from itertools import permutations
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import re
|
|
import numpy as np
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import pytest
|
|
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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interval_range,
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isna,
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notna,
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timedelta_range,
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)
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import pandas._testing as tm
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import pandas.core.common as com
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|
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@pytest.fixture(params=[None, "foo"])
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def name(request):
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return request.param
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|
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class TestIntervalIndex:
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index = IntervalIndex.from_arrays([0, 1], [1, 2])
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def create_index(self, closed="right"):
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return IntervalIndex.from_breaks(range(11), closed=closed)
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def create_index_with_nan(self, closed="right"):
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mask = [True, False] + [True] * 8
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return IntervalIndex.from_arrays(
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np.where(mask, np.arange(10), np.nan),
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np.where(mask, np.arange(1, 11), np.nan),
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closed=closed,
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)
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def test_properties(self, closed):
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index = self.create_index(closed=closed)
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assert len(index) == 10
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assert index.size == 10
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assert index.shape == (10,)
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tm.assert_index_equal(index.left, Index(np.arange(10, dtype=np.int64)))
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tm.assert_index_equal(index.right, Index(np.arange(1, 11, dtype=np.int64)))
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tm.assert_index_equal(index.mid, Index(np.arange(0.5, 10.5, dtype=np.float64)))
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assert index.closed == closed
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ivs = [
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Interval(left, right, closed)
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for left, right in zip(range(10), range(1, 11))
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]
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expected = np.array(ivs, dtype=object)
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tm.assert_numpy_array_equal(np.asarray(index), expected)
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# with nans
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index = self.create_index_with_nan(closed=closed)
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assert len(index) == 10
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assert index.size == 10
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assert index.shape == (10,)
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expected_left = Index([0, np.nan, 2, 3, 4, 5, 6, 7, 8, 9])
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expected_right = expected_left + 1
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expected_mid = expected_left + 0.5
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tm.assert_index_equal(index.left, expected_left)
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tm.assert_index_equal(index.right, expected_right)
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tm.assert_index_equal(index.mid, expected_mid)
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assert index.closed == closed
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ivs = [
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Interval(left, right, closed) if notna(left) else np.nan
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for left, right in zip(expected_left, expected_right)
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]
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expected = np.array(ivs, dtype=object)
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tm.assert_numpy_array_equal(np.asarray(index), expected)
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@pytest.mark.parametrize(
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"breaks",
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[
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[1, 1, 2, 5, 15, 53, 217, 1014, 5335, 31240, 201608],
