craftbeerpi4-pione/venv/lib/python3.8/site-packages/pandas/tests/series/test_duplicates.py
2021-01-30 22:29:33 +01:00

92 lines
2.3 KiB
Python

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