FM / methods

/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/

/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_align.py	5359	0644	edit dl rm
test_append.py	9600	0644	edit dl rm
test_argsort.py	2249	0644	edit dl rm
test_asfreq.py	3616	0644	edit dl rm
test_asof.py	5515	0644	edit dl rm
test_astype.py	2628	0644	edit dl rm
test_at_time.py	2629	0644	edit dl rm
test_autocorr.py	999	0644	edit dl rm
test_between.py	1197	0644	edit dl rm
test_between_time.py	5086	0644	edit dl rm
test_clip.py	3384	0644	edit dl rm
test_combine.py	627	0644	edit dl rm
test_combine_first.py	3411	0644	edit dl rm
test_compare.py	3734	0644	edit dl rm
test_convert_dtypes.py	9914	0644	edit dl rm
test_count.py	1123	0644	edit dl rm
test_cov_corr.py	5215	0644	edit dl rm
test_describe.py	4640	0644	edit dl rm
test_diff.py	2331	0644	edit dl rm
test_drop.py	2986	0644	edit dl rm
test_droplevel.py	629	0644	edit dl rm
test_drop_duplicates.py	6850	0644	edit dl rm
test_duplicated.py	996	0644	edit dl rm
test_equals.py	1295	0644	edit dl rm
test_explode.py	3538	0644	edit dl rm
test_fillna.py	6513	0644	edit dl rm
test_first_and_last.py	2042	0644	edit dl rm
test_head_tail.py	343	0644	edit dl rm
test_interpolate.py	27757	0644	edit dl rm
test_isin.py	3445	0644	edit dl rm
test_nlargest.py	7121	0644	edit dl rm
test_pct_change.py	2968	0644	edit dl rm
test_quantile.py	6810	0644	edit dl rm
test_rank.py	20287	0644	edit dl rm
test_reindex_like.py	1245	0644	edit dl rm
test_rename.py	3378	0644	edit dl rm
test_rename_axis.py	1503	0644	edit dl rm
test_replace.py	15559	0644	edit dl rm
test_reset_index.py	4636	0644	edit dl rm
test_round.py	1970	0644	edit dl rm
test_searchsorted.py	2121	0644	edit dl rm
test_shift.py	12595	0644	edit dl rm
test_sort_index.py	12277	0644	edit dl rm
test_sort_values.py	7981	0644	edit dl rm
test_to_dict.py	742	0644	edit dl rm
test_to_period.py	1755	0644	edit dl rm
test_to_timestamp.py	2616	0644	edit dl rm
test_truncate.py	5557	0644	edit dl rm
test_tz_convert.py	1008	0644	edit dl rm
test_tz_localize.py	3108	0644	edit dl rm
test_unstack.py	4208	0644	edit dl rm
test_update.py	4213	0644	edit dl rm
test_value_counts.py	8136	0644	edit dl rm
__init__.py	225	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/test_quantile.py (6810B)

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 = pd.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 = pd.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 = 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
        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, 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]))

@pytest.mark.parametrize(
        "case",
        [
            [
                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,
            ],
        ],
    )
    def test_quantile_box(self, case):
        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(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, pd.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 = pd.Series(values, dtype=dtype)
        result = ser.quantile([0.5])
        expected = pd.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)

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 = pd.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 = pd.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 = 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 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, 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])) @pytest.mark.parametrize( "case", [ [ 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, ], ], ) def test_quantile_box(self, case): 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(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, pd.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 = pd.Series(values, dtype=dtype) result = ser.quantile([0.5]) expected = pd.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)