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[-np.inf, -100, -10, 0.5, 1, 1.5, 3.8, 101, 202, np.inf],
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pd.to_datetime(["20170101", "20170202", "20170303", "20170404"]),
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pd.to_timedelta(["1ns", "2ms", "3s", "4min", "5H", "6D"]),
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],
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)
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def test_length(self, closed, breaks):
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# GH 18789
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index = IntervalIndex.from_breaks(breaks, closed=closed)
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result = index.length
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expected = Index(iv.length for iv in index)
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tm.assert_index_equal(result, expected)
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# with NA
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index = index.insert(1, np.nan)
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result = index.length
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expected = Index(iv.length if notna(iv) else iv for iv in index)
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tm.assert_index_equal(result, expected)
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def test_with_nans(self, closed):
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index = self.create_index(closed=closed)
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assert index.hasnans is False
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result = index.isna()
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expected = np.zeros(len(index), dtype=bool)
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tm.assert_numpy_array_equal(result, expected)
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result = index.notna()
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expected = np.ones(len(index), dtype=bool)
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tm.assert_numpy_array_equal(result, expected)
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index = self.create_index_with_nan(closed=closed)
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assert index.hasnans is True
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result = index.isna()
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expected = np.array([False, True] + [False] * (len(index) - 2))
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tm.assert_numpy_array_equal(result, expected)
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result = index.notna()
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expected = np.array([True, False] + [True] * (len(index) - 2))
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tm.assert_numpy_array_equal(result, expected)
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def test_copy(self, closed):
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expected = self.create_index(closed=closed)
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result = expected.copy()
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assert result.equals(expected)
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result = expected.copy(deep=True)
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assert result.equals(expected)
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assert result.left is not expected.left
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def test_ensure_copied_data(self, closed):
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# exercise the copy flag in the constructor
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# not copying
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index = self.create_index(closed=closed)
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result = IntervalIndex(index, copy=False)
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tm.assert_numpy_array_equal(
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index.left.values, result.left.values, check_same="same"
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)
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tm.assert_numpy_array_equal(
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index.right.values, result.right.values, check_same="same"
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)
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# by-definition make a copy
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result = IntervalIndex(np.array(index), copy=False)
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tm.assert_numpy_array_equal(
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index.left.values, result.left.values, check_same="copy"
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)
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tm.assert_numpy_array_equal(
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index.right.values, result.right.values, check_same="copy"
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)
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def test_delete(self, closed):
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breaks = np.arange(1, 11, dtype=np.int64)
