424 lines
14 KiB
Python
424 lines
14 KiB
Python
import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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Index,
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Interval,
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IntervalIndex,
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Timedelta,
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Timestamp,
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date_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]), Index([1, 3, 5])),
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(Index([0.0, 1.0, 2.0]), Index([1.0, 2.0, 3.0])),
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(timedelta_range("0 days", periods=3), timedelta_range("1 day", periods=3)),
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(date_range("20170101", periods=3), date_range("20170102", periods=3)),
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(
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date_range("20170101", periods=3, tz="US/Eastern"),
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date_range("20170102", periods=3, tz="US/Eastern"),
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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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class TestAttributes:
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@pytest.mark.parametrize(
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"left, right",
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[
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(0, 1),
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(Timedelta("0 days"), Timedelta("1 day")),
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(Timestamp("2018-01-01"), Timestamp("2018-01-02")),
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(
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Timestamp("2018-01-01", tz="US/Eastern"),
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Timestamp("2018-01-02", tz="US/Eastern"),
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),
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],
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)
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@pytest.mark.parametrize("constructor", [IntervalArray, IntervalIndex])
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def test_is_empty(self, constructor, left, right, closed):
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# GH27219
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tuples = [(left, left), (left, right), np.nan]
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expected = np.array([closed != "both", False, False])
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result = constructor.from_tuples(tuples, closed=closed).is_empty
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tm.assert_numpy_array_equal(result, expected)
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class TestMethods:
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@pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
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def test_set_closed(self, closed, new_closed):
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# GH 21670
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array = IntervalArray.from_breaks(range(10), closed=closed)
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result = array.set_closed(new_closed)
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expected = IntervalArray.from_breaks(range(10), closed=new_closed)
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"other",
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[
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Interval(0, 1, closed="right"),
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IntervalArray.from_breaks([1, 2, 3, 4], closed="right"),
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],
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)
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def test_where_raises(self, other):
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# GH#45768 The IntervalArray methods raises; the Series method coerces
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ser = pd.Series(IntervalArray.from_breaks([1, 2, 3, 4], closed="left"))
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mask = np.array([True, False, True])
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match = "'value.closed' is 'right', expected 'left'."
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with pytest.raises(ValueError, match=match):
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ser.array._where(mask, other)
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res = ser.where(mask, other=other)
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expected = ser.astype(object).where(mask, other)
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tm.assert_series_equal(res, expected)
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def test_shift(self):
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# https://github.com/pandas-dev/pandas/issues/31495, GH#22428, GH#31502
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a = IntervalArray.from_breaks([1, 2, 3])
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result = a.shift()
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# int -> float
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expected = IntervalArray.from_tuples([(np.nan, np.nan), (1.0, 2.0)])
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tm.assert_interval_array_equal(result, expected)
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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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a.shift(1, fill_value=pd.NaT)
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def test_shift_datetime(self):
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# GH#31502, GH#31504
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a = IntervalArray.from_breaks(date_range("2000", periods=4))
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result = a.shift(2)
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expected = a.take([-1, -1, 0], allow_fill=True)
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tm.assert_interval_array_equal(result, expected)
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result = a.shift(-1)
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expected = a.take([1, 2, -1], allow_fill=True)
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tm.assert_interval_array_equal(result, expected)
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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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a.shift(1, fill_value=np.timedelta64("NaT", "ns"))
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class TestSetitem:
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def test_set_na(self, left_right_dtypes):
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left, right = left_right_dtypes
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left = left.copy(deep=True)
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right = right.copy(deep=True)
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result = IntervalArray.from_arrays(left, right)
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if result.dtype.subtype.kind not in ["m", "M"]:
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msg = "'value' should be an interval type, got <.*NaTType'> instead."
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with pytest.raises(TypeError, match=msg):
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result[0] = pd.NaT
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if result.dtype.subtype.kind in ["i", "u"]:
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msg = "Cannot set float NaN to integer-backed IntervalArray"
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# GH#45484 TypeError, not ValueError, matches what we get with
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# non-NA un-holdable value.
