill_value
@@ -2116,7 +2116,7 @@ def argsort(self, *args, **kwargs) -> np.ndarray:
 
     @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
     def repeat(self, repeats, axis=None):
-        nv.validate_repeat(tuple(), dict(axis=axis))
+        nv.validate_repeat((), {"axis": axis})
         repeats = ensure_platform_int(repeats)
         return MultiIndex(
             levels=self.levels,
@@ -3353,7 +3353,7 @@ def _reorder_indexer(
                 return indexer
 
         n = len(self)
-        keys: Tuple[np.ndarray, ...] = tuple()
+        keys: Tuple[np.ndarray, ...] = ()
         # For each level of the sequence in seq, map the level codes with the
         # order they appears in a list-like sequence
         # This mapping is then use to reorder the indexer

pandas/core/series.py

@@ -374,7 +374,7 @@ def _init_dict(self, data, index=None, dtype=None):
             values = na_value_for_dtype(dtype)
             keys = index
         else:
-            keys, values = tuple(), []
+            keys, values = (), []
 
         # Input is now list-like, so rely on "standard" construction:
 
@@ -775,7 +775,7 @@ def take(self, indices, axis=0, is_copy=None, **kwargs) -> "Series":
                 FutureWarning,
                 stacklevel=2,
             )
-        nv.validate_take(tuple(), kwargs)
+        nv.validate_take((), kwargs)
 
         indices = ensure_platform_int(indices)
         new_index = self.index.take(indices)
@@ -1114,7 +1114,7 @@ def repeat(self, repeats, axis=None) -> "Series":
         2    c
         dtype: object
         """
-        nv.validate_repeat(tuple(), {"axis": axis})
+        nv.validate_repeat((), {"axis": axis})
         new_index = self.index.repeat(repeats)
         new_values = self._values.repeat(repeats)
         return self._constructor(new_values, index=new_index).__finalize__(

pandas/tests/dtypes/test_inference.py

@@ -66,7 +66,7 @@ def coerce(request):
     ([1], True, "list"),
     ([], True, "list-empty"),
     ((1,), True, "tuple"),
-    (tuple(), True, "tuple-empty"),
+    ((), True, "tuple-empty"),
     ({"a": 1}, True, "dict"),
     ({}, True, "dict-empty"),
     ({"a", 1}, "set", "set"),
@@ -161,7 +161,7 @@ class DtypeList(list):
     assert inference.is_array_like(DtypeList())
 
     assert not inference.is_array_like([1, 2, 3])
-    assert not inference.is_array_like(tuple())
+    assert not inference.is_array_like(())
     assert not inference.is_array_like("foo")
     assert not inference.is_array_like(123)
 
@@ -326,7 +326,7 @@ class UnhashableClass2:
         def __hash__(self):
             raise TypeError("Not hashable")
 
-    hashable = (1, 3.14, np.float64(3.14), "a", tuple(), (1,), HashableClass())
+    hashable = (1, 3.14, np.float64(3.14), "a", (), (1,), HashableClass())
     not_hashable = ([], UnhashableClass1())
     abc_hashable_not_really_hashable = (([],), UnhashableClass2())
 

pandas/tests/frame/methods/test_to_records.py

@@ -238,7 +238,7 @@ def test_to_records_with_categorical(self):
             ),
             # Invalid dype values.
             (
-                {"index": False, "column_dtypes": list()},
+                {"index": False, "column_dtypes": []},
                 (ValueError, "Invalid dtype \\[\\] specified for column A"),
             ),
             (

pandas/tests/groupby/transform/test_transform.py

@@ -162,7 +162,7 @@ def test_transform_axis_1(transformation_func):
     # GH 36308
     if transformation_func == "tshift":
         pytest.xfail("tshift is deprecated")
-    args = ("ffill",) if transformation_func == "fillna" else tuple()
+    args = ("ffill",) if transformation_func == "fillna" else ()
 
     df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}, index=["x", "y"])
     result = df.groupby([0, 0, 1], axis=1).transform(transformation_func, *args)
@@ -803,7 +803,7 @@ def test_group_fill_methods(
         keys = ["a", "b"] * len(vals)
 
         def interweave(list_obj):
-            temp = list()
+            temp = []
             for x in list_obj:
                 temp.extend([x, x])
 

pandas/tests/io/formats/test_format.py

@@ -2908,11 +2908,11 @@ def test_too_long(self):
         with pd.option_context("display.precision", 4):
             # need both a number > 1e6 and something that normally formats to
             # having length > display.precision + 6
-            df = DataFrame(dict(x=[12345.6789]))
+            df = DataFrame({"x": [12345.6789]})
             assert str(df) == "            x\n0  12345.6789"
-            df = DataFrame(dict(x=[2e6]))
+            df = DataFrame({"x": [2e6]})
             assert str(df) == "           x\n0  2000000.0"
-            df = DataFrame(dict(x=[12345.6789, 2e6]))
+            df = DataFrame({"x": [12345.6789, 2e6]})
             assert str(df) == "            x\n0  1.2346e+04\n1  2.0000e+06"