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import os
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DatetimeIndex,
Interval,
IntervalIndex,
NaT,
Series,
Timedelta,
TimedeltaIndex,
Timestamp,
cut,
date_range,
isna,
qcut,
timedelta_range,
)
import pandas._testing as tm
from pandas.api.types import CategoricalDtype
from pandas.tseries.offsets import Day
def test_qcut():
arr = np.random.default_rng(2).standard_normal(1000)
# We store the bins as Index that have been
# rounded to comparisons are a bit tricky.
labels, _ = qcut(arr, 4, retbins=True)
ex_bins = np.quantile(arr, [0, 0.25, 0.5, 0.75, 1.0])
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():
arr = np.random.default_rng(2).standard_normal(1000)
factor = qcut(arr, 10, labels=False)
assert len(np.unique(factor)) == 10
def test_qcut_specify_quantiles():
arr = np.random.default_rng(2).standard_normal(100)
factor = qcut(arr, [0, 0.25, 0.5, 0.75, 1.0])
expected = qcut(arr, 4)
tm.assert_categorical_equal(factor, expected)
def test_qcut_all_bins_same():
with pytest.raises(ValueError, match="edges.*unique"):
qcut([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3)
def test_qcut_include_lowest():
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():
arr = np.random.default_rng(2).standard_normal(100)
arr[:20] = np.nan
result = qcut(arr, 4)
assert isna(result[:20]).all()
def test_qcut_index():
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_qcut_binning_issues(datapath):
# see gh-1978, gh-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_qcut_return_intervals():
ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
res = qcut(ser, [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(
CategoricalDtype(ordered=True)
)
tm.assert_series_equal(res, exp)
@pytest.mark.parametrize("labels", ["foo", 1, True])
def test_qcut_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):
qcut(values, 4, labels=labels)
@pytest.mark.parametrize("labels", [["a", "b", "c"], list(range(3))])
def test_qcut_wrong_length_labels(labels):
# GH 13318
values = range(10)
msg = "Bin labels must be one fewer than the number of bin edges"
with pytest.raises(ValueError, match=msg):
qcut(values, 4, labels=labels)
@pytest.mark.parametrize(
"labels, expected",
[
(["a", "b", "c"], Categorical(["a", "b", "c"], ordered=True)),
(list(range(3)), Categorical([0, 1, 2], ordered=True)),
],
)
def test_qcut_list_like_labels(labels, expected):
# GH 13318
values = range(3)
result = qcut(values, 3, labels=labels)
tm.assert_categorical_equal(result, expected)
@pytest.mark.parametrize(
"kwargs,msg",
[
({"duplicates": "drop"}, None),
({}, "Bin edges must be unique"),
({"duplicates": "raise"}, "Bin edges must be unique"),
({"duplicates": "foo"}, "invalid value for 'duplicates' parameter"),
],
)
def test_qcut_duplicates_bin(kwargs, msg):
# see gh-7751
values = [0, 0, 0, 0, 1, 2, 3]
if msg is not None:
with pytest.raises(ValueError, match=msg):
qcut(values, 3, **kwargs)
else:
result = qcut(values, 3, **kwargs)
expected = IntervalIndex([Interval(-0.001, 1), Interval(1, 3)])
tm.assert_index_equal(result.categories, expected)
@pytest.mark.parametrize(
"data,start,end", [(9.0, 8.999, 9.0), (0.0, -0.001, 0.0), (-9.0, -9.001, -9.0)]
)
@pytest.mark.parametrize("length", [1, 2])
@pytest.mark.parametrize("labels", [None, False])
def test_single_quantile(data, start, end, length, labels):
# see gh-15431
ser = Series([data] * length)
result = qcut(ser, 1, labels=labels)
if labels is None:
intervals = IntervalIndex([Interval(start, end)] * length, closed="right")
expected = Series(intervals).astype(CategoricalDtype(ordered=True))
else:
expected = Series([0] * length, dtype=np.intp)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"ser",
[
Series(DatetimeIndex(["20180101", NaT, "20180103"])),
Series(TimedeltaIndex(["0 days", NaT, "2 days"])),
],
ids=lambda x: str(x.dtype),
)
def test_qcut_nat(ser, unit):
# see gh-19768
ser = ser.dt.as_unit(unit)
td = Timedelta(1, unit=unit).as_unit(unit)
left = Series([ser[0] - td, np.nan, ser[2] - Day()], dtype=ser.dtype)
right = Series([ser[2] - Day(), np.nan, ser[2]], dtype=ser.dtype)
intervals = IntervalIndex.from_arrays(left, right)
expected = Series(Categorical(intervals, ordered=True))
result = qcut(ser, 2)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("bins", [3, np.linspace(0, 1, 4)])
def test_datetime_tz_qcut(bins):
# see gh-19872
tz = "US/Eastern"
ser = Series(date_range("20130101", periods=3, tz=tz))
result = qcut(ser, 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(CategoricalDtype(ordered=True))
tm.assert_series_equal(result, expected)
@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_date_like_qcut_bins(arg, expected_bins):
# see gh-19891
ser = Series(arg)
result, result_bins = qcut(ser, 2, retbins=True)
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_qcut_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 = qcut(data_expected, bins, duplicates="drop")
result = qcut(data_result, bins, duplicates="drop")
compare(result, expected)
@pytest.mark.parametrize("q", [2, 5, 10])
def test_qcut_nullable_integer(q, any_numeric_ea_dtype):
arr = pd.array(np.arange(100), dtype=any_numeric_ea_dtype)
arr[::2] = pd.NA
result = qcut(arr, q)
expected = qcut(arr.astype(float), q)
tm.assert_categorical_equal(result, expected)
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