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expected = IntervalIndex.from_breaks(breaks, closed=closed)
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result = self.create_index(closed=closed).delete(0)
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"data",
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[
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interval_range(0, periods=10, closed="neither"),
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interval_range(1.7, periods=8, freq=2.5, closed="both"),
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interval_range(Timestamp("20170101"), periods=12, closed="left"),
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interval_range(Timedelta("1 day"), periods=6, closed="right"),
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],
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)
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def test_insert(self, data):
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item = data[0]
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idx_item = IntervalIndex([item])
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# start
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expected = idx_item.append(data)
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result = data.insert(0, item)
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tm.assert_index_equal(result, expected)
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# end
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expected = data.append(idx_item)
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result = data.insert(len(data), item)
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tm.assert_index_equal(result, expected)
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# mid
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expected = data[:3].append(idx_item).append(data[3:])
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result = data.insert(3, item)
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tm.assert_index_equal(result, expected)
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# invalid type
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res = data.insert(1, "foo")
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expected = data.astype(object).insert(1, "foo")
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tm.assert_index_equal(res, 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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data._data.insert(1, "foo")
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# invalid closed
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msg = "'value.closed' is 'left', expected 'right'."
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for closed in {"left", "right", "both", "neither"} - {item.closed}:
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msg = f"'value.closed' is '{closed}', expected '{item.closed}'."
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bad_item = Interval(item.left, item.right, closed=closed)
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res = data.insert(1, bad_item)
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expected = data.astype(object).insert(1, bad_item)
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tm.assert_index_equal(res, expected)
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with pytest.raises(ValueError, match=msg):
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data._data.insert(1, bad_item)
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# GH 18295 (test missing)
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na_idx = IntervalIndex([np.nan], closed=data.closed)
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for na in [np.nan, None, pd.NA]:
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expected = data[:1].append(na_idx).append(data[1:])
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result = data.insert(1, na)
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tm.assert_index_equal(result, expected)
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if data.left.dtype.kind not in ["m", "M"]:
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# trying to insert pd.NaT into a numeric-dtyped Index should cast
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expected = data.astype(object).insert(1, pd.NaT)
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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._data.insert(1, pd.NaT)
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result = data.insert(1, pd.NaT)
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tm.assert_index_equal(result, expected)
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def test_is_unique_interval(self, closed):
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"""
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Interval specific tests for is_unique in addition to base class tests
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"""
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# unique overlapping - distinct endpoints
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idx = IntervalIndex.from_tuples([(0, 1), (0.5, 1.5)], closed=closed)
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assert idx.is_unique is True
