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import numpy as np | ||
import pytest | ||
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from pandas.core.arrays.numpy_ import PandasArray | ||
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@pytest.fixture | ||
def allow_in_pandas(monkeypatch): | ||
""" | ||
A monkeypatch to tell pandas to let us in. | ||
By default, passing a PandasArray to an index / series / frame | ||
constructor will unbox that PandasArray to an ndarray, and treat | ||
it as a non-EA column. We don't want people using EAs without | ||
reason. | ||
The mechanism for this is a check against ABCPandasArray | ||
in each constructor. | ||
But, for testing, we need to allow them in pandas. So we patch | ||
the _typ of PandasArray, so that we evade the ABCPandasArray | ||
check. | ||
""" | ||
with monkeypatch.context() as m: | ||
m.setattr(PandasArray, '_typ', 'extension') | ||
yield | ||
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@pytest.fixture | ||
def na_value(): | ||
return np.nan | ||
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@pytest.fixture | ||
def na_cmp(): | ||
def cmp(a, b): | ||
return np.isnan(a) and np.isnan(b) | ||
return cmp |
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""" | ||
Tests for PandasArray with nested data. Users typically won't create | ||
these objects via `pd.array`, but they can show up through `.array` | ||
on a Series with nested data. | ||
We partition these tests into their own file, as many of the base | ||
tests fail, as they aren't appropriate for nested data. It is easier | ||
to have a seperate file with its own data generating fixtures, than | ||
trying to skip based upon the value of a fixture. | ||
""" | ||
import pytest | ||
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import pandas as pd | ||
from pandas.core.arrays.numpy_ import PandasArray, PandasDtype | ||
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from .. import base | ||
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# For NumPy <1.16, np.array([np.nan, (1,)]) raises | ||
# ValueError: setting an array element with a sequence. | ||
np = pytest.importorskip('numpy', minversion='1.16.0') | ||
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@pytest.fixture | ||
def dtype(): | ||
return PandasDtype(np.dtype('object')) | ||
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@pytest.fixture | ||
def data(allow_in_pandas, dtype): | ||
return pd.Series([(i,) for i in range(100)]).array | ||
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@pytest.fixture | ||
def data_missing(allow_in_pandas): | ||
return PandasArray(np.array([np.nan, (1,)])) | ||
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@pytest.fixture | ||
def data_for_sorting(allow_in_pandas): | ||
"""Length-3 array with a known sort order. | ||
This should be three items [B, C, A] with | ||
A < B < C | ||
""" | ||
# Use an empty tuple for first element, then remove, | ||
# to disable np.array's shape inference. | ||
return PandasArray( | ||
np.array([(), (2,), (3,), (1,)])[1:] | ||
) | ||
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@pytest.fixture | ||
def data_missing_for_sorting(allow_in_pandas): | ||
"""Length-3 array with a known sort order. | ||
This should be three items [B, NA, A] with | ||
A < B and NA missing. | ||
""" | ||
return PandasArray( | ||
np.array([(1,), np.nan, (0,)]) | ||
) | ||
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@pytest.fixture | ||
def data_for_grouping(allow_in_pandas): | ||
"""Data for factorization, grouping, and unique tests. | ||
Expected to be like [B, B, NA, NA, A, A, B, C] | ||
Where A < B < C and NA is missing | ||
""" | ||
a, b, c = (1,), (2,), (3,) | ||
return PandasArray(np.array( | ||
[b, b, np.nan, np.nan, a, a, b, c] | ||
)) | ||
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skip_nested = pytest.mark.skip(reason="Skipping for nested PandasArray") | ||
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class BaseNumPyTests(object): | ||
pass | ||
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class TestCasting(BaseNumPyTests, base.BaseCastingTests): | ||
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@skip_nested | ||
def test_astype_str(self, data): | ||
pass | ||
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class TestConstructors(BaseNumPyTests, base.BaseConstructorsTests): | ||
@pytest.mark.skip(reason="We don't register our dtype") | ||
# We don't want to register. This test should probably be split in two. | ||
def test_from_dtype(self, data): | ||
pass | ||
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@skip_nested | ||
def test_array_from_scalars(self, data): | ||
pass | ||
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class TestDtype(BaseNumPyTests, base.BaseDtypeTests): | ||
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@pytest.mark.skip(reason="Incorrect expected.") | ||
# we unsurprisingly clash with a NumPy name. | ||
def test_check_dtype(self, data): | ||
pass | ||
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class TestGetitem(BaseNumPyTests, base.BaseGetitemTests): | ||
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@skip_nested | ||
def test_getitem_scalar(self, data): | ||
pass | ||
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@skip_nested | ||
def test_take_series(self, data): | ||
pass | ||
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class TestGroupby(BaseNumPyTests, base.BaseGroupbyTests): | ||
@skip_nested | ||
def test_groupby_extension_apply(self, data_for_grouping, op): | ||
pass | ||
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class TestInterface(BaseNumPyTests, base.BaseInterfaceTests): | ||
@skip_nested | ||
