laywerrobot/lib/python3.6/site-packages/pandas/tests/series/test_quantile.py

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2020-08-27 21:55:39 +02:00
# coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pandas as pd
from pandas import Index, Series
from pandas.core.indexes.datetimes import Timestamp
from pandas.core.dtypes.common import is_integer
import pandas.util.testing as tm
from .common import TestData
class TestSeriesQuantile(TestData):
def test_quantile(self):
q = self.ts.quantile(0.1)
assert q == np.percentile(self.ts.dropna(), 10)
q = self.ts.quantile(0.9)
assert q == np.percentile(self.ts.dropna(), 90)
# object dtype
q = Series(self.ts, dtype=object).quantile(0.9)
assert q == np.percentile(self.ts.dropna(), 90)
# datetime64[ns] dtype
dts = self.ts.index.to_series()
q = dts.quantile(.2)
assert q == Timestamp('2000-01-10 19:12:00')
# timedelta64[ns] dtype
tds = dts.diff()
q = tds.quantile(.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 tm.assert_raises_regex(ValueError, msg):
self.ts.quantile(invalid)
def test_quantile_multi(self):
qs = [.1, .9]
result = self.ts.quantile(qs)
expected = pd.Series([np.percentile(self.ts.dropna(), 10),
np.percentile(self.ts.dropna(), 90)],
index=qs, name=self.ts.name)
tm.assert_series_equal(result, expected)
dts = self.ts.index.to_series()
dts.name = 'xxx'
result = dts.quantile((.2, .2))
expected = Series([Timestamp('2000-01-10 19:12:00'),
Timestamp('2000-01-10 19:12:00')],
index=[.2, .2], name='xxx')
tm.assert_series_equal(result, expected)
result = self.ts.quantile([])
expected = pd.Series([], name=self.ts.name, index=Index(
[], dtype=float))
tm.assert_series_equal(result, expected)
def test_quantile_interpolation(self):
# see gh-10174
# interpolation = linear (default case)
q = self.ts.quantile(0.1, interpolation='linear')
assert q == np.percentile(self.ts.dropna(), 10)
q1 = self.ts.quantile(0.1)
assert q1 == np.percentile(self.ts.dropna(), 10)
# test with and without interpolation keyword
assert q == q1
def test_quantile_interpolation_dtype(self):
# GH #10174
# interpolation = linear (default case)
q = pd.Series([1, 3, 4]).quantile(0.5, interpolation='lower')
assert q == np.percentile(np.array([1, 3, 4]), 50)
assert is_integer(q)
q = pd.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 = pd.Series([1, 2, 3, 4, np.nan])
result = s.quantile(0.5)
expected = 2.5
assert result == expected
# all nan/empty
cases = [Series([]), 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, pd.Series([np.nan], index=[0.5]))
res = s.quantile([0.2, 0.3])
tm.assert_series_equal(res, pd.Series([np.nan, np.nan],
index=[0.2, 0.3]))
def test_quantile_box(self):
cases = [[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-03')],
[pd.Timestamp('2011-01-01', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-03', tz='US/Eastern')],
[pd.Timedelta('1 days'), pd.Timedelta('2 days'),
pd.Timedelta('3 days')],
# NaT
[pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02'),
pd.Timestamp('2011-01-03'), pd.NaT],
[pd.Timestamp('2011-01-01', tz='US/Eastern'),
pd.Timestamp('2011-01-02', tz='US/Eastern'),
pd.Timestamp('2011-01-03', tz='US/Eastern'), pd.NaT],
[pd.Timedelta('1 days'), pd.Timedelta('2 days'),
pd.Timedelta('3 days'), pd.NaT]]
for case in cases:
s = pd.Series(case, name='XXX')
res = s.quantile(0.5)
assert res == case[1]
res = s.quantile([0.5])
exp = pd.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(.5))
assert pd.isna(Series([], dtype='m8[ns]').quantile(.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, pd.Series([pd.NaT], index=[0.5]))
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)