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# unique overlapping - shared endpoints
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idx = IntervalIndex.from_tuples([(1, 2), (1, 3), (2, 3)], closed=closed)
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assert idx.is_unique is True
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# unique nested
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idx = IntervalIndex.from_tuples([(-1, 1), (-2, 2)], closed=closed)
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assert idx.is_unique is True
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# unique NaN
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idx = IntervalIndex.from_tuples([(np.NaN, np.NaN)], closed=closed)
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assert idx.is_unique is True
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# non-unique NaN
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idx = IntervalIndex.from_tuples(
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[(np.NaN, np.NaN), (np.NaN, np.NaN)], closed=closed
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)
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assert idx.is_unique is False
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def test_monotonic(self, closed):
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# increasing non-overlapping
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idx = IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)], closed=closed)
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assert idx.is_monotonic_increasing is True
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assert idx._is_strictly_monotonic_increasing is True
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assert idx.is_monotonic_decreasing is False
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assert idx._is_strictly_monotonic_decreasing is False
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# decreasing non-overlapping
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idx = IntervalIndex.from_tuples([(4, 5), (2, 3), (1, 2)], closed=closed)
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assert idx.is_monotonic_increasing is False
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is True
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assert idx._is_strictly_monotonic_decreasing is True
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# unordered non-overlapping
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idx = IntervalIndex.from_tuples([(0, 1), (4, 5), (2, 3)], closed=closed)
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assert idx.is_monotonic_increasing is False
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is False
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assert idx._is_strictly_monotonic_decreasing is False
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# increasing overlapping
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idx = IntervalIndex.from_tuples([(0, 2), (0.5, 2.5), (1, 3)], closed=closed)
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assert idx.is_monotonic_increasing is True
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assert idx._is_strictly_monotonic_increasing is True
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assert idx.is_monotonic_decreasing is False
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assert idx._is_strictly_monotonic_decreasing is False
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# decreasing overlapping
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idx = IntervalIndex.from_tuples([(1, 3), (0.5, 2.5), (0, 2)], closed=closed)
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assert idx.is_monotonic_increasing is False
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is True
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assert idx._is_strictly_monotonic_decreasing is True
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# unordered overlapping
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idx = IntervalIndex.from_tuples([(0.5, 2.5), (0, 2), (1, 3)], closed=closed)
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assert idx.is_monotonic_increasing is False
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is False
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assert idx._is_strictly_monotonic_decreasing is False
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# increasing overlapping shared endpoints
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idx = IntervalIndex.from_tuples([(1, 2), (1, 3), (2, 3)], closed=closed)
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assert idx.is_monotonic_increasing is True
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assert idx._is_strictly_monotonic_increasing is True
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assert idx.is_monotonic_decreasing is False
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assert idx._is_strictly_monotonic_decreasing is False
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# decreasing overlapping shared endpoints
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idx = IntervalIndex.from_tuples([(2, 3), (1, 3), (1, 2)], closed=closed)
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assert idx.is_monotonic_increasing is False
