CLN: remove unnecessary trailing commas on issues #35925

pandas/tests/arrays/categorical/test_replace.py

@@ -43,9 +43,5 @@ def test_replace(to_replace, value, expected, flip_categories):
         # the replace call loses categorical dtype
         expected = pd.Series(np.asarray(expected))
 
-    tm.assert_series_equal(
-        expected, result, check_category_order=False,
-    )
-    tm.assert_series_equal(
-        expected, s, check_category_order=False,
-    )
+    tm.assert_series_equal(expected, result, check_category_order=False)
+    tm.assert_series_equal(expected, s, check_category_order=False)

pandas/tests/arrays/test_array.py

@@ -35,7 +35,7 @@
             np.dtype("float32"),
             PandasArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))),
         ),
-        (np.array([1, 2], dtype="int64"), None, IntegerArray._from_sequence([1, 2]),),
+        (np.array([1, 2], dtype="int64"), None, IntegerArray._from_sequence([1, 2])),
         # String alias passes through to NumPy
         ([1, 2], "float32", PandasArray(np.array([1, 2], dtype="float32"))),
         # Period alias
@@ -120,10 +120,10 @@
         (pd.Series([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
         # String
         (["a", None], "string", StringArray._from_sequence(["a", None])),
-        (["a", None], pd.StringDtype(), StringArray._from_sequence(["a", None]),),
+        (["a", None], pd.StringDtype(), StringArray._from_sequence(["a", None])),
         # Boolean
         ([True, None], "boolean", BooleanArray._from_sequence([True, None])),
-        ([True, None], pd.BooleanDtype(), BooleanArray._from_sequence([True, None]),),
+        ([True, None], pd.BooleanDtype(), BooleanArray._from_sequence([True, None])),
         # Index
         (pd.Index([1, 2]), None, PandasArray(np.array([1, 2], dtype=np.int64))),
         # Series[EA] returns the EA
@@ -174,7 +174,7 @@ def test_array_copy():
             period_array(["2000", "2001"], freq="D"),
         ),
         # interval
-        ([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2]),),
+        ([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2])),
         # datetime
         (
             [pd.Timestamp("2000"), pd.Timestamp("2001")],

pandas/tests/arrays/test_timedeltas.py

@@ -46,7 +46,7 @@ def test_incorrect_dtype_raises(self):
             TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
 
         with pytest.raises(
-            ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]",
+            ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]"
         ):
             TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64"))
 

pandas/tests/base/test_conversion.py

@@ -183,7 +183,7 @@ def test_iter_box(self):
             PeriodArray,
             pd.core.dtypes.dtypes.PeriodDtype("A-DEC"),
         ),
-        (pd.IntervalIndex.from_breaks([0, 1, 2]), IntervalArray, "interval",),
+        (pd.IntervalIndex.from_breaks([0, 1, 2]), IntervalArray, "interval"),
         # This test is currently failing for datetime64[ns] and timedelta64[ns].
         # The NumPy type system is sufficient for representing these types, so
         # we just use NumPy for Series / DataFrame columns of these types (so
@@ -285,10 +285,7 @@ def test_array_multiindex_raises():
             pd.core.arrays.period_array(["2000", "2001"], freq="D"),
             np.array([pd.Period("2000", freq="D"), pd.Period("2001", freq="D")]),
         ),
-        (
-            pd.core.arrays.integer_array([0, np.nan]),
-            np.array([0, pd.NA], dtype=object),
-        ),
+        (pd.core.arrays.integer_array([0, np.nan]), np.array([0, pd.NA], dtype=object)),
         (
             IntervalArray.from_breaks([0, 1, 2]),
             np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object),

pandas/tests/dtypes/test_missing.py

@@ -373,7 +373,7 @@ def test_array_equivalent(dtype_equal):
     )
     # The rest are not dtype_equal
     assert not array_equivalent(
-        DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan], tz="US/Eastern"),
+        DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan], tz="US/Eastern")
     )
     assert not array_equivalent(
         DatetimeIndex([0, np.nan], tz="CET"),

pandas/tests/extension/base/methods.py

@@ -92,18 +92,14 @@ def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_valu
         assert data_missing_for_sorting.argmax() == 0
         assert data_missing_for_sorting.argmin() == 2
 
-    @pytest.mark.parametrize(
-        "method", ["argmax", "argmin"],
-    )
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
     def test_argmin_argmax_empty_array(self, method, data):
         # GH 24382
         err_msg = "attempt to get"
         with pytest.raises(ValueError, match=err_msg):
             getattr(data[:0], method)()
 
-    @pytest.mark.parametrize(
-        "method", ["argmax", "argmin"],
-    )
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
     def test_argmin_argmax_all_na(self, method, data, na_value):
         # all missing with skipna=True is the same as emtpy
         err_msg = "attempt to get"

pandas/tests/extension/test_sparse.py

@@ -316,9 +316,7 @@ def test_shift_0_periods(self, data):
         data._sparse_values[0] = data._sparse_values[1]
         assert result._sparse_values[0] != result._sparse_values[1]
 
-    @pytest.mark.parametrize(
-        "method", ["argmax", "argmin"],
-    )
+    @pytest.mark.parametrize("method", ["argmax", "argmin"])
     def test_argmin_argmax_all_na(self, method, data, na_value):
         # overriding because Sparse[int64, 0] cannot handle na_value
         self._check_unsupported(data)

pandas/tests/frame/indexing/test_setitem.py

@@ -108,7 +108,7 @@ def test_setitem_timestamp_empty_columns(self):
         df["now"] = Timestamp("20130101", tz="UTC")
 
         expected = DataFrame(
-            [[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"],
+            [[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"]
         )
         tm.assert_frame_equal(df, expected)
 
@@ -158,11 +158,7 @@ def test_setitem_dict_preserves_dtypes(self):
             }
         )
         for idx, b in enumerate([1, 2, 3]):
-            df.loc[df.shape[0]] = {
-                "a": int(idx),
-                "b": float(b),
-                "c": float(b),
-            }
+            df.loc[df.shape[0]] = {"a": int(idx), "b": float(b), "c": float(b)}
         tm.assert_frame_equal(df, expected)
 
     @pytest.mark.parametrize(