test_tile.py

import os
import pytest

import numpy as np
from pandas.compat import zip

import pandas as pd
from pandas import (DataFrame, Series, isna, to_datetime, DatetimeIndex, Index,
                    Timestamp, Interval, IntervalIndex, Categorical,
                    cut, qcut, date_range, timedelta_range, NaT,
                    TimedeltaIndex)
from pandas.tseries.offsets import Nano, Day
import pandas.util.testing as tm
from pandas.api.types import CategoricalDtype as CDT

from pandas.core.algorithms import quantile
import pandas.core.reshape.tile as tmod


class TestCut(object):

    def test_simple(self):
        data = np.ones(5, dtype='int64')
        result = cut(data, 4, labels=False)
        expected = np.array([1, 1, 1, 1, 1])
        tm.assert_numpy_array_equal(result, expected,
                                    check_dtype=False)

    def test_bins(self):
        data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1])
        result, bins = cut(data, 3, retbins=True)

        intervals = IntervalIndex.from_breaks(bins.round(3))
        intervals = intervals.take([0, 0, 0, 1, 2, 0])
        expected = Categorical(intervals, ordered=True)
        tm.assert_categorical_equal(result, expected)
        tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667,
                                               6.53333333, 9.7]))

    def test_right(self):
        data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
        result, bins = cut(data, 4, right=True, retbins=True)
        intervals = IntervalIndex.from_breaks(bins.round(3))
        expected = Categorical(intervals, ordered=True)
        expected = expected.take([0, 0, 0, 2, 3, 0, 0])
        tm.assert_categorical_equal(result, expected)
        tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95,
                                               7.325, 9.7]))

    def test_noright(self):
        data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
        result, bins = cut(data, 4, right=False, retbins=True)
        intervals = IntervalIndex.from_breaks(bins.round(3), closed='left')
        intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
        expected = Categorical(intervals, ordered=True)
        tm.assert_categorical_equal(result, expected)
        tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95,
                                               7.325, 9.7095]))

    def test_arraylike(self):
        data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
        result, bins = cut(data, 3, retbins=True)
        intervals = IntervalIndex.from_breaks(bins.round(3))
        intervals = intervals.take([0, 0, 0, 1, 2, 0])
        expected = Categorical(intervals, ordered=True)
        tm.assert_categorical_equal(result, expected)
        tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667,
                                               6.53333333, 9.7]))

    def test_bins_from_intervalindex(self):
        c = cut(range(5), 3)
        expected = c
        result = cut(range(5), bins=expected.categories)
        tm.assert_categorical_equal(result, expected)

        expected = Categorical.from_codes(np.append(c.codes, -1),
                                          categories=c.categories,
                                          ordered=True)
        result = cut(range(6), bins=expected.categories)
        tm.assert_categorical_equal(result, expected)

        # doc example
        # make sure we preserve the bins
        ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
        c = cut(ages, bins=[0, 18, 35, 70])
        expected = IntervalIndex.from_tuples([(0, 18), (18, 35), (35, 70)])
        tm.assert_index_equal(c.categories, expected)

        result = cut([25, 20, 50], bins=c.categories)
        tm.assert_index_equal(result.categories, expected)
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([1, 1, 2], dtype='int8'))

    def test_bins_not_overlapping_from_intervalindex(self):
        # verify if issue 23980 is properly solved.
        ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
        with pytest.raises(ValueError):
            cut([5, 6], bins=ii)

    def test_bins_not_monotonic(self):
        data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
        pytest.raises(ValueError, cut, data, [0.1, 1.5, 1, 10])

    def test_wrong_num_labels(self):
        data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
        pytest.raises(ValueError, cut, data, [0, 1, 10],
                      labels=['foo', 'bar', 'baz'])

    def test_cut_corner(self):
        # h3h
        pytest.raises(ValueError, cut, [], 2)

        pytest.raises(ValueError, cut, [1, 2, 3], 0.5)

