303 lines
8.0 KiB
Python
303 lines
8.0 KiB
Python
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import os
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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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DatetimeIndex,
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Interval,
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IntervalIndex,
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NaT,
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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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)
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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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from pandas.tseries.offsets import (
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Day,
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Nano,
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)
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def test_qcut():
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arr = np.random.randn(1000)
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# We store the bins as Index that have been
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# rounded to comparisons are a bit tricky.
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labels, _ = qcut(arr, 4, retbins=True)
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ex_bins = np.quantile(arr, [0, 0.25, 0.5, 0.75, 1.0])
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result = labels.categories.left.values
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assert np.allclose(result, ex_bins[:-1], atol=1e-2)
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result = labels.categories.right.values
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assert np.allclose(result, ex_bins[1:], atol=1e-2)
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ex_levels = cut(arr, ex_bins, include_lowest=True)
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tm.assert_categorical_equal(labels, ex_levels)
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def test_qcut_bounds():
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arr = np.random.randn(1000)
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factor = qcut(arr, 10, labels=False)
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assert len(np.unique(factor)) == 10
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def test_qcut_specify_quantiles():
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arr = np.random.randn(100)
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factor = qcut(arr, [0, 0.25, 0.5, 0.75, 1.0])
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expected = qcut(arr, 4)
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tm.assert_categorical_equal(factor, expected)
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def test_qcut_all_bins_same():
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with pytest.raises(ValueError, match="edges.*unique"):
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qcut([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3)
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def test_qcut_include_lowest():
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values = np.arange(10)
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ii = qcut(values, 4)
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ex_levels = IntervalIndex(
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[
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Interval(-0.001, 2.25),
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Interval(2.25, 4.5),
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Interval(4.5, 6.75),
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Interval(6.75, 9),
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]
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)
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tm.assert_index_equal(ii.categories, ex_levels)
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def test_qcut_nas():
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arr = np.random.randn(100)
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arr[:20] = np.nan
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result = qcut(arr, 4)
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assert isna(result[:20]).all()
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def test_qcut_index():
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result = qcut([0, 2], 2)
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intervals = [Interval(-0.001, 1), Interval(1, 2)]
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expected = Categorical(intervals, ordered=True)
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tm.assert_categorical_equal(result, expected)
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def test_qcut_binning_issues(datapath):
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# see gh-1978, gh-1979
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cut_file = datapath(os.path.join("reshape", "data", "cut_data.csv"))
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arr = np.loadtxt(cut_file)
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result = qcut(arr, 20)
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starts = []
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ends = []
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for lev in np.unique(result):
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s = lev.left
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e = lev.right
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assert s != e
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starts.append(float(s))
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ends.append(float(e))
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for (sp, sn), (ep, en) in zip(
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zip(starts[:-1], starts[1:]), zip(ends[:-1], ends[1:])
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):
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assert sp < sn
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assert ep < en
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assert ep <= sn
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def test_qcut_return_intervals():
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ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
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res = qcut(ser, [0, 0.333, 0.666, 1])
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exp_levels = np.array(
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[Interval(-0.001, 2.664), Interval(2.664, 5.328), Interval(5.328, 8)]
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)
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exp = Series(exp_levels.take([0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(CDT(ordered=True))
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tm.assert_series_equal(res, exp)
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@pytest.mark.parametrize("labels", ["foo", 1, True])
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def test_qcut_incorrect_labels(labels):
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# GH 13318
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values = range(5)
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msg = "Bin labels must either be False, None or passed in as a list-like argument"
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with pytest.raises(ValueError, match=msg):
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qcut(values, 4, labels=labels)
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@pytest.mark.parametrize("labels", [["a", "b", "c"], list(range(3))])
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def test_qcut_wrong_length_labels(labels):
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# GH 13318
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values = range(10)
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msg = "Bin labels must be one fewer than the number of bin edges"
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with pytest.raises(ValueError, match=msg):
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qcut(values, 4, labels=labels)
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@pytest.mark.parametrize(
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"labels, expected",
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[
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(["a", "b", "c"], Categorical(["a", "b", "c"], ordered=True)),
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(list(range(3)), Categorical([0, 1, 2], ordered=True)),
