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736 lines
22 KiB
736 lines
22 KiB
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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Categorical,
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DataFrame,
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DatetimeIndex,
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Index,
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Interval,
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IntervalIndex,
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Series,
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TimedeltaIndex,
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Timestamp,
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cut,
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date_range,
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isna,
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qcut,
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timedelta_range,
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to_datetime,
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)
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import pandas._testing as tm
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from pandas.api.types import CategoricalDtype as CDT
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import pandas.core.reshape.tile as tmod
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def test_simple():
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data = np.ones(5, dtype="int64")
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result = cut(data, 4, labels=False)
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expected = np.array([1, 1, 1, 1, 1])
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize("func", [list, np.array])
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def test_bins(func):
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data = func([0.2, 1.4, 2.5, 6.2, 9.7, 2.1])
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result, bins = cut(data, 3, retbins=True)
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intervals = IntervalIndex.from_breaks(bins.round(3))
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intervals = intervals.take([0, 0, 0, 1, 2, 0])
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expected = Categorical(intervals, ordered=True)
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tm.assert_categorical_equal(result, expected)
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tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667, 6.53333333, 9.7]))
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def test_right():
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data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
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result, bins = cut(data, 4, right=True, retbins=True)
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intervals = IntervalIndex.from_breaks(bins.round(3))
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expected = Categorical(intervals, ordered=True)
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expected = expected.take([0, 0, 0, 2, 3, 0, 0])
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tm.assert_categorical_equal(result, expected)
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tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95, 7.325, 9.7]))
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def test_no_right():
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data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
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result, bins = cut(data, 4, right=False, retbins=True)
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intervals = IntervalIndex.from_breaks(bins.round(3), closed="left")
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intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
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expected = Categorical(intervals, ordered=True)
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tm.assert_categorical_equal(result, expected)
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tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95, 7.325, 9.7095]))
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def test_bins_from_interval_index():
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c = cut(range(5), 3)
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expected = c
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result = cut(range(5), bins=expected.categories)
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tm.assert_categorical_equal(result, expected)
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expected = Categorical.from_codes(
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np.append(c.codes, -1), categories=c.categories, ordered=True
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)
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result = cut(range(6), bins=expected.categories)
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tm.assert_categorical_equal(result, expected)
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def test_bins_from_interval_index_doc_example():
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# Make sure we preserve the bins.
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ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
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c = cut(ages, bins=[0, 18, 35, 70])
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expected = IntervalIndex.from_tuples([(0, 18), (18, 35), (35, 70)])
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tm.assert_index_equal(c.categories, expected)
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result = cut([25, 20, 50], bins=c.categories)
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tm.assert_index_equal(result.categories, expected)
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tm.assert_numpy_array_equal(result.codes, np.array([1, 1, 2], dtype="int8"))
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def test_bins_not_overlapping_from_interval_index():
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# see gh-23980
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msg = "Overlapping IntervalIndex is not accepted"
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ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
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with pytest.raises(ValueError, match=msg):
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cut([5, 6], bins=ii)
