mirror of
https://github.com/PiBrewing/craftbeerpi4.git
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295 lines
10 KiB
Python
295 lines
10 KiB
Python
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import operator
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_list_like
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import pandas as pd
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from pandas import (
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Categorical,
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Index,
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Interval,
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IntervalIndex,
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Period,
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Series,
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Timedelta,
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Timestamp,
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date_range,
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period_range,
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timedelta_range,
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)
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import pandas._testing as tm
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from pandas.core.arrays import IntervalArray
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@pytest.fixture(
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params=[
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(Index([0, 2, 4, 4]), Index([1, 3, 5, 8])),
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(Index([0.0, 1.0, 2.0, np.nan]), Index([1.0, 2.0, 3.0, np.nan])),
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(
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timedelta_range("0 days", periods=3).insert(4, pd.NaT),
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timedelta_range("1 day", periods=3).insert(4, pd.NaT),
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),
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(
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date_range("20170101", periods=3).insert(4, pd.NaT),
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date_range("20170102", periods=3).insert(4, pd.NaT),
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),
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(
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date_range("20170101", periods=3, tz="US/Eastern").insert(4, pd.NaT),
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date_range("20170102", periods=3, tz="US/Eastern").insert(4, pd.NaT),
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),
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],
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ids=lambda x: str(x[0].dtype),
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)
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def left_right_dtypes(request):
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"""
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Fixture for building an IntervalArray from various dtypes
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"""
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return request.param
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@pytest.fixture
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def array(left_right_dtypes):
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"""
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Fixture to generate an IntervalArray of various dtypes containing NA if possible
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"""
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left, right = left_right_dtypes
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return IntervalArray.from_arrays(left, right)
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def create_categorical_intervals(left, right, closed="right"):
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return Categorical(IntervalIndex.from_arrays(left, right, closed))
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def create_series_intervals(left, right, closed="right"):
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return Series(IntervalArray.from_arrays(left, right, closed))
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def create_series_categorical_intervals(left, right, closed="right"):
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return Series(Categorical(IntervalIndex.from_arrays(left, right, closed)))
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class TestComparison:
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@pytest.fixture(params=[operator.eq, operator.ne])
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def op(self, request):
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return request.param
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@pytest.fixture(
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params=[
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IntervalArray.from_arrays,
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IntervalIndex.from_arrays,
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create_categorical_intervals,
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create_series_intervals,
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create_series_categorical_intervals,
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],
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ids=[
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"IntervalArray",
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"IntervalIndex",
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"Categorical[Interval]",
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"Series[Interval]",
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"Series[Categorical[Interval]]",
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],
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)
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def interval_constructor(self, request):
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"""
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Fixture for all pandas native interval constructors.
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To be used as the LHS of IntervalArray comparisons.
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"""
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return request.param
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def elementwise_comparison(self, op, array, other):
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"""
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Helper that performs elementwise comparisons between `array` and `other`
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"""
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other = other if is_list_like(other) else [other] * len(array)
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return np.array([op(x, y) for x, y in zip(array, other)])
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def test_compare_scalar_interval(self, op, array):
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# matches first interval
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other = array[0]
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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# matches on a single endpoint but not both
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other = Interval(array.left[0], array.right[1])
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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def test_compare_scalar_interval_mixed_closed(self, op, closed, other_closed):
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array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
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other = Interval(0, 1, closed=other_closed)
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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def test_compare_scalar_na(self, op, array, nulls_fixture, request):
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result = op(array, nulls_fixture)
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expected = self.elementwise_comparison(op, array, nulls_fixture)
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if nulls_fixture is pd.NA and array.dtype != pd.IntervalDtype("int64"):
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mark = pytest.mark.xfail(
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reason="broken for non-integer IntervalArray; see GH 31882"
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)
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request.node.add_marker(mark)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize(
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"other",
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[
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0,
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1.0,
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True,
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"foo",
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Timestamp("2017-01-01"),
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Timestamp("2017-01-01", tz="US/Eastern"),
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Timedelta("0 days"),
