Issue35925 Remove trailing commas (#35996)

* pandas/tests/test_multilevel.py

* pandas/tests/test_nanops.py

* pandas/.../test_moments_consistency_rolling.py

* pandas/tests/window/moments/test_moments_ewm.py

* pandas/.../test_moments_rolling.py

* pandas/tests/window/test_base_indexer.py

* pandas/tests/window/test_pairwise.py

* pandas/tests/window/test_rolling.py

* pandas/tseries/frequencies.py

* pandas/util/_test_decorators.py

Author: metehankutlu

pandas/tests/test_multilevel.py

@@ -1846,7 +1846,7 @@ def test_multilevel_index_loc_order(self, dim, keys, expected):
         # GH 22797
         # Try to respect order of keys given for MultiIndex.loc
         kwargs = {dim: [["c", "a", "a", "b", "b"], [1, 1, 2, 1, 2]]}
-        df = pd.DataFrame(np.arange(25).reshape(5, 5), **kwargs,)
+        df = pd.DataFrame(np.arange(25).reshape(5, 5), **kwargs)
         exp_index = MultiIndex.from_arrays(expected)
         if dim == "index":
             res = df.loc[keys, :]

pandas/tests/test_nanops.py

@@ -285,7 +285,7 @@ def test_nansum(self, skipna):
 
     def test_nanmean(self, skipna):
         self.check_funs(
-            nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False,
+            nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False
         )
 
     def test_nanmean_overflow(self):

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -95,7 +95,7 @@ def test_rolling_apply_consistency(
 
     with warnings.catch_warnings():
         warnings.filterwarnings(
-            "ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning,
+            "ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning
         )
         # test consistency between rolling_xyz() and either (a)
         # rolling_apply of Series.xyz(), or (b) rolling_apply of
@@ -107,7 +107,7 @@ def test_rolling_apply_consistency(
             functions = no_nan_functions + base_functions
         for (f, require_min_periods, name) in functions:
             rolling_f = getattr(
-                x.rolling(window=window, center=center, min_periods=min_periods), name,
+                x.rolling(window=window, center=center, min_periods=min_periods), name
             )
 
             if (
@@ -492,7 +492,7 @@ def test_moment_functions_zero_length_pairwise():
     df2["a"] = df2["a"].astype("float64")
 
     df1_expected = DataFrame(
-        index=pd.MultiIndex.from_product([df1.index, df1.columns]), columns=Index([]),
+        index=pd.MultiIndex.from_product([df1.index, df1.columns]), columns=Index([])
     )
     df2_expected = DataFrame(
         index=pd.MultiIndex.from_product(
@@ -635,7 +635,7 @@ def test_rolling_consistency(consistency_data, window, min_periods, center):
     # with empty/0-length Series/DataFrames
     with warnings.catch_warnings():
         warnings.filterwarnings(
-            "ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning,
+            "ignore", message=".*(empty slice|0 for slice).*", category=RuntimeWarning
         )
 
         # test consistency between different rolling_* moments

pandas/tests/window/moments/test_moments_ewm.py

@@ -73,7 +73,7 @@ def simple_wma(s, w):
         (s1, True, True, [(1.0 - alpha), np.nan, 1.0]),
         (s1, False, False, [(1.0 - alpha) ** 2, np.nan, alpha]),
         (s1, False, True, [(1.0 - alpha), np.nan, alpha]),
-        (s2, True, False, [np.nan, (1.0 - alpha) ** 3, np.nan, np.nan, 1.0, np.nan],),
+        (s2, True, False, [np.nan, (1.0 - alpha) ** 3, np.nan, np.nan, 1.0, np.nan]),
         (s2, True, True, [np.nan, (1.0 - alpha), np.nan, np.nan, 1.0, np.nan]),
         (
             s2,
@@ -95,7 +95,7 @@ def simple_wma(s, w):
                 alpha * ((1.0 - alpha) ** 2 + alpha),
             ],
         ),
-        (s3, False, True, [(1.0 - alpha) ** 2, np.nan, (1.0 - alpha) * alpha, alpha],),
+        (s3, False, True, [(1.0 - alpha) ** 2, np.nan, (1.0 - alpha) * alpha, alpha]),
     ]:
         expected = simple_wma(s, Series(w))
         result = s.ewm(com=com, adjust=adjust, ignore_na=ignore_na).mean()

pandas/tests/window/moments/test_moments_rolling.py

@@ -150,14 +150,14 @@ def get_result(obj, window, min_periods=None, center=False):
 
             series_xp = (
                 get_result(
-                    series.reindex(list(series.index) + s), window=25, min_periods=minp,
+                    series.reindex(list(series.index) + s), window=25, min_periods=minp
                 )
                 .shift(-12)
                 .reindex(series.index)
             )
             frame_xp = (
                 get_result(
-                    frame.reindex(list(frame.index) + s), window=25, min_periods=minp,
+                    frame.reindex(list(frame.index) + s), window=25, min_periods=minp
                 )
                 .shift(-12)
                 .reindex(frame.index)
@@ -169,14 +169,14 @@ def get_result(obj, window, min_periods=None, center=False):
         else:
             series_xp = (
                 get_result(
-                    series.reindex(list(series.index) + s), window=25, min_periods=0,
+                    series.reindex(list(series.index) + s), window=25, min_periods=0
                 )
                 .shift(-12)
                 .reindex(series.index)
             )
             frame_xp = (
                 get_result(
-                    frame.reindex(list(frame.index) + s), window=25, min_periods=0,
+                    frame.reindex(list(frame.index) + s), window=25, min_periods=0
                 )
                 .shift(-12)
                 .reindex(frame.index)

