TST: Xfails in Python 3.10 CI #41940

lithomas1 · 2021-06-11T02:41:54Z

A list of failures for pandas on Python 3.10.
Note: Some of these are flaky.

TestUnique.test_first_nan_kept

________________________ TestUnique.test_first_nan_kept ________________________

self = <pandas.tests.test_algos.TestUnique object at 0x7fe611cc19c0>

def test_first_nan_kept(self):
    # GH 22295
    # create different nans from bit-patterns:
    bits_for_nan1 = 0xFFF8000000000001
    bits_for_nan2 = 0x7FF8000000000001
    NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0]
    NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
    assert NAN1 != NAN1
    assert NAN2 != NAN2
    for el_type in [np.float64, object]:
        a = np.array([NAN1, NAN2], dtype=el_type)
        result = pd.unique(a)

      assert result.size == 1

E assert 2 == 1
E + where 2 = array([nan, nan], dtype=object).size

pandas/tests/test_algos.py:798: AssertionError

TestIsin.test_different_nans

_________________________ TestIsin.test_different_nans _________________________

self = <pandas.tests.test_algos.TestIsin object at 0x7fe611cc2b30>

def test_different_nans(self):
    # GH 22160
    # all nans are handled as equivalent

    comps = [float("nan")]
    values = [float("nan")]
    assert comps[0] is not values[0]  # different nan-objects

    # as list of python-objects:
    result = algos.isin(comps, values)
    tm.assert_numpy_array_equal(np.array([True]), result)

    # as object-array:
    result = algos.isin(
        np.asarray(comps, dtype=object), np.asarray(values, dtype=object)
    )

  tm.assert_numpy_array_equal(np.array([True]), result)

pandas/tests/test_algos.py:1007:

left = array([ True]), right = array([False]), err_msg = None

def _raise(left, right, err_msg):
    if err_msg is None:
        if left.shape != right.shape:
            raise_assert_detail(
                obj, f"{obj} shapes are different", left.shape, right.shape
            )

        diff = 0
        for left_arr, right_arr in zip(left, right):
            # count up differences
            if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
                diff += 1

        diff = diff * 100.0 / left.size
        msg = f"{obj} values are different ({np.round(diff, 5)} %)"

      raise_assert_detail(obj, msg, left, right, index_values=index_values)

E AssertionError: numpy array are different
E
E numpy array values are different (100.0 %)
E [left]: [True]
E [right]: [False]

pandas/_testing/asserters.py:726: AssertionError

TestIsin.test_different_nan_objects

_____________________ TestIsin.test_different_nan_objects ______________________

self = <pandas.tests.test_algos.TestIsin object at 0x7fe61167ff40>

def test_different_nan_objects(self):
    # GH 22119
    comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
    vals = np.array([float("nan")], dtype=object)
    expected = np.array([False, False, True])
    result = algos.isin(comps, vals)

  tm.assert_numpy_array_equal(expected, result)

pandas/tests/test_algos.py:1039:

left = array([False, False, True]), right = array([False, False, False])
err_msg = None

def _raise(left, right, err_msg):
    if err_msg is None:
        if left.shape != right.shape:
            raise_assert_detail(
                obj, f"{obj} shapes are different", left.shape, right.shape
            )

        diff = 0
        for left_arr, right_arr in zip(left, right):
            # count up differences
            if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
                diff += 1

        diff = diff * 100.0 / left.size
        msg = f"{obj} values are different ({np.round(diff, 5)} %)"

      raise_assert_detail(obj, msg, left, right, index_values=index_values)

E AssertionError: numpy array are different
E
E numpy array values are different (33.33333 %)
E [left]: [False, False, True]
E [right]: [False, False, False]

pandas/_testing/asserters.py:726: AssertionError

test_hash_equal

_________________________ test_hash_equal[a2-b2-True] __________________________

a = Sparse[float64, nan], b = Sparse[float64, nan], expected = True

@pytest.mark.parametrize(
    "a, b, expected",
    [
        (SparseDtype(float, 0.0), SparseDtype(np.dtype("float"), 0.0), True),
        (SparseDtype(int, 0), SparseDtype(int, 0), True),
        (SparseDtype(float, float("nan")), SparseDtype(float, np.nan), True),
        (SparseDtype(float, 0), SparseDtype(float, np.nan), False),
        (SparseDtype(int, 0.0), SparseDtype(float, 0.0), False),
    ],
)
def test_hash_equal(a, b, expected):
    result = a == b
    assert result is expected

    result = hash(a) == hash(b)

  assert result is expected

E assert False is True

pandas/tests/arrays/sparse/test_dtype.py:141: AssertionError

TestReshaping.test_stack

____________________ TestReshaping.test_stack[nan-columns0] ____________________

self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fc4a6d10>
data = [0.6069529775354311, 0.6532416455715819, nan, 0.45409596635270144, 0.10948849972327801, nan, 0.08987488776518548, 0.19...66, 67, 69, 70, 72, 73, 75,
76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99],
dtype=int32)

columns = ['A', 'B']