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is True
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assert idx._is_strictly_monotonic_decreasing is True
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# stationary
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idx = IntervalIndex.from_tuples([(0, 1), (0, 1)], closed=closed)
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assert idx.is_monotonic_increasing is True
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assert idx._is_strictly_monotonic_increasing is False
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assert idx.is_monotonic_decreasing is True
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assert idx._is_strictly_monotonic_decreasing is False
|
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# empty
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idx = IntervalIndex([], closed=closed)
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assert idx.is_monotonic_increasing is True
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assert idx._is_strictly_monotonic_increasing is True
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assert idx.is_monotonic_decreasing is True
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assert idx._is_strictly_monotonic_decreasing is True
|
|
def test_is_monotonic_with_nans(self):
|
# GH#41831
|
index = IntervalIndex([np.nan, np.nan])
|
|
assert not index.is_monotonic_increasing
|
assert not index._is_strictly_monotonic_increasing
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assert not index.is_monotonic_increasing
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assert not index._is_strictly_monotonic_decreasing
|
assert not index.is_monotonic_decreasing
|
|
def test_get_item(self, closed):
|
i = IntervalIndex.from_arrays((0, 1, np.nan), (1, 2, np.nan), closed=closed)
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assert i[0] == Interval(0.0, 1.0, closed=closed)
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assert i[1] == Interval(1.0, 2.0, closed=closed)
|
assert isna(i[2])
|
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result = i[0:1]
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expected = IntervalIndex.from_arrays((0.0,), (1.0,), closed=closed)
|
tm.assert_index_equal(result, expected)
|
|
result = i[0:2]
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expected = IntervalIndex.from_arrays((0.0, 1), (1.0, 2.0), closed=closed)
|
tm.assert_index_equal(result, expected)
|
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result = i[1:3]
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expected = IntervalIndex.from_arrays(
|
(1.0, np.nan), (2.0, np.nan), closed=closed
|
)
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tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"breaks",
|
[
|
date_range("20180101", periods=4),
|
date_range("20180101", periods=4, tz="US/Eastern"),
|
timedelta_range("0 days", periods=4),
|
],
|
ids=lambda x: str(x.dtype),
|
)
|
def test_maybe_convert_i8(self, breaks):
|
# GH 20636
|
index = IntervalIndex.from_breaks(breaks)
|
|
# intervalindex
|
result = index._maybe_convert_i8(index)
|
expected = IntervalIndex.from_breaks(breaks.asi8)
|
tm.assert_index_equal(result, expected)
|
|
# interval
|
interval = Interval(breaks[0], breaks[1])
|
result = index._maybe_convert_i8(interval)
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expected = Interval(breaks[0]._value, breaks[1]._value)
|
assert result == expected
|
|
# datetimelike index
|
result = index._maybe_convert_i8(breaks)
|
expected = Index(breaks.asi8)
|
tm.assert_index_equal(result, expected)
|
|
# datetimelike scalar
|
result = index._maybe_convert_i8(breaks[0])
|
expected = breaks[0]._value
|
assert result == expected
|
|
# list-like of datetimelike scalars
|
result = index._maybe_convert_i8(list(breaks))
|
expected = Index(breaks.asi8)
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"breaks",
|
[date_range("2018-01-01", periods=5), timedelta_range("0 days", periods=5)],
|
)
|
def test_maybe_convert_i8_nat(self, breaks):
|
# GH 20636
|
index = IntervalIndex.from_breaks(breaks)
|
|
to_convert = breaks._constructor([pd.NaT] * 3)
|
expected = Index([np.nan] * 3, dtype=np.float64)
|
result = index._maybe_convert_i8(to_convert)
|
tm.assert_index_equal(result, expected)
|
|
to_convert = to_convert.insert(0, breaks[0])
|
expected = expected.insert(0, float(breaks[0]._value))
|
result = index._maybe_convert_i8(to_convert)
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"make_key",
|
[lambda breaks: breaks, list],
|
ids=["lambda", "list"],
|
)
|
def test_maybe_convert_i8_numeric(self, make_key, any_real_numpy_dtype):
|
# GH 20636
|
breaks = np.arange(5, dtype=any_real_numpy_dtype)
|
index = IntervalIndex.from_breaks(breaks)
|
key = make_key(breaks)
|
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result = index._maybe_convert_i8(key)
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kind = breaks.dtype.kind
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expected_dtype = {"i": np.int64, "u": np.uint64, "f": np.float64}[kind]
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expected = Index(key, dtype=expected_dtype)
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tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"make_key",