pandas/tests/window/test_base_indexer.py

@@ -88,8 +88,8 @@ def get_window_bounds(self, num_values, min_periods, center, closed):
 @pytest.mark.parametrize(
     "func,np_func,expected,np_kwargs",
     [
-        ("count", len, [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, np.nan], {},),
-        ("min", np.min, [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 6.0, 7.0, 8.0, np.nan], {},),
+        ("count", len, [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, np.nan], {}),
+        ("min", np.min, [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 6.0, 7.0, 8.0, np.nan], {}),
         (
             "max",
             np.max,
@@ -204,7 +204,7 @@ def test_rolling_forward_skewness(constructor):
 @pytest.mark.parametrize(
     "func,expected",
     [
-        ("cov", [2.0, 2.0, 2.0, 97.0, 2.0, -93.0, 2.0, 2.0, np.nan, np.nan],),
+        ("cov", [2.0, 2.0, 2.0, 97.0, 2.0, -93.0, 2.0, 2.0, np.nan, np.nan]),
         (
             "corr",
             [

pandas/tests/window/test_pairwise.py

@@ -195,7 +195,7 @@ def test_cov_mulittindex(self):
 
         columns = MultiIndex.from_product([list("ab"), list("xy"), list("AB")])
         index = range(3)
-        df = DataFrame(np.arange(24).reshape(3, 8), index=index, columns=columns,)
+        df = DataFrame(np.arange(24).reshape(3, 8), index=index, columns=columns)
 
         result = df.ewm(alpha=0.1).cov()
 

pandas/tests/window/test_rolling.py

@@ -73,7 +73,7 @@ def test_constructor_with_timedelta_window(window):
     # GH 15440
     n = 10
     df = DataFrame(
-        {"value": np.arange(n)}, index=pd.date_range("2015-12-24", periods=n, freq="D"),
+        {"value": np.arange(n)}, index=pd.date_range("2015-12-24", periods=n, freq="D")
     )
     expected_data = np.append([0.0, 1.0], np.arange(3.0, 27.0, 3))
 
@@ -92,7 +92,7 @@ def test_constructor_timedelta_window_and_minperiods(window, raw):
     # GH 15305
     n = 10
     df = DataFrame(
-        {"value": np.arange(n)}, index=pd.date_range("2017-08-08", periods=n, freq="D"),
+        {"value": np.arange(n)}, index=pd.date_range("2017-08-08", periods=n, freq="D")
     )
     expected = DataFrame(
         {"value": np.append([np.NaN, 1.0], np.arange(3.0, 27.0, 3))},
@@ -153,7 +153,7 @@ def test_closed_one_entry(func):
 def test_closed_one_entry_groupby(func):
     # GH24718
     ser = pd.DataFrame(
-        data={"A": [1, 1, 2], "B": [3, 2, 1]}, index=pd.date_range("2000", periods=3),
+        data={"A": [1, 1, 2], "B": [3, 2, 1]}, index=pd.date_range("2000", periods=3)
     )
     result = getattr(
         ser.groupby("A", sort=False)["B"].rolling("10D", closed="left"), func
@@ -182,7 +182,7 @@ def test_closed_one_entry_groupby(func):
 def test_closed_min_max_datetime(input_dtype, func, closed, expected):
     # see gh-21704
     ser = pd.Series(
-        data=np.arange(10).astype(input_dtype), index=pd.date_range("2000", periods=10),
+        data=np.arange(10).astype(input_dtype), index=pd.date_range("2000", periods=10)
     )
 
     result = getattr(ser.rolling("3D", closed=closed), func)()

pandas/tseries/frequencies.py

@@ -548,7 +548,7 @@ def is_superperiod(source, target) -> bool:
 
 
 def _maybe_coerce_freq(code) -> str:
-    """ we might need to coerce a code to a rule_code
+    """we might need to coerce a code to a rule_code
     and uppercase it
 
     Parameters

pandas/util/_test_decorators.py

@@ -186,10 +186,10 @@ def skip_if_no(package: str, min_version: Optional[str] = None):
     is_platform_windows(), reason="not used on win32"
 )
 skip_if_has_locale = pytest.mark.skipif(
-    _skip_if_has_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}",
+    _skip_if_has_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
 )
 skip_if_not_us_locale = pytest.mark.skipif(
-    _skip_if_not_us_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}",
+    _skip_if_not_us_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
 )
 skip_if_no_scipy = pytest.mark.skipif(
     _skip_if_no_scipy(), reason="Missing SciPy requirement"