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def test_bins_not_monotonic():
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msg = "bins must increase monotonically"
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data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
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with pytest.raises(ValueError, match=msg):
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cut(data, [0.1, 1.5, 1, 10])
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@pytest.mark.parametrize(
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"x, bins, expected",
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[
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(
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date_range("2017-12-31", periods=3),
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[Timestamp.min, Timestamp("2018-01-01"), Timestamp.max],
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IntervalIndex.from_tuples(
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[
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(Timestamp.min, Timestamp("2018-01-01")),
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(Timestamp("2018-01-01"), Timestamp.max),
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]
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),
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),
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(
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[-1, 0, 1],
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np.array(
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[np.iinfo(np.int64).min, 0, np.iinfo(np.int64).max], dtype="int64"
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),
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IntervalIndex.from_tuples(
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[(np.iinfo(np.int64).min, 0), (0, np.iinfo(np.int64).max)]
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),
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),
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(
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[
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np.timedelta64(-1, "ns"),
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np.timedelta64(0, "ns"),
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np.timedelta64(1, "ns"),
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],
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np.array(
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[
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np.timedelta64(-np.iinfo(np.int64).max, "ns"),
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np.timedelta64(0, "ns"),
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np.timedelta64(np.iinfo(np.int64).max, "ns"),
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]
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),
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IntervalIndex.from_tuples(
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[
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(
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np.timedelta64(-np.iinfo(np.int64).max, "ns"),
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np.timedelta64(0, "ns"),
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),
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(
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np.timedelta64(0, "ns"),
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np.timedelta64(np.iinfo(np.int64).max, "ns"),
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),
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]
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),
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),
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],
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)
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def test_bins_monotonic_not_overflowing(x, bins, expected):
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# GH 26045
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result = cut(x, bins)
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tm.assert_index_equal(result.categories, expected)
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def test_wrong_num_labels():
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msg = "Bin labels must be one fewer than the number of bin edges"
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data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
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with pytest.raises(ValueError, match=msg):
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cut(data, [0, 1, 10], labels=["foo", "bar", "baz"])
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@pytest.mark.parametrize(
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"x,bins,msg",
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[
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([], 2, "Cannot cut empty array"),
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([1, 2, 3], 0.5, "`bins` should be a positive integer"),
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],
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)
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def test_cut_corner(x, bins, msg):
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with pytest.raises(ValueError, match=msg):
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cut(x, bins)
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@pytest.mark.parametrize("arg", [2, np.eye(2), DataFrame(np.eye(2))])
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@pytest.mark.parametrize("cut_func", [cut, qcut])
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def test_cut_not_1d_arg(arg, cut_func):
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msg = "Input array must be 1 dimensional"
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with pytest.raises(ValueError, match=msg):
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cut_func(arg, 2)
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@pytest.mark.parametrize(
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"data",
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[
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[0, 1, 2, 3, 4, np.inf],
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[-np.inf, 0, 1, 2, 3, 4],
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[-np.inf, 0, 1, 2, 3, 4, np.inf],
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],