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],
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)
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def test_qcut_list_like_labels(labels, expected):
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# GH 13318
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values = range(3)
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result = qcut(values, 3, labels=labels)
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tm.assert_categorical_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_qcut_duplicates_bin(kwargs, msg):
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# see gh-7751
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values = [0, 0, 0, 0, 1, 2, 3]
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if msg is not None:
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with pytest.raises(ValueError, match=msg):
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qcut(values, 3, **kwargs)
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else:
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result = qcut(values, 3, **kwargs)
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expected = IntervalIndex([Interval(-0.001, 1), Interval(1, 3)])
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tm.assert_index_equal(result.categories, expected)
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@pytest.mark.parametrize(
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"data,start,end", [(9.0, 8.999, 9.0), (0.0, -0.001, 0.0), (-9.0, -9.001, -9.0)]
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)
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@pytest.mark.parametrize("length", [1, 2])
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@pytest.mark.parametrize("labels", [None, False])
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def test_single_quantile(data, start, end, length, labels):
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# see gh-15431
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ser = Series([data] * length)
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result = qcut(ser, 1, labels=labels)
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if labels is None:
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intervals = IntervalIndex([Interval(start, end)] * length, closed="right")
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expected = Series(intervals).astype(CDT(ordered=True))
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else:
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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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@pytest.mark.parametrize(
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"ser",
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[
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Series(DatetimeIndex(["20180101", NaT, "20180103"])),
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Series(TimedeltaIndex(["0 days", NaT, "2 days"])),
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],
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ids=lambda x: str(x.dtype),
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)
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def test_qcut_nat(ser):
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# see gh-19768
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intervals = IntervalIndex.from_tuples(
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[(ser[0] - Nano(), ser[2] - Day()), np.nan, (ser[2] - Day(), ser[2])]
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)
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expected = Series(Categorical(intervals, ordered=True))
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result = qcut(ser, 2)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("bins", [3, np.linspace(0, 1, 4)])
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def test_datetime_tz_qcut(bins):
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# see gh-19872
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tz = "US/Eastern"
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ser = Series(date_range("20130101", periods=3, tz=tz))
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result = qcut(ser, bins)
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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 23:59:59.999999999", tz=tz),
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Timestamp("2013-01-01 16:00:00", tz=tz),
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),
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Interval(
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Timestamp("2013-01-01 16:00:00", tz=tz),
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Timestamp("2013-01-02 08:00:00", tz=tz),
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),
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Interval(
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Timestamp("2013-01-02 08:00:00", tz=tz),
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Timestamp("2013-01-03 00:00:00", tz=tz),
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),
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]
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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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@pytest.mark.parametrize(
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"arg,expected_bins",
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[
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[
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timedelta_range("1day", periods=3),
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TimedeltaIndex(["1 days", "2 days", "3 days"]),
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],
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[
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date_range("20180101", periods=3),
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DatetimeIndex(["2018-01-01", "2018-01-02", "2018-01-03"]),
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],
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],
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)
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def test_date_like_qcut_bins(arg, expected_bins):
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# see gh-19891
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ser = Series(arg)
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result, result_bins = qcut(ser, 2, retbins=True)
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tm.assert_index_equal(result_bins, expected_bins)
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@pytest.mark.parametrize("bins", [6, 7])
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@pytest.mark.parametrize(
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"box, compare",
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[
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(Series, tm.assert_series_equal),
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(np.array, tm.assert_categorical_equal),
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(list, tm.assert_equal),
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],
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)
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def test_qcut_bool_coercion_to_int(bins, box, compare):
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# issue 20303
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data_expected = box([0, 1, 1, 0, 1] * 10)
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data_result = box([False, True, True, False, True] * 10)
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expected = qcut(data_expected, bins, duplicates="drop")
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result = qcut(data_result, bins, duplicates="drop")
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compare(result, expected)
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@pytest.mark.parametrize("q", [2, 5, 10])
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def test_qcut_nullable_integer(q, any_numeric_ea_dtype):
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arr = pd.array(np.arange(100), dtype=any_numeric_ea_dtype)
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arr[::2] = pd.NA
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result = qcut(arr, q)
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expected = qcut(arr.astype(float), q)
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tm.assert_categorical_equal(result, expected)
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