    @pytest.mark.parametrize('arg', [2, np.eye(2), DataFrame(np.eye(2))])
    @pytest.mark.parametrize('cut_func', [cut, qcut])
    def test_cut_not_1d_arg(self, arg, cut_func):
        with pytest.raises(ValueError):
            cut_func(arg, 2)

    def test_cut_out_of_range_more(self):
        # #1511
        s = Series([0, -1, 0, 1, -3], name='x')
        ind = cut(s, [0, 1], labels=False)
        exp = Series([np.nan, np.nan, np.nan, 0, np.nan], name='x')
        tm.assert_series_equal(ind, exp)

    def test_labels(self):
        arr = np.tile(np.arange(0, 1.01, 0.1), 4)

        result, bins = cut(arr, 4, retbins=True)
        ex_levels = IntervalIndex.from_breaks([-1e-3, 0.25, 0.5, 0.75, 1])
        tm.assert_index_equal(result.categories, ex_levels)

        result, bins = cut(arr, 4, retbins=True, right=False)
        ex_levels = IntervalIndex.from_breaks([0, 0.25, 0.5, 0.75, 1 + 1e-3],
                                              closed='left')
        tm.assert_index_equal(result.categories, ex_levels)

    def test_cut_pass_series_name_to_factor(self):
        s = Series(np.random.randn(100), name='foo')

        factor = cut(s, 4)
        assert factor.name == 'foo'

    def test_label_precision(self):
        arr = np.arange(0, 0.73, 0.01)

        result = cut(arr, 4, precision=2)
        ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36,
                                               0.54, 0.72])
        tm.assert_index_equal(result.categories, ex_levels)

    def test_na_handling(self):
        arr = np.arange(0, 0.75, 0.01)
        arr[::3] = np.nan

        result = cut(arr, 4)

        result_arr = np.asarray(result)

        ex_arr = np.where(isna(arr), np.nan, result_arr)

        tm.assert_almost_equal(result_arr, ex_arr)

        result = cut(arr, 4, labels=False)
        ex_result = np.where(isna(arr), np.nan, result)
        tm.assert_almost_equal(result, ex_result)

    def test_inf_handling(self):
        data = np.arange(6)
        data_ser = Series(data, dtype='int64')

        bins = [-np.inf, 2, 4, np.inf]
        result = cut(data, bins)
        result_ser = cut(data_ser, bins)

        ex_uniques = IntervalIndex.from_breaks(bins)
        tm.assert_index_equal(result.categories, ex_uniques)
        assert result[5] == Interval(4, np.inf)
        assert result[0] == Interval(-np.inf, 2)
        assert result_ser[5] == Interval(4, np.inf)
        assert result_ser[0] == Interval(-np.inf, 2)

    def test_qcut(self):
        arr = np.random.randn(1000)

        # We store the bins as Index that have been rounded
        # to comparisons are a bit tricky.
        labels, bins = qcut(arr, 4, retbins=True)
        ex_bins = quantile(arr, [0, .25, .5, .75, 1.])
        result = labels.categories.left.values
        assert np.allclose(result, ex_bins[:-1], atol=1e-2)
        result = labels.categories.right.values
        assert np.allclose(result, ex_bins[1:], atol=1e-2)

        ex_levels = cut(arr, ex_bins, include_lowest=True)
        tm.assert_categorical_equal(labels, ex_levels)

    def test_qcut_bounds(self):
        arr = np.random.randn(1000)

        factor = qcut(arr, 10, labels=False)
        assert len(np.unique(factor)) == 10

    def test_qcut_specify_quantiles(self):
        arr = np.random.randn(100)

        factor = qcut(arr, [0, .25, .5, .75, 1.])
        expected = qcut(arr, 4)
        tm.assert_categorical_equal(factor, expected)

    def test_qcut_all_bins_same(self):
        with pytest.raises(ValueError, match="edges.*unique"):
            qcut([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3)

    def test_cut_out_of_bounds(self):
        arr = np.random.randn(100)

