diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 52a1e3aae9058..c807e7eb9c4d3 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/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 diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py index c8f5b2b0f6364..ac1100236d2f0 100644 --- a/pandas/tests/frame/test_constructors.py +++ b/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])), diff --git a/pandas/tests/frame/test_reshape.py b/pandas/tests/frame/test_reshape.py index 6a8f1e7c1aca2..703cb4412017b 100644 --- a/pandas/tests/frame/test_reshape.py +++ b/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"] diff --git a/pandas/tests/generic/test_finalize.py b/pandas/tests/generic/test_finalize.py index 4d0f1a326225d..4ea1e07912892 100644 --- a/pandas/tests/generic/test_finalize.py +++ b/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)) diff --git a/pandas/tests/generic/test_to_xarray.py b/pandas/tests/generic/test_to_xarray.py index ab56a752f7e90..a85d7ddc1ea53 100644 --- a/pandas/tests/generic/test_to_xarray.py +++ b/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): diff --git a/pandas/tests/groupby/aggregate/test_numba.py b/pandas/tests/groupby/aggregate/test_numba.py index 29e65e938f6f9..4fe78a481565f 100644 --- a/pandas/tests/groupby/aggregate/test_numba.py +++ b/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"): diff --git a/pandas/tests/groupby/test_apply.py b/pandas/tests/groupby/test_apply.py index a1dcb28a32c6c..3183305fe2933 100644 --- a/pandas/tests/groupby/test_apply.py +++ b/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"]) diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py index 13a32e285e70a..711daf7fe415d 100644 --- a/pandas/tests/groupby/test_categorical.py +++ b/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)