projektAI/venv/Lib/site-packages/pandas/tests/series/methods/test_quantile.py

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2021-06-06 22:13:05 +02:00
import numpy as np
import pytest
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import Index, Series
import pandas._testing as tm
from pandas.core.indexes.datetimes import Timestamp
class TestSeriesQuantile:
def test_quantile(self, datetime_series):
q = datetime_series.quantile(0.1)
assert q == np.percentile(datetime_series.dropna(), 10)
q = datetime_series.quantile(0.9)
assert q == np.percentile(datetime_series.dropna(), 90)
# object dtype
q = Series(datetime_series, dtype=object).quantile(0.9)
assert q == np.percentile(datetime_series.dropna(), 90)
# datetime64[ns] dtype
dts = datetime_series.index.to_series()
q = dts.quantile(0.2)
assert q == Timestamp("2000-01-10 19:12:00")
# timedelta64[ns] dtype
tds = dts.diff()
q = tds.quantile(0.25)
assert q == pd.to_timedelta("24:00:00")
# GH7661
result = Series([np.timedelta64("NaT")]).sum()
assert result == pd.Timedelta(0)
msg = "percentiles should all be in the interval \\[0, 1\\]"
for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
with pytest.raises(ValueError, match=msg):
datetime_series.quantile(invalid)
def test_quantile_multi(self, datetime_series):
qs = [0.1, 0.9]
result = datetime_series.quantile(qs)
expected = Series(
[
np.percentile(datetime_series.dropna(), 10),
np.percentile(datetime_series.dropna(), 90),
],
index=qs,
name=datetime_series.name,
)
tm.assert_series_equal(result, expected)
dts = datetime_series.index.to_series()
dts.name = "xxx"
result = dts.quantile((0.2, 0.2))
expected = Series(
[Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")],
index=[0.2, 0.2],
name="xxx",
)
tm.assert_series_equal(result, expected)
result = datetime_series.quantile([])
expected = Series(
[], name=datetime_series.name, index=Index([], dtype=float), dtype="float64"
)
tm.assert_series_equal(result, expected)
def test_quantile_interpolation(self, datetime_series):
# see gh-10174
# interpolation = linear (default case)
q = datetime_series.quantile(0.1, interpolation="linear")
assert q == np.percentile(datetime_series.dropna(), 10)
q1 = datetime_series.quantile(0.1)
assert q1 == np.percentile(datetime_series.dropna(), 10)
# test with and without interpolation keyword
assert q == q1
def test_quantile_interpolation_dtype(self):
# GH #10174
# interpolation = linear (default case)
q = Series([1, 3, 4]).quantile(0.5, interpolation="lower")
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
q = Series([1, 3, 4]).quantile(0.5, interpolation="higher")
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
def test_quantile_nan(self):
# GH 13098
s = Series([1, 2, 3, 4, np.nan])
result = s.quantile(0.5)
expected = 2.5
assert result == expected
# all nan/empty
s1 = Series([], dtype=object)
cases = [s1, Series([np.nan, np.nan])]
for s in cases:
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
tm.assert_series_equal(res, Series([np.nan], index=[0.5]))
res = s.quantile([0.2, 0.3])
tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3]))
@pytest.mark.parametrize(
"case",
[
[
Timestamp("2011-01-01"),
Timestamp("2011-01-02"),
Timestamp("2011-01-03"),
],
[
Timestamp("2011-01-01", tz="US/Eastern"),
Timestamp("2011-01-02", tz="US/Eastern"),
Timestamp("2011-01-03", tz="US/Eastern"),
],
[pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
# NaT
[
Timestamp("2011-01-01"),
Timestamp("2011-01-02"),
Timestamp("2011-01-03"),
pd.NaT,
],
[
Timestamp("2011-01-01", tz="US/Eastern"),
Timestamp("2011-01-02", tz="US/Eastern"),
Timestamp("2011-01-03", tz="US/Eastern"),
pd.NaT,
],
[
pd.Timedelta("1 days"),
pd.Timedelta("2 days"),
pd.Timedelta("3 days"),
pd.NaT,
],
],
)
def test_quantile_box(self, case):
s = Series(case, name="XXX")
res = s.quantile(0.5)
assert res == case[1]
res = s.quantile([0.5])
exp = Series([case[1]], index=[0.5], name="XXX")
tm.assert_series_equal(res, exp)
def test_datetime_timedelta_quantiles(self):
# covers #9694
assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5))
assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5))
def test_quantile_nat(self):
res = Series([pd.NaT, pd.NaT]).quantile(0.5)
assert res is pd.NaT
res = Series([pd.NaT, pd.NaT]).quantile([0.5])
tm.assert_series_equal(res, Series([pd.NaT], index=[0.5]))
@pytest.mark.parametrize(
"values, dtype",
[([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")],
)
def test_quantile_sparse(self, values, dtype):
ser = Series(values, dtype=dtype)
result = ser.quantile([0.5])
expected = Series(np.asarray(ser)).quantile([0.5])
tm.assert_series_equal(result, expected)
def test_quantile_empty(self):
# floats
s = Series([], dtype="float64")
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
exp = Series([np.nan], index=[0.5])
tm.assert_series_equal(res, exp)
# int
s = Series([], dtype="int64")
res = s.quantile(0.5)
assert np.isnan(res)
res = s.quantile([0.5])
exp = Series([np.nan], index=[0.5])
tm.assert_series_equal(res, exp)
# datetime
s = Series([], dtype="datetime64[ns]")
res = s.quantile(0.5)
assert res is pd.NaT
res = s.quantile([0.5])
exp = Series([pd.NaT], index=[0.5])
tm.assert_series_equal(res, exp)