493 lines
16 KiB
Python
493 lines
16 KiB
Python
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays.numpy_ import PandasArray, PandasDtype
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from . import base
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@pytest.fixture(params=["float", "object"])
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def dtype(request):
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return PandasDtype(np.dtype(request.param))
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@pytest.fixture
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def allow_in_pandas(monkeypatch):
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"""
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A monkeypatch to tells pandas to let us in.
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By default, passing a PandasArray to an index / series / frame
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constructor will unbox that PandasArray to an ndarray, and treat
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it as a non-EA column. We don't want people using EAs without
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reason.
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The mechanism for this is a check against ABCPandasArray
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in each constructor.
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But, for testing, we need to allow them in pandas. So we patch
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the _typ of PandasArray, so that we evade the ABCPandasArray
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check.
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"""
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with monkeypatch.context() as m:
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m.setattr(PandasArray, "_typ", "extension")
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yield
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@pytest.fixture
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def data(allow_in_pandas, dtype):
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if dtype.numpy_dtype == "object":
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return pd.Series([(i,) for i in range(100)]).array
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return PandasArray(np.arange(1, 101, dtype=dtype._dtype))
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@pytest.fixture
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def data_missing(allow_in_pandas, dtype):
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if dtype.numpy_dtype == "object":
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return PandasArray(np.array([np.nan, (1,)], dtype=object))
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return PandasArray(np.array([np.nan, 1.0]))
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@pytest.fixture
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def na_value():
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return np.nan
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@pytest.fixture
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def na_cmp():
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def cmp(a, b):
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return np.isnan(a) and np.isnan(b)
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return cmp
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@pytest.fixture
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def data_for_sorting(allow_in_pandas, dtype):
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"""Length-3 array with a known sort order.
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This should be three items [B, C, A] with
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A < B < C
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"""
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if dtype.numpy_dtype == "object":
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# Use an empty tuple for first element, then remove,
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# to disable np.array's shape inference.
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return PandasArray(np.array([(), (2,), (3,), (1,)], dtype=object)[1:])
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return PandasArray(np.array([1, 2, 0]))
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@pytest.fixture
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def data_missing_for_sorting(allow_in_pandas, dtype):
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"""Length-3 array with a known sort order.
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This should be three items [B, NA, A] with
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A < B and NA missing.
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"""
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if dtype.numpy_dtype == "object":
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return PandasArray(np.array([(1,), np.nan, (0,)], dtype=object))
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return PandasArray(np.array([1, np.nan, 0]))
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@pytest.fixture
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def data_for_grouping(allow_in_pandas, dtype):
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"""Data for factorization, grouping, and unique tests.
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Expected to be like [B, B, NA, NA, A, A, B, C]
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Where A < B < C and NA is missing
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"""
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if dtype.numpy_dtype == "object":
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a, b, c = (1,), (2,), (3,)
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else:
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a, b, c = np.arange(3)
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return PandasArray(
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np.array([b, b, np.nan, np.nan, a, a, b, c], dtype=dtype.numpy_dtype)
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)
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@pytest.fixture
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def skip_numpy_object(dtype):
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"""
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Tests for PandasArray with nested data. Users typically won't create
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these objects via `pd.array`, but they can show up through `.array`
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on a Series with nested data. Many of the base tests fail, as they aren't
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appropriate for nested data.
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This fixture allows these tests to be skipped when used as a usefixtures
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marker to either an individual test or a test class.
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"""
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if dtype == "object":
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raise pytest.skip("Skipping for object dtype.")
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skip_nested = pytest.mark.usefixtures("skip_numpy_object")
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class BaseNumPyTests:
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pass
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class TestCasting(BaseNumPyTests, base.BaseCastingTests):
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@skip_nested
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def test_astype_str(self, data):
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# ValueError: setting an array element with a sequence
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super().test_astype_str(data)
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@skip_nested
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def test_astype_string(self, data):
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# GH-33465
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# ValueError: setting an array element with a sequence
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super().test_astype_string(data)
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class TestConstructors(BaseNumPyTests, base.BaseConstructorsTests):
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@pytest.mark.skip(reason="We don't register our dtype")
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# We don't want to register. This test should probably be split in two.