|
[
|
IntervalIndex.from_breaks,
|
lambda breaks: Interval(breaks[0], breaks[1]),
|
lambda breaks: breaks[0],
|
],
|
ids=["IntervalIndex", "Interval", "scalar"],
|
)
|
def test_maybe_convert_i8_numeric_identical(self, make_key, any_real_numpy_dtype):
|
# GH 20636
|
breaks = np.arange(5, dtype=any_real_numpy_dtype)
|
index = IntervalIndex.from_breaks(breaks)
|
key = make_key(breaks)
|
|
# test if _maybe_convert_i8 won't change key if an Interval or IntervalIndex
|
result = index._maybe_convert_i8(key)
|
assert result is key
|
|
@pytest.mark.parametrize(
|
"breaks1, breaks2",
|
permutations(
|
[
|
date_range("20180101", periods=4),
|
date_range("20180101", periods=4, tz="US/Eastern"),
|
timedelta_range("0 days", periods=4),
|
],
|
2,
|
),
|
ids=lambda x: str(x.dtype),
|
)
|
@pytest.mark.parametrize(
|
"make_key",
|
[
|
IntervalIndex.from_breaks,
|
lambda breaks: Interval(breaks[0], breaks[1]),
|
lambda breaks: breaks,
|
lambda breaks: breaks[0],
|
list,
|
],
|
ids=["IntervalIndex", "Interval", "Index", "scalar", "list"],
|
)
|
def test_maybe_convert_i8_errors(self, breaks1, breaks2, make_key):
|
# GH 20636
|
index = IntervalIndex.from_breaks(breaks1)
|
key = make_key(breaks2)
|
|
msg = (
|
f"Cannot index an IntervalIndex of subtype {breaks1.dtype} with "
|
f"values of dtype {breaks2.dtype}"
|
)
|
msg = re.escape(msg)
|
with pytest.raises(ValueError, match=msg):
|
index._maybe_convert_i8(key)
|
|
def test_contains_method(self):
|
# can select values that are IN the range of a value
|
i = IntervalIndex.from_arrays([0, 1], [1, 2])
|
|
expected = np.array([False, False], dtype="bool")
|
actual = i.contains(0)
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = i.contains(3)
|
tm.assert_numpy_array_equal(actual, expected)
|
|
expected = np.array([True, False], dtype="bool")
|
actual = i.contains(0.5)
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = i.contains(1)
|
tm.assert_numpy_array_equal(actual, expected)
|
|
# __contains__ not implemented for "interval in interval", follow
|
# that for the contains method for now
|
with pytest.raises(
|
NotImplementedError, match="contains not implemented for two"
|
):
|
i.contains(Interval(0, 1))
|
|
def test_dropna(self, closed):
|
expected = IntervalIndex.from_tuples([(0.0, 1.0), (1.0, 2.0)], closed=closed)
|
|
ii = IntervalIndex.from_tuples([(0, 1), (1, 2), np.nan], closed=closed)
|
result = ii.dropna()
|
tm.assert_index_equal(result, expected)
|
|
ii = IntervalIndex.from_arrays([0, 1, np.nan], [1, 2, np.nan], closed=closed)
|
result = ii.dropna()
|
tm.assert_index_equal(result, expected)
|
|
def test_non_contiguous(self, closed):
|
index = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)
|
target = [0.5, 1.5, 2.5]
|
actual = index.get_indexer(target)
|
expected = np.array([0, -1, 1], dtype="intp")
|
tm.assert_numpy_array_equal(actual, expected)
|
|
assert 1.5 not in index
|
|
def test_isin(self, closed):
|
index = self.create_index(closed=closed)
|
|
expected = np.array([True] + [False] * (len(index) - 1))
|
result = index.isin(index[:1])
|
tm.assert_numpy_array_equal(result, expected)
|
|
result = index.isin([index[0]])
|
tm.assert_numpy_array_equal(result, expected)
|
|
other = IntervalIndex.from_breaks(np.arange(-2, 10), closed=closed)
|
expected = np.array([True] * (len(index) - 1) + [False])
|
result = index.isin(other)
|
tm.assert_numpy_array_equal(result, expected)
|
|
result = index.isin(other.tolist())
|
tm.assert_numpy_array_equal(result, expected)
|
|
for other_closed in ["right", "left", "both", "neither"]:
|
other = self.create_index(closed=other_closed)
|
expected = np.repeat(closed == other_closed, len(index))
|
result = index.isin(other)
|
tm.assert_numpy_array_equal(result, expected)
|
|
result = index.isin(other.tolist())
|
tm.assert_numpy_array_equal(result, expected)
|
|
def test_comparison(self):
|
actual = Interval(0, 1) < self.index
|
expected = np.array([False, True])
|
tm.assert_numpy_array_equal(actual, expected)
|
|
actual = Interval(0.5, 1.5) < self.index
|
expected = np.array([False, True])
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = self.index > Interval(0.5, 1.5)
|
tm.assert_numpy_array_equal(actual, expected)
|
|
actual = self.index == self.index
|
expected = np.array([True, True])
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = self.index <= self.index
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = self.index >= self.index
|
tm.assert_numpy_array_equal(actual, expected)
|
|
actual = self.index < self.index
|
expected = np.array([False, False])
|
tm.assert_numpy_array_equal(actual, expected)
|
actual = self.index > self.index
|
tm.assert_numpy_array_equal(actual, expected)
|
|
actual = self.index == IntervalIndex.from_breaks([0, 1, 2], "left")
|
tm.assert_numpy_array_equal(actual, expected)
|
|
actual = self.index == self.index.values
|
tm.assert_numpy_array_equal(actual, np.array([True, True]))
|
actual = self.index.values == self.index
|
tm.assert_numpy_array_equal(actual, np.array([True, True]))
|
actual = self.index <= self.index.values
|
tm.assert_numpy_array_equal(actual, np.array([True, True]))
|
actual = self.index != self.index.values
|
tm.assert_numpy_array_equal(actual, np.array([False, False]))
|
actual = self.index > self.index.values
|
tm.assert_numpy_array_equal(actual, np.array([False, False]))
|
actual = self.index.values > self.index
|
