FM / series

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

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

Name	Size	Mode	Actions
apply/	-	0755	rm
indexing/	-	0755	rm
methods/	-	0755	rm
__pycache__/	-	0755	rm
conftest.py	415	0644	edit dl rm
test_alter_axes.py	1531	0644	edit dl rm
test_analytics.py	6953	0644	edit dl rm
test_api.py	26347	0644	edit dl rm
test_arithmetic.py	24869	0644	edit dl rm
test_block_internals.py	1418	0644	edit dl rm
test_combine_concat.py	4032	0644	edit dl rm
test_constructors.py	54567	0644	edit dl rm
test_cumulative.py	5550	0644	edit dl rm
test_datetime_values.py	25692	0644	edit dl rm
test_dtypes.py	17819	0644	edit dl rm
test_duplicates.py	2384	0644	edit dl rm
test_internals.py	8912	0644	edit dl rm
test_io.py	8195	0644	edit dl rm
test_missing.py	27622	0644	edit dl rm
test_operators.py	20353	0644	edit dl rm
test_period.py	3915	0644	edit dl rm
test_reductions.py	249	0644	edit dl rm
test_repr.py	14988	0644	edit dl rm
test_subclass.py	2084	0644	edit dl rm
test_timeseries.py	4799	0644	edit dl rm
test_timezones.py	1296	0644	edit dl rm
test_ufunc.py	9785	0644	edit dl rm
test_validate.py	669	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/series/test_duplicates.py (2384B)

import numpy as np
import pytest

from pandas import Categorical, Series
import pandas._testing as tm
from pandas.core.construction import create_series_with_explicit_dtype

def test_nunique():
    # basics.rst doc example
    series = Series(np.random.randn(500))
    series[20:500] = np.nan
    series[10:20] = 5000
    result = series.nunique()
    assert result == 11

# GH 18051
    s = Series(Categorical([]))
    assert s.nunique() == 0
    s = Series(Categorical([np.nan]))
    assert s.nunique() == 0

def test_unique():
    # GH714 also, dtype=float
    s = Series([1.2345] * 100)
    s[::2] = np.nan
    result = s.unique()
    assert len(result) == 2

s = Series([1.2345] * 100, dtype="f4")
    s[::2] = np.nan
    result = s.unique()
    assert len(result) == 2

# NAs in object arrays #714
    s = Series(["foo"] * 100, dtype="O")
    s[::2] = np.nan
    result = s.unique()
    assert len(result) == 2

# decision about None
    s = Series([1, 2, 3, None, None, None], dtype=object)
    result = s.unique()
    expected = np.array([1, 2, 3, None], dtype=object)
    tm.assert_numpy_array_equal(result, expected)

# GH 18051
    s = Series(Categorical([]))
    tm.assert_categorical_equal(s.unique(), Categorical([]))
    s = Series(Categorical([np.nan]))
    tm.assert_categorical_equal(s.unique(), Categorical([np.nan]))

def test_unique_data_ownership():
    # it works! #1807
    Series(Series(["a", "c", "b"]).unique()).sort_values()

@pytest.mark.parametrize(
    "data, expected",
    [
        (np.random.randint(0, 10, size=1000), False),
        (np.arange(1000), True),
        ([], True),
        ([np.nan], True),
        (["foo", "bar", np.nan], True),
        (["foo", "foo", np.nan], False),
        (["foo", "bar", np.nan, np.nan], False),
    ],
)
def test_is_unique(data, expected):
    # GH11946 / GH25180
    s = create_series_with_explicit_dtype(data, dtype_if_empty=object)
    assert s.is_unique is expected

def test_is_unique_class_ne(capsys):
    # GH 20661
    class Foo:
        def __init__(self, val):
            self._value = val

def __ne__(self, other):
            raise Exception("NEQ not supported")

with capsys.disabled():
        li = [Foo(i) for i in range(5)]
        s = Series(li, index=list(range(5)))
    s.is_unique
    captured = capsys.readouterr()
    assert len(captured.err) == 0

import numpy as np import pytest from pandas import Categorical, Series import pandas._testing as tm from pandas.core.construction import create_series_with_explicit_dtype def test_nunique(): # basics.rst doc example series = Series(np.random.randn(500)) series[20:500] = np.nan series[10:20] = 5000 result = series.nunique() assert result == 11 # GH 18051 s = Series(Categorical([])) assert s.nunique() == 0 s = Series(Categorical([np.nan])) assert s.nunique() == 0 def test_unique(): # GH714 also, dtype=float s = Series([1.2345] * 100) s[::2] = np.nan result = s.unique() assert len(result) == 2 s = Series([1.2345] * 100, dtype="f4") s[::2] = np.nan result = s.unique() assert len(result) == 2 # NAs in object arrays #714 s = Series(["foo"] * 100, dtype="O") s[::2] = np.nan result = s.unique() assert len(result) == 2 # decision about None s = Series([1, 2, 3, None, None, None], dtype=object) result = s.unique() expected = np.array([1, 2, 3, None], dtype=object) tm.assert_numpy_array_equal(result, expected) # GH 18051 s = Series(Categorical([])) tm.assert_categorical_equal(s.unique(), Categorical([])) s = Series(Categorical([np.nan])) tm.assert_categorical_equal(s.unique(), Categorical([np.nan])) def test_unique_data_ownership(): # it works! #1807 Series(Series(["a", "c", "b"]).unique()).sort_values() @pytest.mark.parametrize( "data, expected", [ (np.random.randint(0, 10, size=1000), False), (np.arange(1000), True), ([], True), ([np.nan], True), (["foo", "bar", np.nan], True), (["foo", "foo", np.nan], False), (["foo", "bar", np.nan, np.nan], False), ], ) def test_is_unique(data, expected): # GH11946 / GH25180 s = create_series_with_explicit_dtype(data, dtype_if_empty=object) assert s.is_unique is expected def test_is_unique_class_ne(capsys): # GH 20661 class Foo: def __init__(self, val): self._value = val def __ne__(self, other): raise Exception("NEQ not supported") with capsys.disabled(): li = [Foo(i) for i in range(5)] s = Series(li, index=list(range(5))) s.is_unique captured = capsys.readouterr() assert len(captured.err) == 0