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Period("2017-01-01", "D"),
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],
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)
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def test_compare_scalar_other(self, op, array, other):
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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def test_compare_list_like_interval(
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self, op, array, interval_constructor,
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):
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# same endpoints
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other = interval_constructor(array.left, array.right)
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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# different endpoints
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other = interval_constructor(array.left[::-1], array.right[::-1])
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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# all nan endpoints
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other = interval_constructor([np.nan] * 4, [np.nan] * 4)
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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def test_compare_list_like_interval_mixed_closed(
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self, op, interval_constructor, closed, other_closed
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):
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array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
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other = interval_constructor(range(2), range(1, 3), closed=other_closed)
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize(
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"other",
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[
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(
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Interval(0, 1),
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Interval(Timedelta("1 day"), Timedelta("2 days")),
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Interval(4, 5, "both"),
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Interval(10, 20, "neither"),
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),
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(0, 1.5, Timestamp("20170103"), np.nan),
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(
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Timestamp("20170102", tz="US/Eastern"),
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Timedelta("2 days"),
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"baz",
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pd.NaT,
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),
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],
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)
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def test_compare_list_like_object(self, op, array, other):
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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def test_compare_list_like_nan(self, op, array, nulls_fixture, request):
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other = [nulls_fixture] * 4
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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if nulls_fixture is pd.NA and array.dtype.subtype != "i8":
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reason = "broken for non-integer IntervalArray; see GH 31882"
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mark = pytest.mark.xfail(reason=reason)
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request.node.add_marker(mark)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize(
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"other",
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[
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np.arange(4, dtype="int64"),
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np.arange(4, dtype="float64"),
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date_range("2017-01-01", periods=4),
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date_range("2017-01-01", periods=4, tz="US/Eastern"),
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timedelta_range("0 days", periods=4),
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period_range("2017-01-01", periods=4, freq="D"),
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Categorical(list("abab")),
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Categorical(date_range("2017-01-01", periods=4)),
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pd.array(list("abcd")),
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pd.array(["foo", 3.14, None, object()]),
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],
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ids=lambda x: str(x.dtype),
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)
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def test_compare_list_like_other(self, op, array, other):
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result = op(array, other)
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expected = self.elementwise_comparison(op, array, other)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("length", [1, 3, 5])
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@pytest.mark.parametrize("other_constructor", [IntervalArray, list])
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def test_compare_length_mismatch_errors(self, op, other_constructor, length):
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array = IntervalArray.from_arrays(range(4), range(1, 5))
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other = other_constructor([Interval(0, 1)] * length)
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with pytest.raises(ValueError, match="Lengths must match to compare"):
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op(array, other)
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@pytest.mark.parametrize(
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"constructor, expected_type, assert_func",
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[
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(IntervalIndex, np.array, tm.assert_numpy_array_equal),
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(Series, Series, tm.assert_series_equal),
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],
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)
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def test_index_series_compat(self, op, constructor, expected_type, assert_func):
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# IntervalIndex/Series that rely on IntervalArray for comparisons
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breaks = range(4)
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index = constructor(IntervalIndex.from_breaks(breaks))
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# scalar comparisons
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other = index[0]
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result = op(index, other)
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expected = expected_type(self.elementwise_comparison(op, index, other))
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assert_func(result, expected)
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other = breaks[0]
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result = op(index, other)
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expected = expected_type(self.elementwise_comparison(op, index, other))
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assert_func(result, expected)
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# list-like comparisons
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other = IntervalArray.from_breaks(breaks)
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result = op(index, other)
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expected = expected_type(self.elementwise_comparison(op, index, other))
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assert_func(result, expected)
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other = [index[0], breaks[0], "foo"]
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result = op(index, other)
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expected = expected_type(self.elementwise_comparison(op, index, other))
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assert_func(result, expected)
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@pytest.mark.parametrize("scalars", ["a", False, 1, 1.0, None])
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def test_comparison_operations(self, scalars):
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# GH #28981
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expected = Series([False, False])
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s = pd.Series([pd.Interval(0, 1), pd.Interval(1, 2)], dtype="interval")
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result = s == scalars
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tm.assert_series_equal(result, expected)
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