CLN remove unnecessary trailing commas in groupby tests

Author: xcz011

pandas/tests/groupby/aggregate/test_numba.py

@@ -138,7 +138,7 @@ def func_1(values, index):
         "min",
         {"B": ["min", "max"], "C": "sum"},
         NamedAgg(column="B", aggfunc="min"),
-    ],
+    ]
 )
 def test_multifunc_notimplimented(agg_func):
     data = DataFrame(

pandas/tests/groupby/test_apply.py

@@ -136,7 +136,7 @@ def f(g):
         "GH12155",
         "GH20084",
         "GH21417",
-    ],
+    ]
 )
 def test_group_apply_once_per_group(df, group_names):
     # GH2936, GH7739, GH10519, GH2656, GH12155, GH20084, GH21417
@@ -800,7 +800,7 @@ def test_func_returns_object():
 
 @pytest.mark.parametrize(
     "group_column_dtlike",
-    [datetime.today(), datetime.today().date(), datetime.today().time()],
+    [datetime.today(), datetime.today().date(), datetime.today().time()]
 )
 def test_apply_datetime_issue(group_column_dtlike):
     # GH-28247
@@ -899,7 +899,7 @@ def test_groupby_apply_datetime_result_dtypes():
         pd.interval_range(0, 3),
         pd.period_range("2020", periods=3, freq="D"),
         pd.MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
-    ],
+    ]
 )
 def test_apply_index_has_complex_internals(index):
     # GH 31248
@@ -922,7 +922,7 @@ def test_apply_index_has_complex_internals(index):
             lambda x: [{n: i} for (n, i) in enumerate(x.index.to_list())],
             [[{0: 0}, {1: 1}], [{0: 2}, {1: 3}]],
         ),
-    ],
+    ]
 )
 def test_apply_function_returns_non_pandas_non_scalar(function, expected_values):
     # GH 31441
@@ -947,7 +947,7 @@ def fct(group):
 
 
 @pytest.mark.parametrize(
-    "function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1],
+    "function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1]
 )
 def test_apply_function_index_return(function):
     # GH: 22541

pandas/tests/groupby/test_categorical.py

@@ -762,7 +762,7 @@ def test_preserve_categorical_dtype():
         ("last", ["fourth", "third"]),
         ("min", ["fourth", "first"]),
         ("max", ["second", "third"]),
-    ],
+    ]
 )
 def test_preserve_on_ordered_ops(func, values):
     # gh-18502
@@ -1102,7 +1102,7 @@ def test_groupby_multiindex_categorical_datetime():
                 }
             ),
         ),
-    ],
+    ]
 )
 def test_groupby_agg_observed_true_single_column(as_index, expected):
     # GH-23970
@@ -1233,7 +1233,7 @@ def test_seriesgroupby_observed_false_or_none(df_cat, observed, operation):
             ),
             [2, 2, 4, 4, np.nan, np.nan, 1, 1, np.nan, np.nan, 3, 3],
         ),
-    ],
+    ]
 )
 def test_seriesgroupby_observed_apply_dict(df_cat, observed, index, data):
     # GH 24880
@@ -1435,7 +1435,7 @@ def test_series_groupby_categorical_aggregation_getitem():
 
 @pytest.mark.parametrize(
     "func, expected_values",
-    [(pd.Series.nunique, [1, 1, 2]), (pd.Series.count, [1, 2, 2])],
+    [(pd.Series.nunique, [1, 1, 2]), (pd.Series.count, [1, 2, 2])]
 )
 def test_groupby_agg_categorical_columns(func, expected_values):
     # 31256

pandas/tests/groupby/test_groupby.py

@@ -1634,7 +1634,7 @@ def test_transform_doesnt_clobber_ints():
 
 @pytest.mark.parametrize(
     "sort_column",
-    ["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]],
+    ["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]]
 )
 @pytest.mark.parametrize(
     "group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]]
@@ -1925,7 +1925,7 @@ def test_groupby_axis_1(group_name):
                 ]
             },
         ),
-    ],
+    ]
 )
 def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
     # GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
@@ -2042,7 +2042,7 @@ def test_groupby_list_level():
     [
         (5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"),
         (4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"),
-    ],
+    ]
 )
 def test_groups_repr_truncates(max_seq_items, expected):
     # GH 1135
@@ -2097,7 +2097,7 @@ def test_group_on_two_row_multiindex_returns_one_tuple_key():
         (Series, "squeeze", True),
         (Series, "observed", True),
         (Series, "dropna", False),
-    ],
+    ]
 )
 @pytest.mark.filterwarnings(
     "ignore:The `squeeze` parameter is deprecated:FutureWarning"