@pytest.mark.parametrize(
    "columns",
    [
        ["A", "B"],
        pd.MultiIndex.from_tuples(
            [("A", "a"), ("A", "b")], names=["outer", "inner"]
        ),
    ],
)
def test_stack(self, data, columns):
    df = pd.DataFrame({"A": data[:5], "B": data[:5]})
    df.columns = columns
    result = df.stack()
    expected = df.astype(object).stack()
    # we need a second astype(object), in case the constructor inferred
    # object -> specialized, as is done for period.
    expected = expected.astype(object)

    if isinstance(expected, pd.Series):
        assert result.dtype == df.iloc[:, 0].dtype
    else:
        assert all(result.dtypes == df.iloc[:, 0].dtype)

    result = result.astype(object)

  self.assert_equal(result, expected)

pandas/tests/extension/base/reshaping.py:270:

cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'>
left = 0 A 0.606953
B 0.606953
1 A 0.653242
B 0.653242
3 A 0.454096
B 0.454096
4 A 0.109488
B 0.109488
dtype: Sparse[object, nan]
right = 0 A 0.606953
B 0.606953
1 A 0.653242
B 0.653242
3 A 0.454096
B 0.454096
4 A 0.109488
B 0.109488
dtype: object
kwargs = {}

@classmethod
def assert_equal(cls, left, right, **kwargs):

  return tm.assert_equal(left, right, **kwargs)

E AssertionError: Attributes of Series are different
E
E Attribute "dtype" are different
E [left]: Sparse[object, nan]
E [right]: object

pandas/tests/extension/base/base.py:9: AssertionError

____________________ TestReshaping.test_stack[nan-columns1] ____________________

self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fbea8a30>
data = [0.08642533151098042, 0.31948867044255314, nan, 0.7649119528014422, 0.8562776453950798, nan, 0.0026917407695283835, 0....66, 67, 69, 70, 72, 73, 75,
76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99],
dtype=int32)

columns = MultiIndex([('A', 'a'),
('A', 'b')],
names=['outer', 'inner'])

@pytest.mark.parametrize(
    "columns",
    [
        ["A", "B"],
        pd.MultiIndex.from_tuples(
            [("A", "a"), ("A", "b")], names=["outer", "inner"]
        ),
    ],
)
def test_stack(self, data, columns):
    df = pd.DataFrame({"A": data[:5], "B": data[:5]})
    df.columns = columns
    result = df.stack()
    expected = df.astype(object).stack()
    # we need a second astype(object), in case the constructor inferred
    # object -> specialized, as is done for period.
    expected = expected.astype(object)

    if isinstance(expected, pd.Series):
        assert result.dtype == df.iloc[:, 0].dtype
    else:
        assert all(result.dtypes == df.iloc[:, 0].dtype)

    result = result.astype(object)

  self.assert_equal(result, expected)

pandas/tests/extension/base/reshaping.py:270:

cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'>
left = outer A
inner
0 a 0.086425
b 0.086425
1 a 0.319489
b 0.319489
3 a 0.764912
b 0.764912
4 a 0.856278
b 0.856278
right = outer A
inner
0 a 0.086425
b 0.086425
1 a 0.319489
b 0.319489
3 a 0.764912
b 0.764912
4 a 0.856278
b 0.856278
kwargs = {}

@classmethod
def assert_equal(cls, left, right, **kwargs):

  return tm.assert_equal(left, right, **kwargs)

E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different
E
E Attribute "dtype" are different
E [left]: Sparse[object, nan]
E [right]: object

pandas/tests/extension/base/base.py:9: AssertionError

TestDataFrameSelectReindex.test_reindex_nan

_________________ TestDataFrameSelectReindex.test_reindex_nan __________________

self = <pandas.tests.frame.methods.test_reindex.TestDataFrameSelectReindex object at 0x7fe5f9d03f40>

def test_reindex_nan(self):
    df = DataFrame(
        [[1, 2], [3, 5], [7, 11], [9, 23]],
        index=[2, np.nan, 1, 5],
        columns=["joe", "jim"],
    )

    i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1]
    tm.assert_frame_equal(df.reindex(i), df.iloc[j])

    df.index = df.index.astype("object")

  tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False)

