Backport PR #42524: CI: Xfail less on Python 3.10 (#42525)

Co-authored-by: Thomas Li <[email protected]>
Co-authored-by: Simon Hawkins <[email protected]>

Author: 3 people

pandas/tests/indexes/test_base.py

@@ -12,7 +12,6 @@
 
 from pandas.compat import (
     IS64,
-    PY310,
     np_datetime64_compat,
 )
 from pandas.util._test_decorators import async_mark
@@ -1004,24 +1003,6 @@ def test_isin_nan_common_object(self, request, nulls_fixture, nulls_fixture2):
             and math.isnan(nulls_fixture)
             and math.isnan(nulls_fixture2)
         ):
-            if PY310:
-                if (
-                    # Failing cases are
-                    # np.nan, float('nan')
-                    # float('nan'), np.nan
-                    # float('nan'), float('nan')
-                    # Since only float('nan'), np.nan is float
-                    # Use not np.nan to identify float('nan')
-                    nulls_fixture is np.nan
-                    and nulls_fixture2 is not np.nan
-                    or nulls_fixture is not np.nan
-                ):
-                    request.applymarker(
-                        # This test is flaky :(
-                        pytest.mark.xfail(
-                            reason="Failing on Python 3.10 GH41940", strict=False
-                        )
-                    )
             tm.assert_numpy_array_equal(
                 Index(["a", nulls_fixture]).isin([nulls_fixture2]),
                 np.array([False, True]),

pandas/tests/reshape/test_get_dummies.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import PY310
-
 from pandas.core.dtypes.common import is_integer_dtype
 
 import pandas as pd
@@ -430,8 +428,6 @@ def test_dataframe_dummies_unicode(self, get_dummies_kwargs, expected):
         result = get_dummies(**get_dummies_kwargs)
         tm.assert_frame_equal(result, expected)
 
-    # This is flaky on Python 3.10
-    @pytest.mark.xfail(PY310, reason="Failing on Python 3.10 GH41940", strict=False)
     def test_get_dummies_basic_drop_first(self, sparse):
         # GH12402 Add a new parameter `drop_first` to avoid collinearity
         # Basic case
@@ -471,7 +467,6 @@ def test_get_dummies_basic_drop_first_one_level(self, sparse):
         result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
-    @pytest.mark.xfail(PY310, reason="Failing on Python 3.10 GH41940", strict=False)
     def test_get_dummies_basic_drop_first_NA(self, sparse):
         # Test NA handling together with drop_first
         s_NA = ["a", "b", np.nan]

pandas/tests/test_algos.py

@@ -9,10 +9,7 @@
     algos as libalgos,
     hashtable as ht,
 )
-from pandas.compat import (
-    PY310,
-    np_array_datetime64_compat,
-)
+from pandas.compat import np_array_datetime64_compat
 import pandas.util._test_decorators as td
 
 from pandas.core.dtypes.common import (
@@ -786,8 +783,6 @@ def test_different_nans(self):
         expected = np.array([np.nan])
         tm.assert_numpy_array_equal(result, expected)
 
-    # Flaky on Python 3.10 -> Don't make strict
-    @pytest.mark.xfail(PY310, reason="Failing on Python 3.10 GH41940", strict=False)
     def test_first_nan_kept(self):
         # GH 22295
         # create different nans from bit-patterns:
@@ -993,8 +988,6 @@ def __hash__(self):
         # different objects -> False
         tm.assert_numpy_array_equal(algos.isin([a], [b]), np.array([False]))
 
-    # Flaky on Python 3.10 -> Don't make strict
-    @pytest.mark.xfail(PY310, reason="Failing on Python 3.10 GH41940", strict=False)
     def test_different_nans(self):
         # GH 22160
         # all nans are handled as equivalent
@@ -1037,8 +1030,6 @@ def test_empty(self, empty):
         result = algos.isin(vals, empty)
         tm.assert_numpy_array_equal(expected, result)
 
-    # Flaky on Python 3.10 -> Don't make strict
-    @pytest.mark.xfail(PY310, reason="Failing on Python 3.10 GH41940", strict=False)
     def test_different_nan_objects(self):
         # GH 22119
         comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)