tm.assert_numpy_array_equal(actual, np.array([False, False]))
|
|
# invalid comparisons
|
actual = self.index == 0
|
tm.assert_numpy_array_equal(actual, np.array([False, False]))
|
actual = self.index == self.index.left
|
tm.assert_numpy_array_equal(actual, np.array([False, False]))
|
|
msg = "|".join(
|
[
|
"not supported between instances of 'int' and '.*.Interval'",
|
r"Invalid comparison between dtype=interval\[int64, right\] and ",
|
]
|
)
|
with pytest.raises(TypeError, match=msg):
|
self.index > 0
|
with pytest.raises(TypeError, match=msg):
|
self.index <= 0
|
with pytest.raises(TypeError, match=msg):
|
self.index > np.arange(2)
|
|
msg = "Lengths must match to compare"
|
with pytest.raises(ValueError, match=msg):
|
self.index > np.arange(3)
|
|
def test_missing_values(self, closed):
|
idx = Index(
|
[np.nan, Interval(0, 1, closed=closed), Interval(1, 2, closed=closed)]
|
)
|
idx2 = IntervalIndex.from_arrays([np.nan, 0, 1], [np.nan, 1, 2], closed=closed)
|
assert idx.equals(idx2)
|
|
msg = (
|
"missing values must be missing in the same location both left "
|
"and right sides"
|
)
|
with pytest.raises(ValueError, match=msg):
|
IntervalIndex.from_arrays(
|
[np.nan, 0, 1], np.array([0, 1, 2]), closed=closed
|
)
|
|
tm.assert_numpy_array_equal(isna(idx), np.array([True, False, False]))
|
|
def test_sort_values(self, closed):
|
index = self.create_index(closed=closed)
|
|
result = index.sort_values()
|
tm.assert_index_equal(result, index)
|
|
result = index.sort_values(ascending=False)
|
tm.assert_index_equal(result, index[::-1])
|
|
# with nan
|
index = IntervalIndex([Interval(1, 2), np.nan, Interval(0, 1)])
|
|
result = index.sort_values()
|
expected = IntervalIndex([Interval(0, 1), Interval(1, 2), np.nan])
|
tm.assert_index_equal(result, expected)
|
|
result = index.sort_values(ascending=False, na_position="first")
|
expected = IntervalIndex([np.nan, Interval(1, 2), Interval(0, 1)])
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize("tz", [None, "US/Eastern"])
|
def test_datetime(self, tz):
|
start = Timestamp("2000-01-01", tz=tz)
|
dates = date_range(start=start, periods=10)
|
index = IntervalIndex.from_breaks(dates)
|
|
# test mid
|
start = Timestamp("2000-01-01T12:00", tz=tz)
|
expected = date_range(start=start, periods=9)
|
tm.assert_index_equal(index.mid, expected)
|
|
# __contains__ doesn't check individual points
|
assert Timestamp("2000-01-01", tz=tz) not in index
|
assert Timestamp("2000-01-01T12", tz=tz) not in index
|
assert Timestamp("2000-01-02", tz=tz) not in index
|
iv_true = Interval(
|
Timestamp("2000-01-02", tz=tz), Timestamp("2000-01-03", tz=tz)
|
)
|
iv_false = Interval(
|
Timestamp("1999-12-31", tz=tz), Timestamp("2000-01-01", tz=tz)
|
)
|
assert iv_true in index
|
assert iv_false not in index
|
|
# .contains does check individual points
|
assert not index.contains(Timestamp("2000-01-01", tz=tz)).any()
|
assert index.contains(Timestamp("2000-01-01T12", tz=tz)).any()
|
assert index.contains(Timestamp("2000-01-02", tz=tz)).any()
|
|
# test get_indexer
|
start = Timestamp("1999-12-31T12:00", tz=tz)
|
target = date_range(start=start, periods=7, freq="12H")
|
actual = index.get_indexer(target)
|
expected = np.array([-1, -1, 0, 0, 1, 1, 2], dtype="intp")
|
tm.assert_numpy_array_equal(actual, expected)
|
|
start = Timestamp("2000-01-08T18:00", tz=tz)
|
target = date_range(start=start, periods=7, freq="6H")
|
actual = index.get_indexer(target)
|
expected = np.array([7, 7, 8, 8, 8, 8, -1], dtype="intp")
|
tm.assert_numpy_array_equal(actual, expected)
|
|
def test_append(self, closed):
|
index1 = IntervalIndex.from_arrays([0, 1], [1, 2], closed=closed)
|
index2 = IntervalIndex.from_arrays([1, 2], [2, 3], closed=closed)
|
|
result = index1.append(index2)
|
expected = IntervalIndex.from_arrays([0, 1, 1, 2], [1, 2, 2, 3], closed=closed)
|
tm.assert_index_equal(result, expected)
|
|
result = index1.append([index1, index2])
|
expected = IntervalIndex.from_arrays(
|
[0, 1, 0, 1, 1, 2], [1, 2, 1, 2, 2, 3], closed=closed
|
)
|
tm.assert_index_equal(result, expected)
|
|
for other_closed in {"left", "right", "both", "neither"} - {closed}:
|
index_other_closed = IntervalIndex.from_arrays(
|
[0, 1], [1, 2], closed=other_closed
|
)
|
result = index1.append(index_other_closed)
|
expected = index1.astype(object).append(index_other_closed.astype(object))
|
tm.assert_index_equal(result, expected)
|
|
def test_is_non_overlapping_monotonic(self, closed):
|
# Should be True in all cases
|
tpls = [(0, 1), (2, 3), (4, 5), (6, 7)]
|
idx = IntervalIndex.from_tuples(tpls, closed=closed)
|
assert idx.is_non_overlapping_monotonic is True
|
|
idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
|
assert idx.is_non_overlapping_monotonic is True
|
|
# Should be False in all cases (overlapping)
|
tpls = [(0, 2), (1, 3), (4, 5), (6, 7)]
|
idx = IntervalIndex.from_tuples(tpls, closed=closed)
|
assert idx.is_non_overlapping_monotonic is False
|
|
idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
|
assert idx.is_non_overlapping_monotonic is False
|
|
# Should be False in all cases (non-monotonic)
|
tpls = [(0, 1), (2, 3), (6, 7), (4, 5)]
|
idx = IntervalIndex.from_tuples(tpls, closed=closed)
|
assert idx.is_non_overlapping_monotonic is False
|
|
idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
|
assert idx.is_non_overlapping_monotonic is False
|
|
# Should be False for closed='both', otherwise True (GH16560)
|