E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="joe") are different
E
E Attribute "dtype" are different
E [left]: float64
E [right]: int64

pandas/tests/frame/methods/test_reindex.py:472: AssertionError

TestIndex.test_isin_nan_common_object

_____________ TestIndex.test_isin_nan_common_object[float0-float1] _____________

self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6a4d930>
nulls_fixture = nan, nulls_fixture2 = nan

def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
    # Test cartesian product of null fixtures and ensure that we don't
    # mangle the various types (save a corner case with PyPy)

    # all nans are the same
    if (
        isinstance(nulls_fixture, float)
        and isinstance(nulls_fixture2, float)
        and math.isnan(nulls_fixture)
        and math.isnan(nulls_fixture2)
    ):

      tm.assert_numpy_array_equal(

            Index(["a", nulls_fixture]).isin([nulls_fixture2]),
            np.array([False, True]),
        )

pandas/tests/indexes/test_base.py:1006:

left = array([False, False]), right = array([False, True]), err_msg = None

def _raise(left, right, err_msg):
    if err_msg is None:
        if left.shape != right.shape:
            raise_assert_detail(
                obj, f"{obj} shapes are different", left.shape, right.shape
            )

        diff = 0
        for left_arr, right_arr in zip(left, right):
            # count up differences
            if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
                diff += 1

        diff = diff * 100.0 / left.size
        msg = f"{obj} values are different ({np.round(diff, 5)} %)"

      raise_assert_detail(obj, msg, left, right, index_values=index_values)

E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]

pandas/_testing/asserters.py:726: AssertionError
_____________ TestIndex.test_isin_nan_common_object[float1-float0] _____________

self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f5e186a0>
nulls_fixture = nan, nulls_fixture2 = nan

def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
    # Test cartesian product of null fixtures and ensure that we don't
    # mangle the various types (save a corner case with PyPy)

    # all nans are the same
    if (
        isinstance(nulls_fixture, float)
        and isinstance(nulls_fixture2, float)
        and math.isnan(nulls_fixture)
        and math.isnan(nulls_fixture2)
    ):

      tm.assert_numpy_array_equal(

            Index(["a", nulls_fixture]).isin([nulls_fixture2]),
            np.array([False, True]),
        )

pandas/tests/indexes/test_base.py:1006:

left = array([False, False]), right = array([False, True]), err_msg = None

def _raise(left, right, err_msg):
    if err_msg is None:
        if left.shape != right.shape:
            raise_assert_detail(
                obj, f"{obj} shapes are different", left.shape, right.shape
            )

        diff = 0
        for left_arr, right_arr in zip(left, right):
            # count up differences
            if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
                diff += 1

        diff = diff * 100.0 / left.size
        msg = f"{obj} values are different ({np.round(diff, 5)} %)"

      raise_assert_detail(obj, msg, left, right, index_values=index_values)

E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]

pandas/_testing/asserters.py:726: AssertionError
_____________ TestIndex.test_isin_nan_common_object[float1-float1] _____________

self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6ab3eb0>
nulls_fixture = nan, nulls_fixture2 = nan

def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
    # Test cartesian product of null fixtures and ensure that we don't
    # mangle the various types (save a corner case with PyPy)

    # all nans are the same
    if (
        isinstance(nulls_fixture, float)
        and isinstance(nulls_fixture2, float)
        and math.isnan(nulls_fixture)
        and math.isnan(nulls_fixture2)
    ):

      tm.assert_numpy_array_equal(

            Index(["a", nulls_fixture]).isin([nulls_fixture2]),
            np.array([False, True]),
        )

pandas/tests/indexes/test_base.py:1006:

left = array([False, False]), right = array([False, True]), err_msg = None

def _raise(left, right, err_msg):
    if err_msg is None:
        if left.shape != right.shape:
            raise_assert_detail(
                obj, f"{obj} shapes are different", left.shape, right.shape
            )

        diff = 0
        for left_arr, right_arr in zip(left, right):
            # count up differences
            if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
                diff += 1

        diff = diff * 100.0 / left.size
        msg = f"{obj} values are different ({np.round(diff, 5)} %)"

      raise_assert_detail(obj, msg, left, right, index_values=index_values)

E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]

pandas/_testing/asserters.py:726: AssertionError

TestUltraJSONTests.test_invalid_double_precision

_____________ TestUltraJSONTests.test_invalid_double_precision[9] ______________

self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780>
invalid_val = '9'