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)
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def test_int_bins_with_inf(data):
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# GH 24314
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msg = "cannot specify integer `bins` when input data contains infinity"
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with pytest.raises(ValueError, match=msg):
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cut(data, bins=3)
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def test_cut_out_of_range_more():
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# see gh-1511
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name = "x"
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ser = Series([0, -1, 0, 1, -3], name=name)
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ind = cut(ser, [0, 1], labels=False)
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exp = Series([np.nan, np.nan, np.nan, 0, np.nan], name=name)
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tm.assert_series_equal(ind, exp)
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@pytest.mark.parametrize(
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"right,breaks,closed",
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[
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(True, [-1e-3, 0.25, 0.5, 0.75, 1], "right"),
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(False, [0, 0.25, 0.5, 0.75, 1 + 1e-3], "left"),
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],
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)
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def test_labels(right, breaks, closed):
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arr = np.tile(np.arange(0, 1.01, 0.1), 4)
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result, bins = cut(arr, 4, retbins=True, right=right)
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ex_levels = IntervalIndex.from_breaks(breaks, closed=closed)
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tm.assert_index_equal(result.categories, ex_levels)
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def test_cut_pass_series_name_to_factor():
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name = "foo"
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ser = Series(np.random.randn(100), name=name)
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factor = cut(ser, 4)
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assert factor.name == name
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def test_label_precision():
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arr = np.arange(0, 0.73, 0.01)
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result = cut(arr, 4, precision=2)
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ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36, 0.54, 0.72])
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tm.assert_index_equal(result.categories, ex_levels)
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@pytest.mark.parametrize("labels", [None, False])
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def test_na_handling(labels):
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arr = np.arange(0, 0.75, 0.01)
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arr[::3] = np.nan
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result = cut(arr, 4, labels=labels)
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result = np.asarray(result)
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expected = np.where(isna(arr), np.nan, result)
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tm.assert_almost_equal(result, expected)
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def test_inf_handling():
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data = np.arange(6)
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data_ser = Series(data, dtype="int64")
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bins = [-np.inf, 2, 4, np.inf]
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result = cut(data, bins)
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result_ser = cut(data_ser, bins)
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ex_uniques = IntervalIndex.from_breaks(bins)
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tm.assert_index_equal(result.categories, ex_uniques)
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assert result[5] == Interval(4, np.inf)
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assert result[0] == Interval(-np.inf, 2)
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assert result_ser[5] == Interval(4, np.inf)
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assert result_ser[0] == Interval(-np.inf, 2)
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def test_cut_out_of_bounds():
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arr = np.random.randn(100)
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result = cut(arr, [-1, 0, 1])
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mask = isna(result)
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ex_mask = (arr < -1) | (arr > 1)
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tm.assert_numpy_array_equal(mask, ex_mask)
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@pytest.mark.parametrize(
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"get_labels,get_expected",
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[
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(
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lambda labels: labels,
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lambda labels: Categorical(
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["Medium"] + 4 * ["Small"] + ["Medium", "Large"],
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categories=labels,
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ordered=True,
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),
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),
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(
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lambda labels: Categorical.from_codes([0, 1, 2], labels),
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lambda labels: Categorical.from_codes([1] + 4 * [0] + [1, 2], labels),
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),
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],
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)
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def test_cut_pass_labels(get_labels, get_expected):