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with pytest.raises(TypeError, match=msg):
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result[0] = np.NaN
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return
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result[0] = np.nan
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expected_left = Index([left._na_value] + list(left[1:]))
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expected_right = Index([right._na_value] + list(right[1:]))
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expected = IntervalArray.from_arrays(expected_left, expected_right)
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tm.assert_extension_array_equal(result, expected)
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def test_setitem_mismatched_closed(self):
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arr = IntervalArray.from_breaks(range(4))
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orig = arr.copy()
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other = arr.set_closed("both")
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msg = "'value.closed' is 'both', expected 'right'"
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with pytest.raises(ValueError, match=msg):
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arr[0] = other[0]
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with pytest.raises(ValueError, match=msg):
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arr[:1] = other[:1]
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with pytest.raises(ValueError, match=msg):
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arr[:0] = other[:0]
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with pytest.raises(ValueError, match=msg):
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arr[:] = other[::-1]
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with pytest.raises(ValueError, match=msg):
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arr[:] = list(other[::-1])
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with pytest.raises(ValueError, match=msg):
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arr[:] = other[::-1].astype(object)
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with pytest.raises(ValueError, match=msg):
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arr[:] = other[::-1].astype("category")
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# empty list should be no-op
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arr[:0] = []
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tm.assert_interval_array_equal(arr, orig)
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def test_repr():
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# GH 25022
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arr = IntervalArray.from_tuples([(0, 1), (1, 2)])
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result = repr(arr)
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expected = (
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"<IntervalArray>\n"
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"[(0, 1], (1, 2]]\n"
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"Length: 2, dtype: interval[int64, right]"
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)
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assert result == expected
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class TestReductions:
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def test_min_max_invalid_axis(self, left_right_dtypes):
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left, right = left_right_dtypes
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left = left.copy(deep=True)
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right = right.copy(deep=True)
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arr = IntervalArray.from_arrays(left, right)
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msg = "`axis` must be fewer than the number of dimensions"
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for axis in [-2, 1]:
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with pytest.raises(ValueError, match=msg):
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arr.min(axis=axis)
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with pytest.raises(ValueError, match=msg):
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arr.max(axis=axis)
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msg = "'>=' not supported between"
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with pytest.raises(TypeError, match=msg):
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arr.min(axis="foo")
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with pytest.raises(TypeError, match=msg):
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arr.max(axis="foo")
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def test_min_max(self, left_right_dtypes, index_or_series_or_array):
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# GH#44746
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left, right = left_right_dtypes
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left = left.copy(deep=True)
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right = right.copy(deep=True)
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arr = IntervalArray.from_arrays(left, right)
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# The expected results below are only valid if monotonic
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assert left.is_monotonic_increasing
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assert Index(arr).is_monotonic_increasing
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MIN = arr[0]
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MAX = arr[-1]
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indexer = np.arange(len(arr))
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np.random.shuffle(indexer)
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arr = arr.take(indexer)
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arr_na = arr.insert(2, np.nan)
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arr = index_or_series_or_array(arr)
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arr_na = index_or_series_or_array(arr_na)
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for skipna in [True, False]:
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res = arr.min(skipna=skipna)
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assert res == MIN
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assert type(res) == type(MIN)
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res = arr.max(skipna=skipna)
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assert res == MAX
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assert type(res) == type(MAX)
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res = arr_na.min(skipna=False)
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assert np.isnan(res)
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res = arr_na.max(skipna=False)
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assert np.isnan(res)
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res = arr_na.min(skipna=True)
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assert res == MIN
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assert type(res) == type(MIN)
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res = arr_na.max(skipna=True)
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assert res == MAX
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assert type(res) == type(MAX)
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# ----------------------------------------------------------------------------
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# Arrow interaction
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pyarrow_skip = td.skip_if_no("pyarrow")
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@pyarrow_skip
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def test_arrow_extension_type():
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import pyarrow as pa
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from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
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p1 = ArrowIntervalType(pa.int64(), "left")
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p2 = ArrowIntervalType(pa.int64(), "left")
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p3 = ArrowIntervalType(pa.int64(), "right")
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assert p1.closed == "left"
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assert p1 == p2
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assert p1 != p3
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assert hash(p1) == hash(p2)
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assert hash(p1) != hash(p3)
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@pyarrow_skip
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def test_arrow_array():
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import pyarrow as pa
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from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
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intervals = pd.interval_range(1, 5, freq=1).array
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result = pa.array(intervals)
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assert isinstance(result.type, ArrowIntervalType)