@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
    double_input = 30.12345678901234567890
    expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
    msg = (
        r"Invalid value '.*' for option 'double_precision', max is '15'|"
        r"an integer is required \(got type "
    )
    with pytest.raises(expected_exception, match=msg):

      ujson.encode(double_input, double_precision=invalid_val)

E TypeError: 'str' object cannot be interpreted as an integer

pandas/tests/io/json/test_ujson.py:260: TypeError

During handling of the above exception, another exception occurred:

self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780>
invalid_val = '9'

@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
    double_input = 30.12345678901234567890
    expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
    msg = (
        r"Invalid value '.*' for option 'double_precision', max is '15'|"
        r"an integer is required \(got type "
    )

  with pytest.raises(expected_exception, match=msg):

E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \(got type " does not match "'str' object cannot be interpreted as an integer".

pandas/tests/io/json/test_ujson.py:259: AssertionError
____________ TestUltraJSONTests.test_invalid_double_precision[None] ____________

self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990>
invalid_val = None

@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
    double_input = 30.12345678901234567890
    expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
    msg = (
        r"Invalid value '.*' for option 'double_precision', max is '15'|"
        r"an integer is required \(got type "
    )
    with pytest.raises(expected_exception, match=msg):

      ujson.encode(double_input, double_precision=invalid_val)

E TypeError: 'NoneType' object cannot be interpreted as an integer

pandas/tests/io/json/test_ujson.py:260: TypeError

During handling of the above exception, another exception occurred:

self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990>
invalid_val = None

@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
    double_input = 30.12345678901234567890
    expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
    msg = (
        r"Invalid value '.*' for option 'double_precision', max is '15'|"
        r"an integer is required \(got type "
    )

  with pytest.raises(expected_exception, match=msg):

E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \(got type " does not match "'NoneType' object cannot be interpreted as an integer".

pandas/tests/io/json/test_ujson.py:259: AssertionError

TestGetDummies.test_get_dummies_basic_drop_first_NA

__________ TestGetDummies.test_get_dummies_basic_drop_first_NA[dense] __________

self = <pandas.tests.reshape.test_get_dummies.TestGetDummies object at 0x7fe5e7b1d7b0>
sparse = False

def test_get_dummies_basic_drop_first_NA(self, sparse):
    # Test NA handling together with drop_first
    s_NA = ["a", "b", np.nan]
    res = get_dummies(s_NA, drop_first=True, sparse=sparse)
    exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
    if sparse:
        exp = exp.apply(SparseArray, fill_value=0)

    tm.assert_frame_equal(res, exp)

    res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
    exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
        ["b", np.nan], axis=1
    )
    if sparse:
        exp_na = exp_na.apply(SparseArray, fill_value=0)

  tm.assert_frame_equal(res_na, exp_na)

E AssertionError

pandas/tests/reshape/test_get_dummies.py:485: AssertionError

test_reindex_nan

_______________________________ test_reindex_nan _______________________________

def test_reindex_nan():
    ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8])

    i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2]
    tm.assert_series_equal(ts.reindex(i), ts.iloc[j])

    ts.index = ts.index.astype("object")

    # reindex coerces index.dtype to float, loc/iloc doesn't

  tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False)

E AssertionError: Attributes of Series are different
E
E Attribute "dtype" are different
E [left]: float64
E [right]: int64

test_map_missing_mixed

_________________ test_map_missing_mixed[vals0-mapping0-exp0] __________________ [gw1] linux -- Python 3.10.0 /opt/hostedtoolcache/Python/3.10.0-beta.2/x64/bin/python

vals = ['a', 'b', 'c'], mapping = {nan: 'not NaN'}
exp = [nan, nan, nan, 'not NaN']

@pytest.mark.parametrize(
    "vals,mapping,exp",
    [
        (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
        (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
        (list(range(3)), {0: 42}, [42] + [np.nan] * 3),
    ],
)
def test_map_missing_mixed(vals, mapping, exp):
    # GH20495
    s = Series(vals + [np.nan])
    result = s.map(mapping)

  tm.assert_series_equal(result, Series(exp))

pandas/tests/apply/test_series_apply.py:821:

pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,

raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (25.0 %)
E [index]: [0, 1, 2, 3]
E [left]: [nan, nan, nan, nan]
E [right]: [nan, nan, nan, not NaN]

pandas/_libs/testing.pyx:168: AssertionError

NOTE: This list is not complete since there are also some failing JSON tests that freeze pytest. Will look at those soon.