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def test_from_dtype(self, data):
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pass
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@skip_nested
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def test_array_from_scalars(self, data):
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# ValueError: PandasArray must be 1-dimensional.
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super().test_array_from_scalars(data)
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@skip_nested
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def test_series_constructor_scalar_with_index(self, data, dtype):
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# ValueError: Length of passed values is 1, index implies 3.
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super().test_series_constructor_scalar_with_index(data, dtype)
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class TestDtype(BaseNumPyTests, base.BaseDtypeTests):
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@pytest.mark.skip(reason="Incorrect expected.")
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# we unsurprisingly clash with a NumPy name.
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def test_check_dtype(self, data):
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pass
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class TestGetitem(BaseNumPyTests, base.BaseGetitemTests):
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@skip_nested
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def test_getitem_scalar(self, data):
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# AssertionError
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super().test_getitem_scalar(data)
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@skip_nested
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def test_take_series(self, data):
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# ValueError: PandasArray must be 1-dimensional.
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super().test_take_series(data)
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def test_loc_iloc_frame_single_dtype(self, data, request):
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npdtype = data.dtype.numpy_dtype
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if npdtype == object:
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# GH#33125
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mark = pytest.mark.xfail(
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reason="GH#33125 astype doesn't recognize data.dtype"
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)
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request.node.add_marker(mark)
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super().test_loc_iloc_frame_single_dtype(data)
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class TestGroupby(BaseNumPyTests, base.BaseGroupbyTests):
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@skip_nested
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def test_groupby_extension_apply(
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self, data_for_grouping, groupby_apply_op, request
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):
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super().test_groupby_extension_apply(data_for_grouping, groupby_apply_op)
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class TestInterface(BaseNumPyTests, base.BaseInterfaceTests):
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@skip_nested
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def test_array_interface(self, data):
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# NumPy array shape inference
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super().test_array_interface(data)
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class TestMethods(BaseNumPyTests, base.BaseMethodsTests):
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@pytest.mark.skip(reason="TODO: remove?")
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def test_value_counts(self, all_data, dropna):
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pass
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@pytest.mark.xfail(reason="not working. will be covered by #32028")
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def test_value_counts_with_normalize(self, data):
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return super().test_value_counts_with_normalize(data)
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@pytest.mark.skip(reason="Incorrect expected")
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# We have a bool dtype, so the result is an ExtensionArray
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# but expected is not
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def test_combine_le(self, data_repeated):
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super().test_combine_le(data_repeated)
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@skip_nested
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def test_combine_add(self, data_repeated):
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# Not numeric
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super().test_combine_add(data_repeated)
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@skip_nested
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def test_shift_fill_value(self, data):
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# np.array shape inference. Shift implementation fails.
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super().test_shift_fill_value(data)
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@skip_nested
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@pytest.mark.parametrize("box", [pd.Series, lambda x: x])
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@pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
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def test_unique(self, data, box, method):
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# Fails creating expected
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super().test_unique(data, box, method)
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@skip_nested
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def test_fillna_copy_frame(self, data_missing):
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# The "scalar" for this array isn't a scalar.
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super().test_fillna_copy_frame(data_missing)
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@skip_nested
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def test_fillna_copy_series(self, data_missing):
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# The "scalar" for this array isn't a scalar.
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super().test_fillna_copy_series(data_missing)
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@skip_nested
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def test_hash_pandas_object_works(self, data, as_frame):
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# ndarray of tuples not hashable
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super().test_hash_pandas_object_works(data, as_frame)
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@skip_nested
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def test_searchsorted(self, data_for_sorting, as_series):
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# Test setup fails.
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super().test_searchsorted(data_for_sorting, as_series)
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@skip_nested
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def test_where_series(self, data, na_value, as_frame):
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# Test setup fails.