        result = cut(arr, [-1, 0, 1])

        mask = isna(result)
        ex_mask = (arr < -1) | (arr > 1)
        tm.assert_numpy_array_equal(mask, ex_mask)

    def test_cut_pass_labels(self):
        arr = [50, 5, 10, 15, 20, 30, 70]
        bins = [0, 25, 50, 100]
        labels = ['Small', 'Medium', 'Large']

        result = cut(arr, bins, labels=labels)
        exp = Categorical(['Medium'] + 4 * ['Small'] + ['Medium', 'Large'],
                          categories=labels,
                          ordered=True)
        tm.assert_categorical_equal(result, exp)

        result = cut(arr, bins, labels=Categorical.from_codes([0, 1, 2],
                                                              labels))
        exp = Categorical.from_codes([1] + 4 * [0] + [1, 2], labels)
        tm.assert_categorical_equal(result, exp)

        # issue 16459
        labels = ['Good', 'Medium', 'Bad']
        result = cut(arr, 3, labels=labels)
        exp = cut(arr, 3, labels=Categorical(labels, categories=labels,
                                             ordered=True))
        tm.assert_categorical_equal(result, exp)

    def test_qcut_include_lowest(self):
        values = np.arange(10)

        ii = qcut(values, 4)

        ex_levels = IntervalIndex(
            [Interval(-0.001, 2.25),
             Interval(2.25, 4.5),
             Interval(4.5, 6.75),
             Interval(6.75, 9)])
        tm.assert_index_equal(ii.categories, ex_levels)

    def test_qcut_nas(self):
        arr = np.random.randn(100)
        arr[:20] = np.nan

        result = qcut(arr, 4)
        assert isna(result[:20]).all()

    def test_qcut_index(self):
        result = qcut([0, 2], 2)
        intervals = [Interval(-0.001, 1), Interval(1, 2)]
        expected = Categorical(intervals, ordered=True)
        tm.assert_categorical_equal(result, expected)

    def test_round_frac(self):
        # it works
        result = cut(np.arange(11.), 2)

        result = cut(np.arange(11.) / 1e10, 2)

        # #1979, negative numbers

        result = tmod._round_frac(-117.9998, precision=3)
        assert result == -118
        result = tmod._round_frac(117.9998, precision=3)
        assert result == 118

        result = tmod._round_frac(117.9998, precision=2)
        assert result == 118
        result = tmod._round_frac(0.000123456, precision=2)
        assert result == 0.00012

    def test_qcut_binning_issues(self, datapath):
        # #1978, 1979
        cut_file = datapath(os.path.join('reshape', 'data', 'cut_data.csv'))
        arr = np.loadtxt(cut_file)

        result = qcut(arr, 20)

        starts = []
        ends = []
        for lev in np.unique(result):
            s = lev.left
            e = lev.right
            assert s != e

            starts.append(float(s))
            ends.append(float(e))

        for (sp, sn), (ep, en) in zip(zip(starts[:-1], starts[1:]),
                                      zip(ends[:-1], ends[1:])):
            assert sp < sn
            assert ep < en
            assert ep <= sn

    def test_cut_return_intervals(self):
        s = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
        res = cut(s, 3)
        exp_bins = np.linspace(0, 8, num=4).round(3)
        exp_bins[0] -= 0.008
        exp = Series(IntervalIndex.from_breaks(exp_bins, closed='right').take(
            [0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(CDT(ordered=True))
        tm.assert_series_equal(res, exp)

    def test_qcut_return_intervals(self):
        s = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
        res = qcut(s, [0, 0.333, 0.666, 1])
        exp_levels = np.array([Interval(-0.001, 2.664),
                               Interval(2.664, 5.328), Interval(5.328, 8)])
        exp = Series(exp_levels.take([0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(
            CDT(ordered=True))
        tm.assert_series_equal(res, exp)

