CLN remove unnecessary trailing commas in groupby tests

Author: xcz011

pandas/tests/groupby/aggregate/test_numba.py

@@ -57,7 +57,7 @@ def func_numba(values, index):
         func_numba = numba.jit(func_numba)
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -90,7 +90,7 @@ def func_2(values, index):
         func_2 = numba.jit(func_2)
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -121,7 +121,7 @@ def func_1(values, index):
         return np.mean(values) - 3.4
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     grouped = data.groupby(0)
     expected = grouped.agg(func_1, engine="numba")
@@ -142,7 +142,7 @@ def func_1(values, index):
 )
 def test_multifunc_notimplimented(agg_func):
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     grouped = data.groupby(0)
     with pytest.raises(NotImplementedError, match="Numba engine can"):

pandas/tests/groupby/test_apply.py

@@ -946,9 +946,7 @@ def fct(group):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize(
-    "function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1],
-)
+@pytest.mark.parametrize("function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1])
 def test_apply_function_index_return(function):
     # GH: 22541
     df = pd.DataFrame([1, 2, 2, 2, 1, 2, 3, 1, 3, 1], columns=["id"])

pandas/tests/groupby/test_categorical.py

@@ -17,7 +17,7 @@
 
 
 def cartesian_product_for_groupers(result, args, names, fill_value=np.NaN):
-    """ Reindex to a cartesian production for the groupers,
+    """Reindex to a cartesian production for the groupers,
     preserving the nature (Categorical) of each grouper
     """
 

pandas/tests/groupby/test_groupby_dropna.py

@@ -246,9 +246,7 @@ def test_groupby_dropna_multi_index_dataframe_agg(dropna, tuples, outputs):
         (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

@@ -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 +53,7 @@ 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

@@ -408,7 +408,7 @@ def test_timegrouper_apply_return_type_series(self):
         df_dt["date"] = pd.to_datetime(df_dt["date"])
 
         def sumfunc_series(x):
-            return pd.Series([x["value"].sum()], ("sum",))
+            return pd.Series([x["value"].sum()], ("sum"))
 
         expected = df.groupby(pd.Grouper(key="date")).apply(sumfunc_series)
         result = df_dt.groupby(pd.Grouper(freq="M", key="date")).apply(sumfunc_series)
@@ -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)

pandas/tests/groupby/transform/test_numba.py

@@ -56,7 +56,7 @@ def func(values, index):
         func = numba.jit(func)
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -89,7 +89,7 @@ def func_2(values, index):
         func_2 = numba.jit(func_2)
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -120,7 +120,7 @@ def func_1(values, index):
         return values + 1
 
     data = DataFrame(
-        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1],
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
     )
     grouped = data.groupby(0)
     expected = grouped.transform(func_1, engine="numba")