def test_array_interface(self, data): | ||
# NumPy array shape inference | ||
pass | ||
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class TestMethods(BaseNumPyTests, base.BaseMethodsTests): | ||
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@pytest.mark.skip(reason="TODO: remove?") | ||
def test_value_counts(self, all_data, dropna): | ||
pass | ||
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@pytest.mark.skip(reason="Incorrect expected") | ||
# We have a bool dtype, so the result is an ExtensionArray | ||
# but expected is not | ||
def test_combine_le(self, data_repeated): | ||
super(TestMethods, self).test_combine_le(data_repeated) | ||
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@skip_nested | ||
def test_combine_add(self, data_repeated): | ||
# Not numeric | ||
pass | ||
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@skip_nested | ||
def test_shift_fill_value(self, data): | ||
# np.array shape inference. Shift implementation fails. | ||
super().test_shift_fill_value(data) | ||
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@skip_nested | ||
def test_unique(self, data, box, method): | ||
# Fails creating expected | ||
pass | ||
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@skip_nested | ||
def test_fillna_copy_frame(self, data_missing): | ||
# The "scalar" for this array isn't a scalar. | ||
pass | ||
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@skip_nested | ||
def test_fillna_copy_series(self, data_missing): | ||
# The "scalar" for this array isn't a scalar. | ||
pass | ||
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@skip_nested | ||
def test_hash_pandas_object_works(self, data, as_frame): | ||
# ndarray of tuples not hashable | ||
pass | ||
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@skip_nested | ||
def test_searchsorted(self, data_for_sorting, as_series): | ||
# Test setup fails. | ||
pass | ||
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@skip_nested | ||
def test_where_series(self, data, na_value, as_frame): | ||
# Test setup fails. | ||
pass | ||
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@skip_nested | ||
def test_repeat(self, data, repeats, as_series, use_numpy): | ||
# Fails creating expected | ||
pass | ||
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class TestPrinting(BaseNumPyTests, base.BasePrintingTests): | ||
pass | ||
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class TestMissing(BaseNumPyTests, base.BaseMissingTests): | ||
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@skip_nested | ||
def test_fillna_scalar(self, data_missing): | ||
# Non-scalar "scalar" values. | ||
pass | ||
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@skip_nested | ||
def test_fillna_series_method(self, data_missing, method): | ||
# Non-scalar "scalar" values. | ||
pass | ||
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@skip_nested | ||
def test_fillna_series(self, data_missing): | ||
# Non-scalar "scalar" values. | ||
pass | ||
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@skip_nested | ||
def test_fillna_frame(self, data_missing): | ||
# Non-scalar "scalar" values. | ||
pass | ||
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class TestReshaping(BaseNumPyTests, base.BaseReshapingTests): | ||
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@pytest.mark.skip("Incorrect parent test") | ||
# not actually a mixed concat, since we concat int and int. | ||
def test_concat_mixed_dtypes(self, data): | ||
super(TestReshaping, self).test_concat_mixed_dtypes(data) | ||
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@skip_nested | ||
def test_merge(self, data, na_value): | ||
# Fails creating expected | ||
pass | ||
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@skip_nested | ||
def test_merge_on_extension_array(self, data): | ||
# Fails creating expected | ||
pass | ||
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@skip_nested | ||
def test_merge_on_extension_array_duplicates(self, data): | ||
# Fails creating expected | ||
pass | ||
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class TestSetitem(BaseNumPyTests, base.BaseSetitemTests): | ||
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@skip_nested | ||
def test_setitem_scalar_series(self, data, box_in_series): | ||
pass | ||
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@skip_nested | ||
def test_setitem_sequence(self, data, box_in_series): | ||
pass | ||
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@skip_nested | ||
def test_setitem_sequence_mismatched_length_raises(self, data, as_array): | ||
pass | ||
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@skip_nested | ||
def test_setitem_sequence_broadcasts(self, data, box_in_series): | ||
pass | ||
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@skip_nested | ||
def test_setitem_loc_scalar_mixed(self, data): | ||
pass | ||
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@skip_nested | ||
def test_setitem_loc_scalar_multiple_homogoneous(self, data): | ||
pass | ||
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@skip_nested | ||
def test_setitem_iloc_scalar_mixed(self, data): | ||
pass | ||
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@skip_nested | ||
def test_setitem_iloc_scalar_multiple_homogoneous(self, data): | ||
pass | ||
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@skip_nested | ||
def test_setitem_mask_broadcast(self, data, setter): | ||
pass | ||
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@skip_nested | ||
def test_setitem_scalar_key_sequence_raise(self, data): | ||
pass | ||
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# Skip Arithmetics, NumericReduce, BooleanReduce, Parsing |