311 lines
9.3 KiB
Python
311 lines
9.3 KiB
Python
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"""
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This file contains a minimal set of tests for compliance with the extension
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array interface test suite, and should contain no other tests.
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The test suite for the full functionality of the array is located in
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`pandas/tests/arrays/`.
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The tests in this file are inherited from the BaseExtensionTests, and only
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minimal tweaks should be applied to get the tests passing (by overwriting a
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parent method).
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Additional tests should either be added to one of the BaseExtensionTests
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classes (if they are relevant for the extension interface for all dtypes), or
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be added to the array-specific tests in `pandas/tests/arrays/`.
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"""
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import string
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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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from pandas import (
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Categorical,
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CategoricalIndex,
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Timestamp,
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)
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import pandas._testing as tm
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from pandas.api.types import CategoricalDtype
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from pandas.tests.extension import base
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def make_data():
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while True:
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values = np.random.choice(list(string.ascii_letters), size=100)
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# ensure we meet the requirements
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# 1. first two not null
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# 2. first and second are different
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if values[0] != values[1]:
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break
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return values
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@pytest.fixture
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def dtype():
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return CategoricalDtype()
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@pytest.fixture
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def data():
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"""Length-100 array for this type.
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* data[0] and data[1] should both be non missing
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* data[0] and data[1] should not be equal
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"""
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return Categorical(make_data())
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@pytest.fixture
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def data_missing():
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"""Length 2 array with [NA, Valid]"""
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return Categorical([np.nan, "A"])
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@pytest.fixture
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def data_for_sorting():
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return Categorical(["A", "B", "C"], categories=["C", "A", "B"], ordered=True)
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@pytest.fixture
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def data_missing_for_sorting():
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return Categorical(["A", None, "B"], categories=["B", "A"], ordered=True)
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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 data_for_grouping():
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return Categorical(["a", "a", None, None, "b", "b", "a", "c"])
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class TestDtype(base.BaseDtypeTests):
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pass
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class TestInterface(base.BaseInterfaceTests):
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@pytest.mark.xfail(reason="Memory usage doesn't match")
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def test_memory_usage(self, data):
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# Is this deliberate?
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super().test_memory_usage(data)
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def test_contains(self, data, data_missing):
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# GH-37867
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# na value handling in Categorical.__contains__ is deprecated.
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# See base.BaseInterFaceTests.test_contains for more details.
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na_value = data.dtype.na_value
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# ensure data without missing values
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data = data[~data.isna()]
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# first elements are non-missing
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assert data[0] in data
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assert data_missing[0] in data_missing
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# check the presence of na_value
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assert na_value in data_missing
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assert na_value not in data
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# Categoricals can contain other nan-likes than na_value
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for na_value_obj in tm.NULL_OBJECTS:
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if na_value_obj is na_value:
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continue
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assert na_value_obj not in data
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assert na_value_obj in data_missing # this line differs from super method
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class TestConstructors(base.BaseConstructorsTests):
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def test_empty(self, dtype):
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cls = dtype.construct_array_type()
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result = cls._empty((4,), dtype=dtype)
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assert isinstance(result, cls)
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# the dtype we passed is not initialized, so will not match the
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# dtype on our result.
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assert result.dtype == CategoricalDtype([])
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class TestReshaping(base.BaseReshapingTests):
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pass
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class TestGetitem(base.BaseGetitemTests):
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@pytest.mark.skip(reason="Backwards compatibility")
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def test_getitem_scalar(self, data):
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# CategoricalDtype.type isn't "correct" since it should
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# be a parent of the elements (object). But don't want
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# to break things by changing.
