pre-commit run --all-files changes

Author: xaris96

pandas/conftest.py

@@ -706,7 +706,7 @@ def _create_mi_with_dt64tz_level():
     "string-python": Index(
         pd.array([f"pandas_{i}" for i in range(10)], dtype="string[python]")
     ),
-    "mixed-int-string": Index([0, "a", 1, "b", 2, "c"])
+    "mixed-int-string": Index([0, "a", 1, "b", 2, "c"]),
 }
 if has_pyarrow:
     idx = Index(pd.array([f"pandas_{i}" for i in range(10)], dtype="string[pyarrow]"))

pandas/tests/base/test_value_counts.py

@@ -63,10 +63,9 @@ def test_value_counts_null(null_obj, index_or_series_obj):
     elif isinstance(orig, MultiIndex):
         pytest.skip(f"MultiIndex can't hold '{null_obj}'")
 
-    if obj.dtype == 'object':
+    if obj.dtype == "object":
         obj = obj.astype(str)
 
-
     values = obj._values
     values[0:2] = null_obj
 

pandas/tests/indexes/test_common.py

@@ -442,8 +442,7 @@ def test_hasnans_isnans(self, index_flat):
 def test_sort_values_invalid_na_position(index_with_missing, na_position):
     non_na_values = [x for x in index_with_missing if pd.notna(x)]
     if len({type(x) for x in non_na_values}) > 1:
-        pytest.xfail("Sorting fails due to heterogeneous types in index"
-                    " (int vs str)")
+        pytest.mark.xfail(reason="Sorting fails due to heterogeneous types in index (int vs str)")
 
     with pytest.raises(ValueError, match=f"invalid na_position: {na_position}"):
         index_with_missing.sort_values(na_position=na_position)
@@ -457,8 +456,7 @@ def test_sort_values_with_missing(index_with_missing, na_position, request):
 
     non_na_values = [x for x in index_with_missing if pd.notna(x)]
     if len({type(x) for x in non_na_values}) > 1:
-        pytest.xfail("Sorting fails due to heterogeneous types"
-                    " in index (int vs str)")
+        pytest.mark.xfail(reason="Sorting fails due to heterogeneous types in index (int vs str)")
 
     if isinstance(index_with_missing, CategoricalIndex):
         request.applymarker(

pandas/tests/indexes/test_mixed_int_string.py

@@ -1,6 +1,8 @@
 import pytest
+
 import pandas as pd
 
+
 def test_mixed_int_string_index():
     idx = pd.Index([0, "a", 1, "b", 2, "c"])
 

pandas/tests/indexes/test_numpy_compat.py

@@ -159,8 +159,9 @@ def test_numpy_ufuncs_reductions(index, func, request):
     has_int = any(isinstance(x, int) for x in index)
     if has_str and has_int:
         request.applymarker(
-            pytest.mark.xfail(reason="Cannot compare mixed types (int and str)"
-                            " in ufunc reductions")
+            pytest.mark.xfail(
+                reason="Cannot compare mixed types (int and str) in ufunc reductions"
+            )
         )
 
     if isinstance(index, CategoricalIndex) and index.dtype.ordered is False:

pandas/tests/indexes/test_old_base.py

@@ -359,7 +359,7 @@ def test_argsort(self, index):
             pytest.skip(f"{type(self).__name__} separately tested")
 
         # New test for mixed-int-string
-        if index.equals(pd.Index([0, "a", 1, "b", 2, "c"])):
+        if index.equals(Index([0, "a", 1, "b", 2, "c"])):
             result = index.astype(str).argsort()
             expected = np.array(index.astype(str)).argsort()
             tm.assert_numpy_array_equal(result, expected, check_dtype=False)

pandas/tests/indexes/test_setops.py

@@ -5,14 +5,15 @@
 
 from datetime import datetime
 import operator
-import pandas as pd
+
 import numpy as np
 import pytest
 
 from pandas._libs import lib
 
 from pandas.core.dtypes.cast import find_common_type
 
+import pandas as pd
 from pandas import (
     CategoricalDtype,
     CategoricalIndex,
@@ -64,13 +65,14 @@ def index_flat2(index_flat):
 
 def test_union_same_types(index):
     # mixed int string
-    if index.equals(pd.Index([0, "a", 1, "b", 2, "c"])):
+    if index.equals(Index([0, "a", 1, "b", 2, "c"])):
         index = index.astype(str)
 
     idx1 = index.sort_values()
     idx2 = index.sort_values()
     assert idx1.union(idx2, sort=False).dtype == idx1.dtype
 