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super().test_where_series(data, na_value, as_frame)
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@skip_nested
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@pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
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def test_repeat(self, data, repeats, as_series, use_numpy):
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# Fails creating expected
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super().test_repeat(data, repeats, as_series, use_numpy)
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@pytest.mark.xfail(reason="PandasArray.diff may fail on dtype")
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def test_diff(self, data, periods):
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return super().test_diff(data, periods)
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@skip_nested
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@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
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def test_equals(self, data, na_value, as_series, box):
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# Fails creating with _from_sequence
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super().test_equals(data, na_value, as_series, box)
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@skip_nested
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class TestArithmetics(BaseNumPyTests, base.BaseArithmeticOpsTests):
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divmod_exc = None
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series_scalar_exc = None
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frame_scalar_exc = None
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series_array_exc = None
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def test_divmod_series_array(self, data):
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s = pd.Series(data)
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self._check_divmod_op(s, divmod, data, exc=None)
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@pytest.mark.skip("We implement ops")
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def test_error(self, data, all_arithmetic_operators):
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pass
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def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
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super().test_arith_series_with_scalar(data, all_arithmetic_operators)
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def test_arith_series_with_array(self, data, all_arithmetic_operators):
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super().test_arith_series_with_array(data, all_arithmetic_operators)
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class TestPrinting(BaseNumPyTests, base.BasePrintingTests):
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pass
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@skip_nested
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class TestNumericReduce(BaseNumPyTests, base.BaseNumericReduceTests):
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def check_reduce(self, s, op_name, skipna):
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result = getattr(s, op_name)(skipna=skipna)
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# avoid coercing int -> float. Just cast to the actual numpy type.
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expected = getattr(s.astype(s.dtype._dtype), op_name)(skipna=skipna)
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tm.assert_almost_equal(result, expected)
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@skip_nested
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class TestBooleanReduce(BaseNumPyTests, base.BaseBooleanReduceTests):
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pass
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class TestMissing(BaseNumPyTests, base.BaseMissingTests):
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@skip_nested
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def test_fillna_scalar(self, data_missing):
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# Non-scalar "scalar" values.
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super().test_fillna_scalar(data_missing)
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@skip_nested
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def test_fillna_series_method(self, data_missing, fillna_method):
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# Non-scalar "scalar" values.
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super().test_fillna_series_method(data_missing, fillna_method)
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@skip_nested
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def test_fillna_series(self, data_missing):
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# Non-scalar "scalar" values.
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super().test_fillna_series(data_missing)
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@skip_nested
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def test_fillna_frame(self, data_missing):
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# Non-scalar "scalar" values.
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super().test_fillna_frame(data_missing)
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@pytest.mark.skip("Invalid test")
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def test_fillna_fill_other(self, data):
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# inplace update doesn't work correctly with patched extension arrays
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# extract_array returns PandasArray, while dtype is a numpy dtype
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super().test_fillna_fill_other(data_missing)
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class TestReshaping(BaseNumPyTests, base.BaseReshapingTests):
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@pytest.mark.skip("Incorrect parent test")
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# not actually a mixed concat, since we concat int and int.
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def test_concat_mixed_dtypes(self, data):
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super().test_concat_mixed_dtypes(data)
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@pytest.mark.xfail(
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reason="GH#33125 PandasArray.astype does not recognize PandasDtype"
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)
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def test_concat(self, data, in_frame):
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super().test_concat(data, in_frame)
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@pytest.mark.xfail(
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reason="GH#33125 PandasArray.astype does not recognize PandasDtype"
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)
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def test_concat_all_na_block(self, data_missing, in_frame):
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super().test_concat_all_na_block(data_missing, in_frame)
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@skip_nested
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def test_merge(self, data, na_value):
|
||
|
|
# Fails creating expected
|
||
|
|
super().test_merge(data, na_value)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_merge_on_extension_array(self, data):
|
||
|
|
# Fails creating expected
|
||
|
|
super().test_merge_on_extension_array(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_merge_on_extension_array_duplicates(self, data):
|
||
|
|
# Fails creating expected
|
||
|
|
super().test_merge_on_extension_array_duplicates(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_transpose_frame(self, data):
|
||
|
|
super().test_transpose_frame(data)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSetitem(BaseNumPyTests, base.BaseSetitemTests):
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_scalar_series(self, data, box_in_series):
|
||
|
|
# AssertionError
|
||
|
|
super().test_setitem_scalar_series(data, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_sequence(self, data, box_in_series):
|
||
|
|
# ValueError: shape mismatch: value array of shape (2,1) could not
|
||
|
|
# be broadcast to indexing result of shape (2,)
|
||
|
|
super().test_setitem_sequence(data, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