    def test_series_retbins(self):
        # GH 8589
        s = Series(np.arange(4))
        result, bins = cut(s, 2, retbins=True)
        expected = Series(IntervalIndex.from_breaks(
            [-0.003, 1.5, 3], closed='right').repeat(2)).astype(
            CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        result, bins = qcut(s, 2, retbins=True)
        expected = Series(IntervalIndex.from_breaks(
            [-0.001, 1.5, 3], closed='right').repeat(2)).astype(
            CDT(ordered=True))
        tm.assert_series_equal(result, expected)

    def test_cut_duplicates_bin(self):
        # issue 20947
        values = Series(np.array([1, 3, 5, 7, 9]),
                        index=["a", "b", "c", "d", "e"])
        bins = [0, 2, 4, 6, 10, 10]
        result = cut(values, bins, duplicates='drop')
        expected = cut(values, pd.unique(bins))
        tm.assert_series_equal(result, expected)

        pytest.raises(ValueError, cut, values, bins)
        pytest.raises(ValueError, cut, values, bins, duplicates='raise')

        # invalid
        pytest.raises(ValueError, cut, values, bins, duplicates='foo')

    def test_qcut_duplicates_bin(self):
        # GH 7751
        values = [0, 0, 0, 0, 1, 2, 3]
        expected = IntervalIndex([Interval(-0.001, 1), Interval(1, 3)])

        result = qcut(values, 3, duplicates='drop')
        tm.assert_index_equal(result.categories, expected)

        pytest.raises(ValueError, qcut, values, 3)
        pytest.raises(ValueError, qcut, values, 3, duplicates='raise')

        # invalid
        pytest.raises(ValueError, qcut, values, 3, duplicates='foo')

    def test_single_quantile(self):
        # issue 15431
        expected = Series([0, 0])

        s = Series([9., 9.])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(8.999, 9.0),
                                   Interval(8.999, 9.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        s = Series([-9., -9.])
        expected = Series([0, 0])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(-9.001, -9.0),
                                   Interval(-9.001, -9.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        s = Series([0., 0.])
        expected = Series([0, 0])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(-0.001, 0.0),
                                   Interval(-0.001, 0.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        s = Series([9])
        expected = Series([0])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(8.999, 9.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        s = Series([-9])
        expected = Series([0])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(-9.001, -9.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

        s = Series([0])
        expected = Series([0])
        result = qcut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)
        result = qcut(s, 1)
        intervals = IntervalIndex([Interval(-0.001, 0.0)], closed='right')
        expected = Series(intervals).astype(CDT(ordered=True))
        tm.assert_series_equal(result, expected)

    def test_single_bin(self):
        # issue 14652
        expected = Series([0, 0])

        s = Series([9., 9.])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

        s = Series([-9., -9.])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

        expected = Series([0])

        s = Series([9])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

        s = Series([-9])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

        # issue 15428
        expected = Series([0, 0])

        s = Series([0., 0.])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

        expected = Series([0])

        s = Series([0])
        result = cut(s, 1, labels=False)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "array_1_writeable, array_2_writeable",
        [(True, True), (True, False), (False, False)])
    def test_cut_read_only(self, array_1_writeable, array_2_writeable):
        # issue 18773
        array_1 = np.arange(0, 100, 10)
        array_1.flags.writeable = array_1_writeable

        array_2 = np.arange(0, 100, 10)
        array_2.flags.writeable = array_2_writeable

        hundred_elements = np.arange(100)

        tm.assert_categorical_equal(cut(hundred_elements, array_1),
                                    cut(hundred_elements, array_2))


class TestDatelike(object):