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bins = [0, 25, 50, 100]
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arr = [50, 5, 10, 15, 20, 30, 70]
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labels = ["Small", "Medium", "Large"]
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result = cut(arr, bins, labels=get_labels(labels))
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tm.assert_categorical_equal(result, get_expected(labels))
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def test_cut_pass_labels_compat():
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# see gh-16459
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arr = [50, 5, 10, 15, 20, 30, 70]
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labels = ["Good", "Medium", "Bad"]
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result = cut(arr, 3, labels=labels)
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exp = cut(arr, 3, labels=Categorical(labels, categories=labels, ordered=True))
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tm.assert_categorical_equal(result, exp)
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@pytest.mark.parametrize("x", [np.arange(11.0), np.arange(11.0) / 1e10])
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def test_round_frac_just_works(x):
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# It works.
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cut(x, 2)
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@pytest.mark.parametrize(
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"val,precision,expected",
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[
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(-117.9998, 3, -118),
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(117.9998, 3, 118),
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(117.9998, 2, 118),
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(0.000123456, 2, 0.00012),
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],
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)
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def test_round_frac(val, precision, expected):
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# see gh-1979
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result = tmod._round_frac(val, precision=precision)
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assert result == expected
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def test_cut_return_intervals():
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ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
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result = cut(ser, 3)
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exp_bins = np.linspace(0, 8, num=4).round(3)
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exp_bins[0] -= 0.008
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expected = Series(
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IntervalIndex.from_breaks(exp_bins, closed="right").take(
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[0, 0, 0, 1, 1, 1, 2, 2, 2]
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)
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).astype(CDT(ordered=True))
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tm.assert_series_equal(result, expected)
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def test_series_ret_bins():
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# see gh-8589
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ser = Series(np.arange(4))
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result, bins = cut(ser, 2, retbins=True)
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expected = Series(
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IntervalIndex.from_breaks([-0.003, 1.5, 3], closed="right").repeat(2)
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).astype(CDT(ordered=True))
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"kwargs,msg",
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[
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({"duplicates": "drop"}, None),
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({}, "Bin edges must be unique"),
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({"duplicates": "raise"}, "Bin edges must be unique"),
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({"duplicates": "foo"}, "invalid value for 'duplicates' parameter"),
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],
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)
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def test_cut_duplicates_bin(kwargs, msg):
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# see gh-20947
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bins = [0, 2, 4, 6, 10, 10]
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values = Series(np.array([1, 3, 5, 7, 9]), index=["a", "b", "c", "d", "e"])
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if msg is not None:
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with pytest.raises(ValueError, match=msg):
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cut(values, bins, **kwargs)
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else:
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result = cut(values, bins, **kwargs)
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expected = cut(values, pd.unique(bins))
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tm.assert_series_equal(result, expected)
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|
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@pytest.mark.parametrize("data", [9.0, -9.0, 0.0])
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@pytest.mark.parametrize("length", [1, 2])
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def test_single_bin(data, length):
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# see gh-14652, gh-15428
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ser = Series([data] * length)
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result = cut(ser, 1, labels=False)
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expected = Series([0] * length, dtype=np.intp)
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tm.assert_series_equal(result, expected)
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|
|
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@pytest.mark.parametrize(
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"array_1_writeable,array_2_writeable", [(True, True), (True, False), (False, False)]
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)
