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124 lines
3.5 KiB
Python
124 lines
3.5 KiB
Python
"""
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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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from __future__ import annotations
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from typing import TYPE_CHECKING
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import numpy as np
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import pytest
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from pandas.core.dtypes.dtypes import IntervalDtype
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from pandas import Interval
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from pandas.core.arrays import IntervalArray
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from pandas.tests.extension import base
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if TYPE_CHECKING:
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import pandas as pd
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def make_data():
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N = 100
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left_array = np.random.default_rng(2).uniform(size=N).cumsum()
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right_array = left_array + np.random.default_rng(2).uniform(size=N)
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return [Interval(left, right) for left, right in zip(left_array, right_array)]
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@pytest.fixture
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def dtype():
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return IntervalDtype()
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@pytest.fixture
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def data():
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"""Length-100 PeriodArray for semantics test."""
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return IntervalArray(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 IntervalArray.from_tuples([None, (0, 1)])
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@pytest.fixture
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def data_for_twos():
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pytest.skip("Interval is not a numeric dtype")
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@pytest.fixture
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def data_for_sorting():
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return IntervalArray.from_tuples([(1, 2), (2, 3), (0, 1)])
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@pytest.fixture
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def data_missing_for_sorting():
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return IntervalArray.from_tuples([(1, 2), None, (0, 1)])
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@pytest.fixture
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def data_for_grouping():
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a = (0, 1)
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b = (1, 2)
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c = (2, 3)
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return IntervalArray.from_tuples([b, b, None, None, a, a, b, c])
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class TestIntervalArray(base.ExtensionTests):
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divmod_exc = TypeError
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def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
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return op_name in ["min", "max"]
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@pytest.mark.xfail(
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reason="Raises with incorrect message bc it disallows *all* listlikes "
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"instead of just wrong-length listlikes"
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)
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def test_fillna_length_mismatch(self, data_missing):
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super().test_fillna_length_mismatch(data_missing)
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@pytest.mark.filterwarnings(
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"ignore:invalid value encountered in cast:RuntimeWarning"
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)
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def test_hash_pandas_object(self, data):
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super().test_hash_pandas_object(data)
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@pytest.mark.filterwarnings(
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"ignore:invalid value encountered in cast:RuntimeWarning"
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)
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def test_hash_pandas_object_works(self, data, as_frame):
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super().test_hash_pandas_object_works(data, as_frame)
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@pytest.mark.filterwarnings(
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"ignore:invalid value encountered in cast:RuntimeWarning"
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)
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@pytest.mark.parametrize("engine", ["c", "python"])
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def test_EA_types(self, engine, data, request):
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super().test_EA_types(engine, data, request)
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@pytest.mark.filterwarnings(
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"ignore:invalid value encountered in cast:RuntimeWarning"
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)
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def test_astype_str(self, data):
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super().test_astype_str(data)
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# TODO: either belongs in tests.arrays.interval or move into base tests.
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def test_fillna_non_scalar_raises(data_missing):
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msg = "can only insert Interval objects and NA into an IntervalArray"
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with pytest.raises(TypeError, match=msg):
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data_missing.fillna([1, 1])
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