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