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super().test_getitem_scalar(data)
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class TestSetitem(base.BaseSetitemTests):
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pass
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class TestIndex(base.BaseIndexTests):
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pass
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class TestMissing(base.BaseMissingTests):
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pass
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class TestReduce(base.BaseNoReduceTests):
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pass
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class TestMethods(base.BaseMethodsTests):
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@pytest.mark.xfail(reason="Unobserved categories included")
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def test_value_counts(self, all_data, dropna):
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return super().test_value_counts(all_data, dropna)
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def test_combine_add(self, data_repeated):
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# GH 20825
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# When adding categoricals in combine, result is a string
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orig_data1, orig_data2 = data_repeated(2)
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s1 = pd.Series(orig_data1)
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s2 = pd.Series(orig_data2)
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result = s1.combine(s2, lambda x1, x2: x1 + x2)
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expected = pd.Series(
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[a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
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)
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self.assert_series_equal(result, expected)
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val = s1.iloc[0]
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result = s1.combine(val, lambda x1, x2: x1 + x2)
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expected = pd.Series([a + val for a in list(orig_data1)])
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self.assert_series_equal(result, expected)
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class TestCasting(base.BaseCastingTests):
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@pytest.mark.parametrize("cls", [Categorical, CategoricalIndex])
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@pytest.mark.parametrize("values", [[1, np.nan], [Timestamp("2000"), pd.NaT]])
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def test_cast_nan_to_int(self, cls, values):
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# GH 28406
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s = cls(values)
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msg = "Cannot (cast|convert)"
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with pytest.raises((ValueError, TypeError), match=msg):
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s.astype(int)
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@pytest.mark.parametrize(
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"expected",
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[
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pd.Series(["2019", "2020"], dtype="datetime64[ns, UTC]"),
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pd.Series([0, 0], dtype="timedelta64[ns]"),
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pd.Series([pd.Period("2019"), pd.Period("2020")], dtype="period[A-DEC]"),
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pd.Series([pd.Interval(0, 1), pd.Interval(1, 2)], dtype="interval"),
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pd.Series([1, np.nan], dtype="Int64"),
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],
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)
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def test_cast_category_to_extension_dtype(self, expected):
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# GH 28668
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result = expected.astype("category").astype(expected.dtype)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype, expected",
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[
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(
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"datetime64[ns]",
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np.array(["2015-01-01T00:00:00.000000000"], dtype="datetime64[ns]"),
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),
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(
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"datetime64[ns, MET]",
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pd.DatetimeIndex(
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[Timestamp("2015-01-01 00:00:00+0100", tz="MET")]
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).array,
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),
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],
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)
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def test_consistent_casting(self, dtype, expected):
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# GH 28448
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result = Categorical(["2015-01-01"]).astype(dtype)
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assert result == expected
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class TestArithmeticOps(base.BaseArithmeticOpsTests):
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def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
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# frame & scalar
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op_name = all_arithmetic_operators
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if op_name == "__rmod__":
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request.node.add_marker(
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pytest.mark.xfail(
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reason="rmod never called when string is first argument"
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)
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)
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super().test_arith_frame_with_scalar(data, op_name)
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def test_arith_series_with_scalar(self, data, all_arithmetic_operators, request):
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op_name = all_arithmetic_operators
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if op_name == "__rmod__":
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request.node.add_marker(
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pytest.mark.xfail(
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reason="rmod never called when string is first argument"
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)
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)
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super().test_arith_series_with_scalar(data, op_name)
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def test_add_series_with_extension_array(self, data):
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ser = pd.Series(data)
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with pytest.raises(TypeError, match="cannot perform|unsupported operand"):
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ser + data
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def test_divmod_series_array(self):
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# GH 23287
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# skipping because it is not implemented
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pass
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def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
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return super()._check_divmod_op(s, op, other, exc=TypeError)
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class TestComparisonOps(base.BaseComparisonOpsTests):
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def _compare_other(self, s, data, op, other):
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op_name = f"__{op.__name__}__"
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if op_name == "__eq__":
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result = op(s, other)
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expected = s.combine(other, lambda x, y: x == y)
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assert (result == expected).all()
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elif op_name == "__ne__":
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result = op(s, other)
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expected = s.combine(other, lambda x, y: x != y)
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assert (result == expected).all()
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else:
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msg = "Unordered Categoricals can only compare equality or not"
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with pytest.raises(TypeError, match=msg):
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op(data, other)
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@pytest.mark.parametrize(
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"categories",
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[["a", "b"], [0, 1], [Timestamp("2019"), Timestamp("2020")]],
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)
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def test_not_equal_with_na(self, categories):
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# https://github.com/pandas-dev/pandas/issues/32276
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c1 = Categorical.from_codes([-1, 0], categories=categories)
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c2 = Categorical.from_codes([0, 1], categories=categories)
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result = c1 != c2
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assert result.all()
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class TestParsing(base.BaseParsingTests):
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pass
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class Test2DCompat(base.NDArrayBacked2DTests):
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def test_repr_2d(self, data):
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# Categorical __repr__ doesn't include "Categorical", so we need
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# to special-case
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res = repr(data.reshape(1, -1))
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assert res.count("\nCategories") == 1
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res = repr(data.reshape(-1, 1))
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assert res.count("\nCategories") == 1
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