import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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IntervalIndex,
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Series,
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)
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import pandas._testing as tm
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class TestIntervalIndex:
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@pytest.fixture
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def series_with_interval_index(self):
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return Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6)))
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def test_getitem_with_scalar(self, series_with_interval_index, indexer_sl):
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ser = series_with_interval_index.copy()
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expected = ser.iloc[:3]
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tm.assert_series_equal(expected, indexer_sl(ser)[:3])
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tm.assert_series_equal(expected, indexer_sl(ser)[:2.5])
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tm.assert_series_equal(expected, indexer_sl(ser)[0.1:2.5])
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if indexer_sl is tm.loc:
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tm.assert_series_equal(expected, ser.loc[-1:3])
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expected = ser.iloc[1:4]
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tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 2.5, 3.5]])
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tm.assert_series_equal(expected, indexer_sl(ser)[[2, 3, 4]])
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tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 3, 4]])
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expected = ser.iloc[2:5]
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tm.assert_series_equal(expected, indexer_sl(ser)[ser >= 2])
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@pytest.mark.parametrize("direction", ["increasing", "decreasing"])
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def test_getitem_nonoverlapping_monotonic(self, direction, closed, indexer_sl):
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tpls = [(0, 1), (2, 3), (4, 5)]
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if direction == "decreasing":
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tpls = tpls[::-1]
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idx = IntervalIndex.from_tuples(tpls, closed=closed)
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ser = Series(list("abc"), idx)
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for key, expected in zip(idx.left, ser):
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if idx.closed_left:
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assert indexer_sl(ser)[key] == expected
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else:
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with pytest.raises(KeyError, match=str(key)):
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indexer_sl(ser)[key]
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for key, expected in zip(idx.right, ser):
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if idx.closed_right:
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assert indexer_sl(ser)[key] == expected
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else:
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with pytest.raises(KeyError, match=str(key)):
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indexer_sl(ser)[key]
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for key, expected in zip(idx.mid, ser):
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assert indexer_sl(ser)[key] == expected
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def test_getitem_non_matching(self, series_with_interval_index, indexer_sl):
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ser = series_with_interval_index.copy()
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# this is a departure from our current
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# indexing scheme, but simpler
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with pytest.raises(KeyError, match=r"\[-1\] not in index"):
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indexer_sl(ser)[[-1, 3, 4, 5]]
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with pytest.raises(KeyError, match=r"\[-1\] not in index"):
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indexer_sl(ser)[[-1, 3]]
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@pytest.mark.slow
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def test_loc_getitem_large_series(self):
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ser = Series(
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np.arange(1000000), index=IntervalIndex.from_breaks(np.arange(1000001))
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)
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result1 = ser.loc[:80000]
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result2 = ser.loc[0:80000]
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result3 = ser.loc[0:80000:1]
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tm.assert_series_equal(result1, result2)
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tm.assert_series_equal(result1, result3)
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def test_loc_getitem_frame(self):
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# CategoricalIndex with IntervalIndex categories
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df = DataFrame({"A": range(10)})
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ser = pd.cut(df.A, 5)
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df["B"] = ser
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df = df.set_index("B")
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result = df.loc[4]
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expected = df.iloc[4:6]
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tm.assert_frame_equal(result, expected)
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with pytest.raises(KeyError, match="10"):
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df.loc[10]
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# single list-like
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result = df.loc[[4]]
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expected = df.iloc[4:6]
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tm.assert_frame_equal(result, expected)
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# non-unique
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result = df.loc[[4, 5]]
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expected = df.take([4, 5, 4, 5])
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tm.assert_frame_equal(result, expected)
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with pytest.raises(KeyError, match=r"None of \[\[10\]\] are"):
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df.loc[[10]]
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# partial missing
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with pytest.raises(KeyError, match=r"\[10\] not in index"):
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df.loc[[10, 4]]
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def test_getitem_interval_with_nans(self, frame_or_series, indexer_sl):
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# GH#41831
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index = IntervalIndex([np.nan, np.nan])
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key = index[:-1]
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obj = frame_or_series(range(2), index=index)
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if frame_or_series is DataFrame and indexer_sl is tm.setitem:
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obj = obj.T
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result = indexer_sl(obj)[key]
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expected = obj
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tm.assert_equal(result, expected)
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class TestIntervalIndexInsideMultiIndex:
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def test_mi_intervalindex_slicing_with_scalar(self):
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# GH#27456
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ii = IntervalIndex.from_arrays(
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[0, 1, 10, 11, 0, 1, 10, 11], [1, 2, 11, 12, 1, 2, 11, 12], name="MP"
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)
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idx = pd.MultiIndex.from_arrays(
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[
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pd.Index(["FC", "FC", "FC", "FC", "OWNER", "OWNER", "OWNER", "OWNER"]),
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pd.Index(
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["RID1", "RID1", "RID2", "RID2", "RID1", "RID1", "RID2", "RID2"]
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),
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ii,
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]
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)
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idx.names = ["Item", "RID", "MP"]
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df = DataFrame({"value": [1, 2, 3, 4, 5, 6, 7, 8]})
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df.index = idx
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query_df = DataFrame(
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{
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"Item": ["FC", "OWNER", "FC", "OWNER", "OWNER"],
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"RID": ["RID1", "RID1", "RID1", "RID2", "RID2"],
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"MP": [0.2, 1.5, 1.6, 11.1, 10.9],
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}
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)
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query_df = query_df.sort_index()
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idx = pd.MultiIndex.from_arrays([query_df.Item, query_df.RID, query_df.MP])
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query_df.index = idx
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result = df.value.loc[query_df.index]
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# the IntervalIndex level is indexed with floats, which map to
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# the intervals containing them. Matching the behavior we would get
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# with _only_ an IntervalIndex, we get an IntervalIndex level back.
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sliced_level = ii.take([0, 1, 1, 3, 2])
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expected_index = pd.MultiIndex.from_arrays(
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[idx.get_level_values(0), idx.get_level_values(1), sliced_level]
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)
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expected = Series([1, 6, 2, 8, 7], index=expected_index, name="value")
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tm.assert_series_equal(result, expected)
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