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def test_cut_read_only(array_1_writeable, array_2_writeable):
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# issue 18773
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array_1 = np.arange(0, 100, 10)
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array_1.flags.writeable = array_1_writeable
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array_2 = np.arange(0, 100, 10)
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array_2.flags.writeable = array_2_writeable
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hundred_elements = np.arange(100)
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tm.assert_categorical_equal(
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cut(hundred_elements, array_1), cut(hundred_elements, array_2)
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)
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|
|
|
|
@pytest.mark.parametrize(
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"conv",
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[
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lambda v: Timestamp(v),
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lambda v: to_datetime(v),
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lambda v: np.datetime64(v),
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lambda v: Timestamp(v).to_pydatetime(),
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],
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)
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|
def test_datetime_bin(conv):
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data = [np.datetime64("2012-12-13"), np.datetime64("2012-12-15")]
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bin_data = ["2012-12-12", "2012-12-14", "2012-12-16"]
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|
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|
expected = Series(
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IntervalIndex(
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[
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Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
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Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2])),
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]
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)
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).astype(CDT(ordered=True))
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bins = [conv(v) for v in bin_data]
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result = Series(cut(data, bins=bins))
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tm.assert_series_equal(result, expected)
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|
|
|
|
@pytest.mark.parametrize(
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"data",
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[
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to_datetime(Series(["2013-01-01", "2013-01-02", "2013-01-03"])),
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[
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np.datetime64("2013-01-01"),
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np.datetime64("2013-01-02"),
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np.datetime64("2013-01-03"),
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],
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np.array(
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[
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np.datetime64("2013-01-01"),
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np.datetime64("2013-01-02"),
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np.datetime64("2013-01-03"),
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]
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),
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DatetimeIndex(["2013-01-01", "2013-01-02", "2013-01-03"]),
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],
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)
|
|
def test_datetime_cut(data):
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# see gh-14714
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#
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# Testing time data when it comes in various collection types.
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result, _ = cut(data, 3, retbins=True)
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expected = Series(
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IntervalIndex(
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[
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Interval(
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Timestamp("2012-12-31 23:57:07.200000"),
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Timestamp("2013-01-01 16:00:00"),
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),
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|
Interval(
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Timestamp("2013-01-01 16:00:00"), Timestamp("2013-01-02 08:00:00")
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),
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|
Interval(
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Timestamp("2013-01-02 08:00:00"), Timestamp("2013-01-03 00:00:00")
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),
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|
]
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|
)
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).astype(CDT(ordered=True))
|
|
tm.assert_series_equal(Series(result), expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"bins",
|
|
[
|
|
3,
|
|
[
|
|
Timestamp("2013-01-01 04:57:07.200000"),
|
|
Timestamp("2013-01-01 21:00:00"),
|
|
Timestamp("2013-01-02 13:00:00"),
|
|
Timestamp("2013-01-03 05:00:00"),
|
|
],
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("box", [list, np.array, Index, Series])
|
|
def test_datetime_tz_cut(bins, box):
|
|
# see gh-19872
|
|
tz = "US/Eastern"
|
|
s = Series(date_range("20130101", periods=3, tz=tz))
|
|
|
|
if not isinstance(bins, int):
|
|
bins = box(bins)
|
|
|
|
result = cut(s, bins)
|
|
expected = Series(
|
|
IntervalIndex(
|
|
[
|
|
Interval(
|
|
Timestamp("2012-12-31 23:57:07.200000", tz=tz),
|
|
Timestamp("2013-01-01 16:00:00", tz=tz),
|
|
),
|
|
Interval(
|
|
Timestamp("2013-01-01 16:00:00", tz=tz),
|
|
Timestamp("2013-01-02 08:00:00", tz=tz),
|
|
),
|
|
Interval(
|
|
Timestamp("2013-01-02 08:00:00", tz=tz),
|
|
Timestamp("2013-01-03 00:00:00", tz=tz),
|
|
),
|
|
]
|
|
)
|
|
