106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
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import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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Interval,
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IntervalIndex,
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Series,
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Timedelta,
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Timestamp,
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)
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import pandas._testing as tm
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class TestIntervalIndexRendering:
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def test_frame_repr(self):
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# https://github.com/pandas-dev/pandas/pull/24134/files
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df = DataFrame(
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{"A": [1, 2, 3, 4]}, index=IntervalIndex.from_breaks([0, 1, 2, 3, 4])
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)
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result = repr(df)
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expected = " A\n(0, 1] 1\n(1, 2] 2\n(2, 3] 3\n(3, 4] 4"
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assert result == expected
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@pytest.mark.parametrize(
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"constructor,expected",
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[
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(
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Series,
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(
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"(0.0, 1.0] a\n"
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"NaN b\n"
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"(2.0, 3.0] c\n"
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"dtype: object"
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),
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),
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(DataFrame, (" 0\n(0.0, 1.0] a\nNaN b\n(2.0, 3.0] c")),
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],
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)
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def test_repr_missing(self, constructor, expected):
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# GH 25984
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index = IntervalIndex.from_tuples([(0, 1), np.nan, (2, 3)])
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obj = constructor(list("abc"), index=index)
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result = repr(obj)
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assert result == expected
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def test_repr_floats(self):
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# GH 32553
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markers = Series(
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["foo", "bar"],
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index=IntervalIndex(
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[
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Interval(left, right)
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for left, right in zip(
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Index([329.973, 345.137], dtype="float64"),
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Index([345.137, 360.191], dtype="float64"),
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)
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]
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),
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)
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result = str(markers)
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expected = "(329.973, 345.137] foo\n(345.137, 360.191] bar\ndtype: object"
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assert result == expected
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@pytest.mark.parametrize(
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"tuples, closed, expected_data",
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[
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([(0, 1), (1, 2), (2, 3)], "left", ["[0, 1)", "[1, 2)", "[2, 3)"]),
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(
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[(0.5, 1.0), np.nan, (2.0, 3.0)],
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"right",
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["(0.5, 1.0]", "NaN", "(2.0, 3.0]"],
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),
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(
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[
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(Timestamp("20180101"), Timestamp("20180102")),
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np.nan,
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((Timestamp("20180102"), Timestamp("20180103"))),
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],
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"both",
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["[2018-01-01, 2018-01-02]", "NaN", "[2018-01-02, 2018-01-03]"],
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),
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(
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[
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(Timedelta("0 days"), Timedelta("1 days")),
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(Timedelta("1 days"), Timedelta("2 days")),
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np.nan,
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],
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"neither",
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[
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"(0 days 00:00:00, 1 days 00:00:00)",
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"(1 days 00:00:00, 2 days 00:00:00)",
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"NaN",
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],
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),
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],
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)
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def test_to_native_types(self, tuples, closed, expected_data):
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# GH 28210
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index = IntervalIndex.from_tuples(tuples, closed=closed)
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result = index._format_native_types()
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expected = np.array(expected_data)
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tm.assert_numpy_array_equal(result, expected)
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