Comma cleanup

pandas/tests/frame/test_analytics.py

@@ -86,11 +86,7 @@ def wrapper(x):
         result0 = f(axis=0, skipna=False)
         result1 = f(axis=1, skipna=False)
         tm.assert_series_equal(
-            result0,
-            frame.apply(wrapper),
-            check_dtype=check_dtype,
-            rtol=rtol,
-            atol=atol,
+            result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol
         )
         # HACK: win32
         tm.assert_series_equal(
@@ -116,7 +112,15 @@ def wrapper(x):
     if opname in ["sum", "prod"]:
         expected = frame.apply(skipna_wrapper, axis=1)
         tm.assert_series_equal(
-            result1, expected, check_dtype=False, rtol=rtol, atol=atol,
+<<<<<<< HEAD
+            result1, expected, check_dtype=False, rtol=rtol, atol=atol
+=======
+            result1,
+            expected,
+            check_dtype=False,
+            rtol=rtol,
+            atol=atol,
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
         )
 
     # check dtypes

pandas/tests/frame/test_constructors.py

@@ -932,7 +932,13 @@ def test_constructor_mrecarray(self):
         # from GH3479
 
         assert_fr_equal = functools.partial(
-            tm.assert_frame_equal, check_index_type=True, check_column_type=True,
+<<<<<<< HEAD
+            tm.assert_frame_equal, check_index_type=True, check_column_type=True
+=======
+            tm.assert_frame_equal,
+            check_index_type=True,
+            check_column_type=True,
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
         )
         arrays = [
             ("float", np.array([1.5, 2.0])),

pandas/tests/frame/test_reshape.py

@@ -417,7 +417,9 @@ def test_unstack_mixed_type_name_in_multiindex(
         result = df.unstack(unstack_idx)
 
         expected = pd.DataFrame(
-            expected_values, columns=expected_columns, index=expected_index,
+            expected_values,
+            columns=expected_columns,
+            index=expected_index,
         )
         tm.assert_frame_equal(result, expected)
 
@@ -807,7 +809,12 @@ def test_unstack_multi_level_cols(self):
                 [["B", "C"], ["B", "D"]], names=["c1", "c2"]
             ),
             index=pd.MultiIndex.from_tuples(
-                [[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"],
+<<<<<<< HEAD
+                [[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"]
+=======
+                [[10, 20, 30], [10, 20, 40]],
+                names=["i1", "i2", "i3"],
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
             ),
         )
         assert df.unstack(["i2", "i1"]).columns.names[-2:] == ["i2", "i1"]

pandas/tests/generic/test_finalize.py

@@ -123,7 +123,11 @@
     (pd.DataFrame, frame_data, operator.methodcaller("sort_index")),
     (pd.DataFrame, frame_data, operator.methodcaller("nlargest", 1, "A")),
     (pd.DataFrame, frame_data, operator.methodcaller("nsmallest", 1, "A")),
-    (pd.DataFrame, frame_mi_data, operator.methodcaller("swaplevel"),),
+    (
+        pd.DataFrame,
+        frame_mi_data,
+        operator.methodcaller("swaplevel"),
+    ),
     pytest.param(
         (
             pd.DataFrame,
@@ -178,7 +182,11 @@
         marks=not_implemented_mark,
     ),
     pytest.param(
-        (pd.DataFrame, frame_mi_data, operator.methodcaller("unstack"),),
+        (
+            pd.DataFrame,
+            frame_mi_data,
+            operator.methodcaller("unstack"),
+        ),
         marks=not_implemented_mark,
     ),
     pytest.param(
@@ -317,7 +325,7 @@
         marks=not_implemented_mark,
     ),
     pytest.param(
-        (pd.Series, ([1, 2],), operator.methodcaller("squeeze")),
+        (pd.Series, ([1, 2],), operator.methodcaller("squeeze"))
         # marks=not_implemented_mark,
     ),
     (pd.Series, ([1, 2],), operator.methodcaller("rename_axis", index="a")),
@@ -733,9 +741,14 @@ def test_timedelta_property(attr):
     assert result.attrs == {"a": 1}
 
 
+<<<<<<< HEAD
+@pytest.mark.parametrize("method", [operator.methodcaller("total_seconds")])
+=======
 @pytest.mark.parametrize(
-    "method", [operator.methodcaller("total_seconds")],
+    "method",
+    [operator.methodcaller("total_seconds")],
 )
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
 @not_implemented_mark
 def test_timedelta_methods(method):
     s = pd.Series(pd.timedelta_range("2000", periods=4))

pandas/tests/generic/test_to_xarray.py

@@ -47,9 +47,7 @@ def test_to_xarray_index_types(self, index):
         expected = df.copy()
         expected["f"] = expected["f"].astype(object)
         expected.columns.name = None
-        tm.assert_frame_equal(
-            result.to_dataframe(), expected,
-        )
+        tm.assert_frame_equal(result.to_dataframe(), expected)
 
     @td.skip_if_no("xarray", min_version="0.7.0")
     def test_to_xarray(self):

pandas/tests/groupby/aggregate/test_numba.py

@@ -57,7 +57,12 @@ 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],
+<<<<<<< HEAD
+        {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],
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -90,7 +95,12 @@ 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],
+<<<<<<< HEAD
+        {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],
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
     )
     engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
     grouped = data.groupby(0)
@@ -121,7 +131,12 @@ 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],
+<<<<<<< HEAD
+        {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],
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
     )
     grouped = data.groupby(0)
     expected = grouped.agg(func_1, engine="numba")
@@ -142,7 +157,12 @@ 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],
+<<<<<<< HEAD
+        {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],
+>>>>>>> c34ed0ebf1599a6ea21cf94846e4c7a8bb72a298
     )
     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
     """
 
@@ -1449,7 +1449,7 @@ def test_groupby_agg_categorical_columns(func, expected_values):
     result = df.groupby("groups").agg(func)
 
     expected = pd.DataFrame(
-        {"value": expected_values}, index=pd.Index([0, 1, 2], name="groups"),
+        {"value": expected_values}, index=pd.Index([0, 1, 2], name="groups")
     )
     tm.assert_frame_equal(result, expected)