).astype(CDT(ordered=True))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_datetime_nan_error():
|
|
msg = "bins must be of datetime64 dtype"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
cut(date_range("20130101", periods=3), bins=[0, 2, 4])
|
|
|
|
|
|
def test_datetime_nan_mask():
|
|
result = cut(
|
|
date_range("20130102", periods=5), bins=date_range("20130101", periods=2)
|
|
)
|
|
|
|
mask = result.categories.isna()
|
|
tm.assert_numpy_array_equal(mask, np.array([False]))
|
|
|
|
mask = result.isna()
|
|
tm.assert_numpy_array_equal(mask, np.array([False, True, True, True, True]))
|
|
|
|
|
|
@pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
|
|
def test_datetime_cut_roundtrip(tz):
|
|
# see gh-19891
|
|
ser = Series(date_range("20180101", periods=3, tz=tz))
|
|
result, result_bins = cut(ser, 2, retbins=True)
|
|
|
|
expected = cut(ser, result_bins)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
expected_bins = DatetimeIndex(
|
|
["2017-12-31 23:57:07.200000", "2018-01-02 00:00:00", "2018-01-03 00:00:00"]
|
|
)
|
|
expected_bins = expected_bins.tz_localize(tz)
|
|
tm.assert_index_equal(result_bins, expected_bins)
|
|
|
|
|
|
def test_timedelta_cut_roundtrip():
|
|
# see gh-19891
|
|
ser = Series(timedelta_range("1day", periods=3))
|
|
result, result_bins = cut(ser, 2, retbins=True)
|
|
|
|
expected = cut(ser, result_bins)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
expected_bins = TimedeltaIndex(
|
|
["0 days 23:57:07.200000", "2 days 00:00:00", "3 days 00:00:00"]
|
|
)
|
|
tm.assert_index_equal(result_bins, expected_bins)
|
|
|
|
|
|
@pytest.mark.parametrize("bins", [6, 7])
|
|
@pytest.mark.parametrize(
|
|
"box, compare",
|
|
[
|
|
(Series, tm.assert_series_equal),
|
|
(np.array, tm.assert_categorical_equal),
|
|
(list, tm.assert_equal),
|
|
],
|
|
)
|
|
def test_cut_bool_coercion_to_int(bins, box, compare):
|
|
# issue 20303
|
|
data_expected = box([0, 1, 1, 0, 1] * 10)
|
|
data_result = box([False, True, True, False, True] * 10)
|
|
expected = cut(data_expected, bins, duplicates="drop")
|
|
result = cut(data_result, bins, duplicates="drop")
|
|
compare(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("labels", ["foo", 1, True])
|
|
def test_cut_incorrect_labels(labels):
|
|
# GH 13318
|
|
values = range(5)
|
|
msg = "Bin labels must either be False, None or passed in as a list-like argument"
|
|
with pytest.raises(ValueError, match=msg):
|
|
cut(values, 4, labels=labels)
|
|
|
|
|
|
@pytest.mark.parametrize("bins", [3, [0, 5, 15]])
|
|
@pytest.mark.parametrize("right", [True, False])
|
|
@pytest.mark.parametrize("include_lowest", [True, False])
|
|
def test_cut_nullable_integer(bins, right, include_lowest):
|
|
a = np.random.randint(0, 10, size=50).astype(float)
|
|
a[::2] = np.nan
|
|
result = cut(
|
|
pd.array(a, dtype="Int64"), bins, right=right, include_lowest=include_lowest
|
|
)
|
|
expected = cut(a, bins, right=right, include_lowest=include_lowest)
|
|
tm.assert_categorical_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data, bins, labels, expected_codes, expected_labels",
|
|
[
|
|
([15, 17, 19], [14, 16, 18, 20], ["A", "B", "A"], [0, 1, 0], ["A", "B"]),
|
|
([1, 3, 5], [0, 2, 4, 6, 8], [2, 0, 1, 2], [2, 0, 1], [0, 1, 2]),
|
|
],
|
|
)
|
|
def test_cut_non_unique_labels(data, bins, labels, expected_codes, expected_labels):
|
|
# GH 33141
|
|
result = cut(data, bins=bins, labels=labels, ordered=False)
|
|
expected = Categorical.from_codes(
|
|
expected_codes, categories=expected_labels, ordered=False
|
|
)
|
|
tm.assert_categorical_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data, bins, labels, expected_codes, expected_labels",
|
|
[
|
|
([15, 17, 19], [14, 16, 18, 20], ["C", "B", "A"], [0, 1, 2], ["C", "B", "A"]),
|
|
([1, 3, 5], [0, 2, 4, 6, 8], [3, 0, 1, 2], [0, 1, 2], [3, 0, 1, 2]),
|
|
],
|
|
)
|
|
def test_cut_unordered_labels(data, bins, labels, expected_codes, expected_labels):
|
|
# GH 33141
|
|
result = cut(data, bins=bins, labels=labels, ordered=False)
|
|
expected = Categorical.from_codes(
|
|
expected_codes, categories=expected_labels, ordered=False
|
|
)
|
|
tm.assert_categorical_equal(result, expected)
|
|
|
|
|
|
def test_cut_unordered_with_missing_labels_raises_error():
|
|
# GH 33141
|
|
msg = "'labels' must be provided if 'ordered = False'"
|
|
with pytest.raises(ValueError, match=msg):
|
|
cut([0.5, 3], bins=[0, 1, 2], ordered=False)
|
|
|
|
|
|
def test_cut_unordered_with_series_labels():
|
|
# https://github.com/pandas-dev/pandas/issues/36603
|
|
s = Series([1, 2, 3, 4, 5])
|
|
bins = Series([0, 2, 4, 6])
|
|
labels = Series(["a", "b", "c"])
|
|
result = cut(s, bins=bins, labels=labels, ordered=False)
|
|
expected = Series(["a", "a", "b", "b", "c"], dtype="category")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_cut_no_warnings():
|
|
df = DataFrame({"value": np.random.randint(0, 100, 20)})
|
|
labels = [f"{i} - {i + 9}" for i in range(0, 100, 10)]
|
|
with tm.assert_produces_warning(False):
|
|
df["group"] = cut(df.value, range(0, 105, 10), right=False, labels=labels)
|
|
|
|
|
|
def test_cut_with_duplicated_index_lowest_included():
|
|
# GH 42185
|
|
expected = Series(
|
|
[Interval(-0.001, 2, closed="right")] * 3
|
|
+ [Interval(2, 4, closed="right"), Interval(-0.001, 2, closed="right")],
|
|
index=[0, 1, 2, 3, 0],
|
|
dtype="category",
|
|
).cat.as_ordered()
|
|
|
|
s = Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
|
|
result = cut(s, bins=[0, 2, 4], include_lowest=True)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_cut_with_nonexact_categorical_indices():
|
|
# GH 42424
|
|
|
|
ser = Series(range(0, 100))
|
|
ser1 = cut(ser, 10).value_counts().head(5)
|
|
ser2 = cut(ser, 10).value_counts().tail(5)
|
|
result = DataFrame({"1": ser1, "2": ser2})
|
|
|
|
index = pd.CategoricalIndex(
|
|
[
|
|
Interval(-0.099, 9.9, closed="right"),
|
|
Interval(9.9, 19.8, closed="right"),
|
|
Interval(19.8, 29.7, closed="right"),
|
|
Interval(29.7, 39.6, closed="right"),
|
|
Interval(39.6, 49.5, closed="right"),
|
|
Interval(49.5, 59.4, closed="right"),
|
|
Interval(59.4, 69.3, closed="right"),
|
|
Interval(69.3, 79.2, closed="right"),
|
|
Interval(79.2, 89.1, closed="right"),
|
|
Interval(89.1, 99, closed="right"),
|
|
],
|
|
ordered=True,
|
|
)
|
|
|
|
expected = DataFrame(
|
|
{"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
|
|
)
|
|
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
|
|
def test_cut_with_timestamp_tuple_labels():
|
|
# GH 40661
|
|
labels = [(Timestamp(10),), (Timestamp(20),), (Timestamp(30),)]
|
|
result = cut([2, 4, 6], bins=[1, 3, 5, 7], labels=labels)
|
|
|
|
expected = Categorical.from_codes([0, 1, 2], labels, ordered=True)
|
|
tm.assert_categorical_equal(result, expected)
|
|
|