The text was updated successfully, but these errors were encountered:

simonjayhawkins · 2021-06-11T14:46:56Z

@lithomas1 when we added support for 3.9, we did it in a patch release. So if we need to make changes, we'll probably backport them, so we probably want ci changes on 1.3.x also.

lithomas1 · 2021-06-11T16:23:47Z

@simonjayhawkins Sure, moved right now. I'm just milestoning so we don't forget.

jbrockmendel · 2021-06-12T18:24:06Z

@lithomas1 are you able to reproduce the numpy failures locally xref #41935?

jreback · 2021-06-15T20:59:19Z

@lithomas1 can you update this after merging the patch.

realead · 2021-06-18T21:03:24Z

I cannot test it easily, but my guess is that #41988 had fixed the following examples:

TestUnique.test_first_nan_kept, TestIsin.test_different_nans, TestIsin.test_different_nan_objects, TestDataFrameSelectReindex.test_reindex_nan, TestIndex.test_isin_nan_common_object, TestGetDummies.test_get_dummies_basic_drop_first_NA, test_map_missing_mixed, test_reindex_nan

test_hash_equal isn't fixed but has the same reason for the failure as the above: python/cpython@a07da09.

lithomas1 · 2021-06-18T23:47:24Z

I'll test w/ CI in #41595, but having some difficulties due to #41935.

simonjayhawkins · 2021-07-23T10:33:08Z

@lithomas1 what's the status here? Is is maybe worth opening separate issues for the ones that can be fixed now.

lithomas1 · 2021-07-23T15:42:13Z

just the json tests left i think.

simonjayhawkins · 2021-09-11T09:29:46Z

Moved to 1.3.4. Have currently scheduled 1.3.4 for October 17, 2021 but could release earlier if there are issues with the pandas 1.3.3 py3.10 wheels once Python 3.10.0 final is released.

simonjayhawkins · 2021-10-16T18:46:58Z

just the json tests left i think.

test_tz_is_utc and test_tz_range_is_utc in pandas/tests/io/json/test_pandas.py is covered by #42130

test_read_csv_and_table_sys_setprofile in pandas/tests/io/parser/common/test_common_basic.py is covered by #41935

so that just leaves test_invalid_double_precision in pandas/tests/io/json/test_ujson.py which is linked back to this issue.

changing milestone to 1.3.5 (or could close and open a specific issue for test_invalid_double_precision in pandas/tests/io/json/test_ujson.py or combine with #42130 @lithomas1)

simonjayhawkins · 2021-11-27T11:09:42Z

so that just leaves test_invalid_double_precision in pandas/tests/io/json/test_ujson.py which is linked back to this issue.

removing from 1.3.5 milestone but will leave this issue open while this xfail is still in play on this test.

lithomas1 added Testing pandas testing functions or related to the test suite Python 3.10 labels Jun 11, 2021

lithomas1 added this to the 1.4 milestone Jun 11, 2021

lithomas1 modified the milestones: 1.4, 1.3 Jun 11, 2021

lithomas1 mentioned this issue Jun 14, 2021

CI: troubleshoot py310 build #41990

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4 tasks

realead mentioned this issue Jun 19, 2021

BUG: use hash-function which takes nans correctly into account for ExtensionDtype #42135

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simonjayhawkins modified the milestones: 1.3, 1.3.1 Jun 30, 2021

simonjayhawkins added the Master Tracker High level tracker for similar issues label Jul 23, 2021

simonjayhawkins modified the milestones: 1.3.1, 1.3.2 Jul 24, 2021

simonjayhawkins modified the milestones: 1.3.2, 1.3.3 Aug 15, 2021

simonjayhawkins modified the milestones: 1.3.3, 1.3.4 Sep 11, 2021

simonjayhawkins mentioned this issue Sep 12, 2021

RLS: 1.3.4 #43531

Closed

simonjayhawkins modified the milestones: 1.3.4, 1.3.5 Oct 16, 2021

simonjayhawkins modified the milestones: 1.3.5, Contributions Welcome Nov 27, 2021

simonjayhawkins added IO JSON read_json, to_json, json_normalize and removed Testing pandas testing functions or related to the test suite Master Tracker High level tracker for similar issues labels Nov 27, 2021

lithomas1 mentioned this issue Jan 23, 2022

TST: Update error msg for Python 3.10 #45567

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jreback modified the milestones: Contributions Welcome, 1.5 Jan 23, 2022

jreback closed this as completed in #45567 Jan 23, 2022

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TST: Xfails in Python 3.10 CI #41940

TST: Xfails in Python 3.10 CI #41940

lithomas1 commented Jun 11, 2021 •

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lithomas1 commented Jul 23, 2021

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simonjayhawkins commented Nov 27, 2021

TST: Xfails in Python 3.10 CI #41940

TST: Xfails in Python 3.10 CI #41940

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