    @pytest.mark.parametrize('s', [
        Series(DatetimeIndex(['20180101', NaT, '20180103'])),
        Series(TimedeltaIndex(['0 days', NaT, '2 days']))],
        ids=lambda x: str(x.dtype))
    def test_qcut_nat(self, s):
        # GH 19768
        intervals = IntervalIndex.from_tuples(
            [(s[0] - Nano(), s[2] - Day()), np.nan, (s[2] - Day(), s[2])])
        expected = Series(Categorical(intervals, ordered=True))
        result = qcut(s, 2)
        tm.assert_series_equal(result, expected)

    def test_datetime_cut(self):
        # GH 14714
        # testing for time data to be present as series
        data = to_datetime(Series(['2013-01-01', '2013-01-02', '2013-01-03']))

        result, bins = cut(data, 3, retbins=True)
        expected = (
            Series(IntervalIndex([
                Interval(Timestamp('2012-12-31 23:57:07.200000'),
                         Timestamp('2013-01-01 16:00:00')),
                Interval(Timestamp('2013-01-01 16:00:00'),
                         Timestamp('2013-01-02 08:00:00')),
                Interval(Timestamp('2013-01-02 08:00:00'),
                         Timestamp('2013-01-03 00:00:00'))]))
            .astype(CDT(ordered=True)))

        tm.assert_series_equal(result, expected)

        # testing for time data to be present as list
        data = [np.datetime64('2013-01-01'), np.datetime64('2013-01-02'),
                np.datetime64('2013-01-03')]
        result, bins = cut(data, 3, retbins=True)
        tm.assert_series_equal(Series(result), expected)

        # testing for time data to be present as ndarray
        data = np.array([np.datetime64('2013-01-01'),
                         np.datetime64('2013-01-02'),
                         np.datetime64('2013-01-03')])
        result, bins = cut(data, 3, retbins=True)
        tm.assert_series_equal(Series(result), expected)

        # testing for time data to be present as datetime index
        data = DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'])
        result, bins = cut(data, 3, retbins=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_datetimetz_cut(self, bins, box):
        # 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)

    @pytest.mark.parametrize('bins', [3, np.linspace(0, 1, 4)])
    def test_datetimetz_qcut(self, bins):
        # GH 19872
        tz = 'US/Eastern'
        s = Series(date_range('20130101', periods=3, tz=tz))
        result = qcut(s, bins)
        expected = (
            Series(IntervalIndex([
                Interval(Timestamp('2012-12-31 23:59:59.999999999', 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_bin(self):
        data = [np.datetime64('2012-12-13'), np.datetime64('2012-12-15')]
        bin_data = ['2012-12-12', '2012-12-14', '2012-12-16']
        expected = (
            Series(IntervalIndex([
                Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
                Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2]))]))
            .astype(CDT(ordered=True)))

        for conv in [Timestamp, Timestamp, np.datetime64]:
            bins = [conv(v) for v in bin_data]
            result = cut(data, bins=bins)
            tm.assert_series_equal(Series(result), expected)

        bin_pydatetime = [Timestamp(v).to_pydatetime() for v in bin_data]
        result = cut(data, bins=bin_pydatetime)
        tm.assert_series_equal(Series(result), expected)

        bins = to_datetime(bin_data)
        result = cut(data, bins=bin_pydatetime)
        tm.assert_series_equal(Series(result), expected)

    def test_datetime_nan(self):

        def f():
            cut(date_range('20130101', periods=3), bins=[0, 2, 4])
        pytest.raises(ValueError, f)

        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(self, tz):
        # GH 19891
        s = Series(date_range('20180101', periods=3, tz=tz))
        result, result_bins = cut(s, 2, retbins=True)
        expected = cut(s, 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(self):
        # GH 19891
        s = Series(timedelta_range('1day', periods=3))
        result, result_bins = cut(s, 2, retbins=True)
        expected = cut(s, 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('arg, expected_bins', [
        [timedelta_range('1day', periods=3),
         TimedeltaIndex(['1 days', '2 days', '3 days'])],
        [date_range('20180101', periods=3),
         DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'])]])
    def test_datelike_qcut_bins(self, arg, expected_bins):
        # GH 19891
        s = Series(arg)
        result, result_bins = qcut(s, 2, retbins=True)
        tm.assert_index_equal(result_bins, expected_bins)