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_isin.py (3445B)

import numpy as np
import pytest

import pandas as pd
from pandas import Series, date_range
import pandas._testing as tm

class TestSeriesIsIn:
    def test_isin(self):
        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])

result = s.isin(["A", "C"])
        expected = Series([True, False, True, False, False, False, True, True])
        tm.assert_series_equal(result, expected)

# GH#16012
        # This specific issue has to have a series over 1e6 in len, but the
        # comparison array (in_list) must be large enough so that numpy doesn't
        # do a manual masking trick that will avoid this issue altogether
        s = Series(list("abcdefghijk" * 10 ** 5))
        # If numpy doesn't do the manual comparison/mask, these
        # unorderable mixed types are what cause the exception in numpy
        in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6

assert s.isin(in_list).sum() == 200000

def test_isin_with_string_scalar(self):
        # GH#4763
        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
        msg = (
            r"only list-like objects are allowed to be passed to isin\(\), "
            r"you passed a \[str\]"
        )
        with pytest.raises(TypeError, match=msg):
            s.isin("a")

s = Series(["aaa", "b", "c"])
        with pytest.raises(TypeError, match=msg):
            s.isin("aaa")

def test_isin_with_i8(self):
        # GH#5021

expected = Series([True, True, False, False, False])
        expected2 = Series([False, True, False, False, False])

# datetime64[ns]
        s = Series(date_range("jan-01-2013", "jan-05-2013"))

result = s.isin(s[0:2])
        tm.assert_series_equal(result, expected)

result = s.isin(s[0:2].values)
        tm.assert_series_equal(result, expected)

# fails on dtype conversion in the first place
        result = s.isin(s[0:2].values.astype("datetime64[D]"))
        tm.assert_series_equal(result, expected)

result = s.isin([s[1]])
        tm.assert_series_equal(result, expected2)

result = s.isin([np.datetime64(s[1])])
        tm.assert_series_equal(result, expected2)

result = s.isin(set(s[0:2]))
        tm.assert_series_equal(result, expected)

# timedelta64[ns]
        s = Series(pd.to_timedelta(range(5), unit="d"))
        result = s.isin(s[0:2])
        tm.assert_series_equal(result, expected)

@pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
    def test_isin_empty(self, empty):
        # see GH#16991
        s = Series(["a", "b"])
        expected = Series([False, False])

result = s.isin(empty)
        tm.assert_series_equal(expected, result)

def test_isin_read_only(self):
        # https://github.com/pandas-dev/pandas/issues/37174
        arr = np.array([1, 2, 3])
        arr.setflags(write=False)
        s = Series([1, 2, 3])
        result = s.isin(arr)
        expected = Series([True, True, True])
        tm.assert_series_equal(result, expected)

@pytest.mark.slow
def test_isin_large_series_mixed_dtypes_and_nan():
    # https://github.com/pandas-dev/pandas/issues/37094
    # combination of object dtype for the values and > 1_000_000 elements
    ser = Series([1, 2, np.nan] * 1_000_000)
    result = ser.isin({"foo", "bar"})
    expected = Series([False] * 3 * 1_000_000)
    tm.assert_series_equal(result, expected)

import numpy as np import pytest import pandas as pd from pandas import Series, date_range import pandas._testing as tm class TestSeriesIsIn: def test_isin(self): s = Series(["A", "B", "C", "a", "B", "B", "A", "C"]) result = s.isin(["A", "C"]) expected = Series([True, False, True, False, False, False, True, True]) tm.assert_series_equal(result, expected) # GH#16012 # This specific issue has to have a series over 1e6 in len, but the # comparison array (in_list) must be large enough so that numpy doesn't # do a manual masking trick that will avoid this issue altogether s = Series(list("abcdefghijk" * 10 ** 5)) # If numpy doesn't do the manual comparison/mask, these # unorderable mixed types are what cause the exception in numpy in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6 assert s.isin(in_list).sum() == 200000 def test_isin_with_string_scalar(self): # GH#4763 s = Series(["A", "B", "C", "a", "B", "B", "A", "C"]) msg = ( r"only list-like objects are allowed to be passed to isin\(\), " r"you passed a \[str\]" ) with pytest.raises(TypeError, match=msg): s.isin("a") s = Series(["aaa", "b", "c"]) with pytest.raises(TypeError, match=msg): s.isin("aaa") def test_isin_with_i8(self): # GH#5021 expected = Series([True, True, False, False, False]) expected2 = Series([False, True, False, False, False]) # datetime64[ns] s = Series(date_range("jan-01-2013", "jan-05-2013")) result = s.isin(s[0:2]) tm.assert_series_equal(result, expected) result = s.isin(s[0:2].values) tm.assert_series_equal(result, expected) # fails on dtype conversion in the first place result = s.isin(s[0:2].values.astype("datetime64[D]")) tm.assert_series_equal(result, expected) result = s.isin([s[1]]) tm.assert_series_equal(result, expected2) result = s.isin([np.datetime64(s[1])]) tm.assert_series_equal(result, expected2) result = s.isin(set(s[0:2])) tm.assert_series_equal(result, expected) # timedelta64[ns] s = Series(pd.to_timedelta(range(5), unit="d")) result = s.isin(s[0:2]) tm.assert_series_equal(result, expected) @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])]) def test_isin_empty(self, empty): # see GH#16991 s = Series(["a", "b"]) expected = Series([False, False]) result = s.isin(empty) tm.assert_series_equal(expected, result) def test_isin_read_only(self): # https://github.com/pandas-dev/pandas/issues/37174 arr = np.array([1, 2, 3]) arr.setflags(write=False) s = Series([1, 2, 3]) result = s.isin(arr) expected = Series([True, True, True]) tm.assert_series_equal(result, expected) @pytest.mark.slow def test_isin_large_series_mixed_dtypes_and_nan(): # https://github.com/pandas-dev/pandas/issues/37094 # combination of object dtype for the values and > 1_000_000 elements ser = Series([1, 2, np.nan] * 1_000_000) result = ser.isin({"foo", "bar"}) expected = Series([False] * 3 * 1_000_000) tm.assert_series_equal(result, expected)