mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-24 22:44:56 +01:00
93 lines
2.3 KiB
Python
93 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
|