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assert result.type.closed == intervals.closed
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assert result.type.subtype == pa.int64()
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assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64"))
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assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64"))
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expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)])
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assert result.storage.equals(expected)
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# convert to its storage type
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result = pa.array(intervals, type=expected.type)
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assert result.equals(expected)
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# unsupported conversions
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with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
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pa.array(intervals, type="float64")
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with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
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pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left"))
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@pyarrow_skip
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def test_arrow_array_missing():
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import pyarrow as pa
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from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
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arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
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arr[1] = None
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result = pa.array(arr)
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assert isinstance(result.type, ArrowIntervalType)
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assert result.type.closed == arr.closed
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assert result.type.subtype == pa.float64()
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# fields have missing values (not NaN)
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left = pa.array([0.0, None, 2.0], type="float64")
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right = pa.array([1.0, None, 3.0], type="float64")
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assert result.storage.field("left").equals(left)
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assert result.storage.field("right").equals(right)
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# structarray itself also has missing values on the array level
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vals = [
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{"left": 0.0, "right": 1.0},
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{"left": None, "right": None},
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{"left": 2.0, "right": 3.0},
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]
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expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False]))
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assert result.storage.equals(expected)
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@pyarrow_skip
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@pytest.mark.parametrize(
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"breaks",
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[[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
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ids=["float", "datetime64[ns]"],
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)
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def test_arrow_table_roundtrip(breaks):
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import pyarrow as pa
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from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
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arr = IntervalArray.from_breaks(breaks)
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arr[1] = None
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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assert isinstance(table.field("a").type, ArrowIntervalType)
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, pd.IntervalDtype)
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tm.assert_frame_equal(result, df)
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table2 = pa.concat_tables([table, table])
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result = table2.to_pandas()
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expected = pd.concat([df, df], ignore_index=True)
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tm.assert_frame_equal(result, expected)
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# GH-41040
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table = pa.table(
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[pa.chunked_array([], type=table.column(0).type)], schema=table.schema
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)
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result = table.to_pandas()
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tm.assert_frame_equal(result, expected[0:0])
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@pyarrow_skip
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@pytest.mark.parametrize(
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"breaks",
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[[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
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ids=["float", "datetime64[ns]"],
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)
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def test_arrow_table_roundtrip_without_metadata(breaks):
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import pyarrow as pa
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arr = IntervalArray.from_breaks(breaks)
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arr[1] = None
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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# remove the metadata
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table = table.replace_schema_metadata()
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assert table.schema.metadata is None
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, pd.IntervalDtype)
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tm.assert_frame_equal(result, df)
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@pyarrow_skip
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def test_from_arrow_from_raw_struct_array():
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# in case pyarrow lost the Interval extension type (eg on parquet roundtrip
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# with datetime64[ns] subtype, see GH-45881), still allow conversion
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# from arrow to IntervalArray
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import pyarrow as pa
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arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}])
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dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither")
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result = dtype.__from_arrow__(arr)
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expected = IntervalArray.from_breaks(
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np.array([0, 1, 2], dtype="int64"), closed="neither"
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)
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tm.assert_extension_array_equal(result, expected)
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result = dtype.__from_arrow__(pa.chunked_array([arr]))
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("timezone", ["UTC", "US/Pacific", "GMT"])
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def test_interval_index_subtype(timezone, inclusive_endpoints_fixture):
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# GH 46999
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dates = date_range("2022", periods=3, tz=timezone)
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dtype = f"interval[datetime64[ns, {timezone}], {inclusive_endpoints_fixture}]"
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result = IntervalIndex.from_arrays(
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["2022-01-01", "2022-01-02"],
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["2022-01-02", "2022-01-03"],
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closed=inclusive_endpoints_fixture,
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dtype=dtype,
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)
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expected = IntervalIndex.from_arrays(
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dates[:-1], dates[1:], closed=inclusive_endpoints_fixture
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)
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tm.assert_index_equal(result, expected)
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