if closed == "both":
|
idx = IntervalIndex.from_breaks(range(4), closed=closed)
|
assert idx.is_non_overlapping_monotonic is False
|
else:
|
idx = IntervalIndex.from_breaks(range(4), closed=closed)
|
assert idx.is_non_overlapping_monotonic is True
|
|
@pytest.mark.parametrize(
|
"start, shift, na_value",
|
[
|
(0, 1, np.nan),
|
(Timestamp("2018-01-01"), Timedelta("1 day"), pd.NaT),
|
(Timedelta("0 days"), Timedelta("1 day"), pd.NaT),
|
],
|
)
|
def test_is_overlapping(self, start, shift, na_value, closed):
|
# GH 23309
|
# see test_interval_tree.py for extensive tests; interface tests here
|
|
# non-overlapping
|
tuples = [(start + n * shift, start + (n + 1) * shift) for n in (0, 2, 4)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
assert index.is_overlapping is False
|
|
# non-overlapping with NA
|
tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
assert index.is_overlapping is False
|
|
# overlapping
|
tuples = [(start + n * shift, start + (n + 2) * shift) for n in range(3)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
assert index.is_overlapping is True
|
|
# overlapping with NA
|
tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
assert index.is_overlapping is True
|
|
# common endpoints
|
tuples = [(start + n * shift, start + (n + 1) * shift) for n in range(3)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
result = index.is_overlapping
|
expected = closed == "both"
|
assert result is expected
|
|
# common endpoints with NA
|
tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
|
index = IntervalIndex.from_tuples(tuples, closed=closed)
|
result = index.is_overlapping
|
assert result is expected
|
|
# intervals with duplicate left values
|
a = [10, 15, 20, 25, 30, 35, 40, 45, 45, 50, 55, 60, 65, 70, 75, 80, 85]
|
b = [15, 20, 25, 30, 35, 40, 45, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90]
|
index = IntervalIndex.from_arrays(a, b, closed="right")
|
result = index.is_overlapping
|
assert result is False
|
|
@pytest.mark.parametrize(
|
"tuples",
|
[
|
list(zip(range(10), range(1, 11))),
|
list(
|
zip(
|
date_range("20170101", periods=10),
|
date_range("20170101", periods=10),
|
)
|
),
|
list(
|
zip(
|
timedelta_range("0 days", periods=10),
|
timedelta_range("1 day", periods=10),
|
)
|
),
|
],
|
)
|
def test_to_tuples(self, tuples):
|
# GH 18756
|
idx = IntervalIndex.from_tuples(tuples)
|
result = idx.to_tuples()
|
expected = Index(com.asarray_tuplesafe(tuples))
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"tuples",
|
[
|
list(zip(range(10), range(1, 11))) + [np.nan],
|
list(
|
zip(
|
date_range("20170101", periods=10),
|
date_range("20170101", periods=10),
|
)
|
)
|
+ [np.nan],
|
list(
|
zip(
|
timedelta_range("0 days", periods=10),
|
timedelta_range("1 day", periods=10),
|
)
|
)
|
+ [np.nan],
|
],
|
)
|
@pytest.mark.parametrize("na_tuple", [True, False])
|
def test_to_tuples_na(self, tuples, na_tuple):
|
# GH 18756
|
idx = IntervalIndex.from_tuples(tuples)
|
result = idx.to_tuples(na_tuple=na_tuple)
|
|
# check the non-NA portion
|
expected_notna = Index(com.asarray_tuplesafe(tuples[:-1]))
|
result_notna = result[:-1]
|
tm.assert_index_equal(result_notna, expected_notna)
|
|
# check the NA portion
|
result_na = result[-1]
|
if na_tuple:
|
assert isinstance(result_na, tuple)
|
assert len(result_na) == 2
|
assert all(isna(x) for x in result_na)
|
else:
|
assert isna(result_na)
|
|
def test_nbytes(self):
|
# GH 19209
|
left = np.arange(0, 4, dtype="i8")
|
right = np.arange(1, 5, dtype="i8")
|
|
result = IntervalIndex.from_arrays(left, right).nbytes
|
expected = 64 # 4 * 8 * 2
|
assert result == expected
|
|
@pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
|
def test_set_closed(self, name, closed, new_closed):
|
# GH 21670
|
index = interval_range(0, 5, closed=closed, name=name)
|
result = index.set_closed(new_closed)
|
expected = interval_range(0, 5, closed=new_closed, name=name)
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize("bad_closed", ["foo", 10, "LEFT", True, False])
|
def test_set_closed_errors(self, bad_closed):
|
# GH 21670
|
index = interval_range(0, 5)
|
msg = f"invalid option for 'closed': {bad_closed}"
|
with pytest.raises(ValueError, match=msg):
|
index.set_closed(bad_closed)
|
|
def test_is_all_dates(self):
|
# GH 23576
|
year_2017 = Interval(
|
Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")
|
)
|
year_2017_index = IntervalIndex([year_2017])
|
assert not year_2017_index._is_all_dates
|
|
|
def test_dir():
|
# GH#27571 dir(interval_index) should not raise
|
index = IntervalIndex.from_arrays([0, 1], [1, 2])
|
result = dir(index)
|
assert "str" not in result
|
|
|
def test_searchsorted_different_argument_classes(listlike_box):
|
# https://github.com/pandas-dev/pandas/issues/32762
|
values = IntervalIndex([Interval(0, 1), Interval(1, 2)])
|
result = values.searchsorted(listlike_box(values))
|
expected = np.array([0, 1], dtype=result.dtype)
|
tm.assert_numpy_array_equal(result, expected)
|
|
result = values._data.searchsorted(listlike_box(values))
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2]
|
)
|
def test_searchsorted_invalid_argument(arg):
|
values = IntervalIndex([Interval(0, 1), Interval(1, 2)])
|
msg = "'<' not supported between instances of 'pandas._libs.interval.Interval' and "
|
with pytest.raises(TypeError, match=msg):
|
values.searchsorted(arg)
|