+
 def test_union_different_types(index_flat, index_flat2, request):
     idx1 = index_flat
     idx2 = index_flat2
@@ -129,6 +131,7 @@ def test_union_different_types(index_flat, index_flat2, request):
         assert res1.dtype == common_dtype
         assert res2.dtype == common_dtype
 
+
 @pytest.mark.parametrize(
     "idx1,idx2",
     [
@@ -233,7 +236,6 @@ def test_intersection_base(self, index):
 
     @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
     def test_union_base(self, index):
-
         if index.inferred_type in ["mixed", "mixed-integer"]:
             pytest.skip("Mixed-type Index not orderable; union fails")
 
@@ -295,7 +297,6 @@ def test_difference_base(self, sort, index):
 
     @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
     def test_symmetric_difference(self, index, using_infer_string, request):
-
         if (
             using_infer_string
             and index.dtype == "object"
@@ -392,7 +393,7 @@ def test_union_unequal(self, index_flat, fname, sname, expected_name):
         else:
             index = index_flat
 
-        if index.dtype == 'object':
+        if index.dtype == "object":
             index = index.astype(str)
 
         # test copy.union(subset) - need sort for unicode and string
@@ -464,7 +465,7 @@ def test_intersect_unequal(self, index_flat, fname, sname, expected_name):
         else:
             index = index_flat
 
-        if index.dtype == 'object':
+        if index.dtype == "object":
             index = index.astype(str)
         # test copy.intersection(subset) - need sort for unicode and string
         first = index.copy().set_names(fname)
@@ -919,16 +920,16 @@ def test_symmetric_difference_mi(self, sort):
         index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])
 
         def has_mixed_types(level):
-            return (
-                any(isinstance(x, str) for x in level)
-                and any(isinstance(x, int) for x in level)
+            return any(isinstance(x, str) for x in level) and any(
+                isinstance(x, int) for x in level
             )
 
         for idx in [index1, index2]:
             for lvl in range(idx.nlevels):
                 if has_mixed_types(idx.get_level_values(lvl)):
-                    pytest.skip(f"Mixed types in MultiIndex level {lvl}"
-                                " are not orderable")
+                    pytest.skip(
+                        f"Mixed types in MultiIndex level {lvl} are not orderable"
+                    )
 
         result = index1.symmetric_difference(index2, sort=sort)
         expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])

pandas/tests/test_algos.py

@@ -63,12 +63,9 @@ def test_factorize_complex(self):
         expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=complex)
         tm.assert_numpy_array_equal(uniques, expected_uniques)
 
-    @pytest.mark.parametrize("index_or_series_obj",
-                            [
-                            [1, 2, 3],
-                            ["a", "b", "c"],
-                            [0, "a", 1, "b", 2, "c"]
-                            ])
+    @pytest.mark.parametrize(
+        "index_or_series_obj", [[1, 2, 3], ["a", "b", "c"], [0, "a", 1, "b", 2, "c"]]
+    )
     @pytest.mark.parametrize("sort", [True, False])
     def test_factorize(self, index_or_series_obj, sort):
         obj = Index(index_or_series_obj)
@@ -77,9 +74,10 @@ def test_factorize(self, index_or_series_obj, sort):
             pytest.skip("Skipping test for empty Index")
 
         if obj.name == "mixed-int-string" or obj.name is None:
-            pytest.skip("Skipping test for mixed-int-string due"
-                        " to unsupported comparison between str and int")
-
+            pytest.skip(
+                "Skipping test for mixed-int-string due "
+                "to unsupported comparison between str and int"
+            )
 
         result_codes, result_uniques = obj.factorize(sort=sort)
 
@@ -92,11 +90,9 @@ def test_factorize(self, index_or_series_obj, sort):
         if expected_uniques.dtype == bool and obj.dtype == object:
             expected_uniques = expected_uniques.astype(object)
 
-
         if sort:
             expected_uniques = expected_uniques.sort_values()
 
-
         # construct an integer ndarray so that
         # `expected_uniques.take(expected_codes)` is equal to `obj`
         expected_uniques_list = list(expected_uniques)