pandas/tests/groupby/test_groupby_dropna.py

@@ -22,7 +22,7 @@
                 "e": [13.0, 12.0, 1.0],
             },
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_multi_index_dataframe_nan_in_one_group(
     dropna, tuples, outputs, nulls_fixture
@@ -65,7 +65,7 @@ def test_groupby_dropna_multi_index_dataframe_nan_in_one_group(
                 "e": [12.0, 13.0, 1.0, 1.0],
             },
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_multi_index_dataframe_nan_in_two_groups(
     dropna, tuples, outputs, nulls_fixture, nulls_fixture2
@@ -105,7 +105,7 @@ def test_groupby_dropna_multi_index_dataframe_nan_in_two_groups(
                 "d": [1.0, 13.0, 12.0],
             },
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_normal_index_dataframe(dropna, idx, outputs):
     # GH 3729
@@ -132,7 +132,7 @@ def test_groupby_dropna_normal_index_dataframe(dropna, idx, outputs):
             ["a", "a", "b", np.nan],
             pd.Series([3, 3, 3], index=["a", "b", np.nan]),
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_series_level(dropna, idx, expected):
     ser = pd.Series([1, 2, 3, 3], index=idx)
@@ -149,7 +149,7 @@ def test_groupby_dropna_series_level(dropna, idx, expected):
             False,
             pd.Series([210.0, 350.0, 20.0], index=["a", "b", np.nan], name="Max Speed"),
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_series_by(dropna, expected):
     ser = pd.Series(
@@ -176,7 +176,7 @@ def test_groupby_dropna_series_by(dropna, expected):
             pd.DataFrame({"B": [2, 2, 1, 1]}),
             pd.Series(data=[2, 2, 1, 1], name="B"),
         ),
-    ],
+    ]
 )
 def test_slice_groupby_then_transform(dropna, df_expected, s_expected):
     # GH35014
@@ -213,7 +213,7 @@ def test_slice_groupby_then_transform(dropna, df_expected, s_expected):
                 "e": [1.0, 12.0, 1.0],
             },
         ),
-    ],
+    ]
 )
 def test_groupby_dropna_multi_index_dataframe_agg(dropna, tuples, outputs):
     # GH 3729
@@ -244,11 +244,9 @@ def test_groupby_dropna_multi_index_dataframe_agg(dropna, tuples, outputs):
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-02-01")),
         (pd.Timedelta("-2 days"), pd.Timedelta("-1 days")),
         (pd.Period("2020-01-01"), pd.Period("2020-02-01")),
-    ],
-)
-@pytest.mark.parametrize(
-    "dropna, values", [(True, [12, 3]), (False, [12, 3, 6],)],
+    ]
 )
+@pytest.mark.parametrize("dropna, values", [(True, [12, 3]), (False, [12, 3, 6],)])
 def test_groupby_dropna_datetime_like_data(
     dropna, values, datetime1, datetime2, unique_nulls_fixture, unique_nulls_fixture2
 ):

pandas/tests/groupby/test_groupby_subclass.py

@@ -12,7 +12,7 @@
     [
         tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
         tm.SubclassedSeries(np.arange(0, 10), name="A"),
-    ],
+    ]
 )
 @pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
 def test_groupby_preserves_subclass(obj, groupby_func):
@@ -51,9 +51,7 @@ def test_groupby_preserves_subclass(obj, groupby_func):
         tm.assert_series_equal(result1, result2)
 
 
-@pytest.mark.parametrize(
-    "obj", [DataFrame, tm.SubclassedDataFrame],
-)
+@pytest.mark.parametrize("obj", [DataFrame, tm.SubclassedDataFrame])
 def test_groupby_resample_preserves_subclass(obj):
     # GH28330 -- preserve subclass through groupby.resample()
 

pandas/tests/groupby/test_size.py

@@ -53,7 +53,8 @@ def test_size_on_categorical(as_index):
     result = df.groupby(["A", "B"], as_index=as_index).size()
 
     expected = DataFrame(
-        [[1, 1, 1], [1, 2, 0], [2, 1, 0], [2, 2, 1]], columns=["A", "B", "size"],
+        [[1, 1, 1], [1, 2, 0], [2, 1, 0], [2, 2, 1]],
+        columns=["A", "B", "size"]
     )
     expected["A"] = expected["A"].astype("category")
     if as_index:

pandas/tests/groupby/test_timegrouper.py

@@ -780,6 +780,6 @@ def test_grouper_period_index(self):
         result = period_series.groupby(period_series.index.month).sum()
 
         expected = pd.Series(
-            range(0, periods), index=Index(range(1, periods + 1), name=index.name),
+            range(0, periods), index=Index(range(1, periods + 1), name=index.name)
         )
         tm.assert_series_equal(result, expected)