|
||
|
|
# ValueError: PandasArray must be 1-dimensional.
|
||
|
|
super().test_setitem_sequence_mismatched_length_raises(data, as_array)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_sequence_broadcasts(self, data, box_in_series):
|
||
|
|
# ValueError: cannot set using a list-like indexer with a different
|
||
|
|
# length than the value
|
||
|
|
super().test_setitem_sequence_broadcasts(data, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_loc_scalar_mixed(self, data):
|
||
|
|
# AssertionError
|
||
|
|
super().test_setitem_loc_scalar_mixed(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_loc_scalar_multiple_homogoneous(self, data):
|
||
|
|
# AssertionError
|
||
|
|
super().test_setitem_loc_scalar_multiple_homogoneous(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_iloc_scalar_mixed(self, data):
|
||
|
|
# AssertionError
|
||
|
|
super().test_setitem_iloc_scalar_mixed(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
|
||
|
|
# AssertionError
|
||
|
|
super().test_setitem_iloc_scalar_multiple_homogoneous(data)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
@pytest.mark.parametrize("setter", ["loc", None])
|
||
|
|
def test_setitem_mask_broadcast(self, data, setter):
|
||
|
|
# ValueError: cannot set using a list-like indexer with a different
|
||
|
|
# length than the value
|
||
|
|
super().test_setitem_mask_broadcast(data, setter)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_scalar_key_sequence_raise(self, data):
|
||
|
|
# Failed: DID NOT RAISE <class 'ValueError'>
|
||
|
|
super().test_setitem_scalar_key_sequence_raise(data)
|
||
|
|
|
||
|
|
# TODO: there is some issue with PandasArray, therefore,
|
||
|
|
# skip the setitem test for now, and fix it later (GH 31446)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"mask",
|
||
|
|
[
|
||
|
|
np.array([True, True, True, False, False]),
|
||
|
|
pd.array([True, True, True, False, False], dtype="boolean"),
|
||
|
|
],
|
||
|
|
ids=["numpy-array", "boolean-array"],
|
||
|
|
)
|
||
|
|
def test_setitem_mask(self, data, mask, box_in_series):
|
||
|
|
super().test_setitem_mask(data, mask, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_mask_raises(self, data, box_in_series):
|
||
|
|
super().test_setitem_mask_raises(data, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"idx",
|
||
|
|
[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
|
||
|
|
ids=["list", "integer-array", "numpy-array"],
|
||
|
|
)
|
||
|
|
def test_setitem_integer_array(self, data, idx, box_in_series):
|
||
|
|
super().test_setitem_integer_array(data, idx, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"idx, box_in_series",
|
||
|
|
[
|
||
|
|
([0, 1, 2, pd.NA], False),
|
||
|
|
pytest.param([0, 1, 2, pd.NA], True, marks=pytest.mark.xfail),
|
||
|
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
||
|
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
||
|
|
],
|
||
|
|
ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
|
||
|
|
)
|
||
|
|
def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
|
||
|
|
super().test_setitem_integer_with_missing_raises(data, idx, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_slice(self, data, box_in_series):
|
||
|
|
super().test_setitem_slice(data, box_in_series)
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
def test_setitem_loc_iloc_slice(self, data):
|
||
|
|
super().test_setitem_loc_iloc_slice(data)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_nested
|
||
|
|
class TestParsing(BaseNumPyTests, base.BaseParsingTests):
|
||
|
|
pass
|