You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
from pandas.tests.base.common import allow_na_ops
|
|
|
|
|
|
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
|
|
def test_unique(index_or_series_obj):
|
|
obj = index_or_series_obj
|
|
obj = np.repeat(obj, range(1, len(obj) + 1))
|
|
result = obj.unique()
|
|
|
|
# dict.fromkeys preserves the order
|
|
unique_values = list(dict.fromkeys(obj.values))
|
|
if isinstance(obj, pd.MultiIndex):
|
|
expected = pd.MultiIndex.from_tuples(unique_values)
|
|
expected.names = obj.names
|
|
tm.assert_index_equal(result, expected, exact=True)
|
|
elif isinstance(obj, pd.Index):
|
|
expected = pd.Index(unique_values, dtype=obj.dtype)
|
|
if isinstance(obj.dtype, pd.DatetimeTZDtype):
|
|
expected = expected.normalize()
|
|
tm.assert_index_equal(result, expected, exact=True)
|
|
else:
|
|
expected = np.array(unique_values)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
|
|
@pytest.mark.parametrize("null_obj", [np.nan, None])
|
|
def test_unique_null(null_obj, index_or_series_obj):
|
|
obj = index_or_series_obj
|
|
|
|
if not allow_na_ops(obj):
|
|
pytest.skip("type doesn't allow for NA operations")
|
|
elif len(obj) < 1:
|
|
pytest.skip("Test doesn't make sense on empty data")
|
|
elif isinstance(obj, pd.MultiIndex):
|
|
pytest.skip(f"MultiIndex can't hold '{null_obj}'")
|
|
|
|
values = obj._values
|
|
values[0:2] = null_obj
|
|
|
|
klass = type(obj)
|
|
repeated_values = np.repeat(values, range(1, len(values) + 1))
|
|
obj = klass(repeated_values, dtype=obj.dtype)
|
|
result = obj.unique()
|
|
|
|
unique_values_raw = dict.fromkeys(obj.values)
|
|
# because np.nan == np.nan is False, but None == None is True
|
|
# np.nan would be duplicated, whereas None wouldn't
|
|
unique_values_not_null = [val for val in unique_values_raw if not pd.isnull(val)]
|
|
unique_values = [null_obj] + unique_values_not_null
|
|
|
|
if isinstance(obj, pd.Index):
|
|
expected = pd.Index(unique_values, dtype=obj.dtype)
|
|
if isinstance(obj.dtype, pd.DatetimeTZDtype):
|
|
result = result.normalize()
|
|
expected = expected.normalize()
|
|
tm.assert_index_equal(result, expected, exact=True)
|
|
else:
|
|
expected = np.array(unique_values, dtype=obj.dtype)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
def test_nunique(index_or_series_obj):
|
|
obj = index_or_series_obj
|
|
obj = np.repeat(obj, range(1, len(obj) + 1))
|
|
expected = len(obj.unique())
|
|
assert obj.nunique(dropna=False) == expected
|
|
|
|
|
|
@pytest.mark.parametrize("null_obj", [np.nan, None])
|
|
def test_nunique_null(null_obj, index_or_series_obj):
|
|
obj = index_or_series_obj
|
|
|
|
if not allow_na_ops(obj):
|
|
pytest.skip("type doesn't allow for NA operations")
|
|
elif isinstance(obj, pd.MultiIndex):
|
|
pytest.skip(f"MultiIndex can't hold '{null_obj}'")
|
|
|
|
values = obj._values
|
|
values[0:2] = null_obj
|
|
|
|
klass = type(obj)
|
|
repeated_values = np.repeat(values, range(1, len(values) + 1))
|
|
obj = klass(repeated_values, dtype=obj.dtype)
|
|
|
|
if isinstance(obj, pd.CategoricalIndex):
|
|
assert obj.nunique() == len(obj.categories)
|
|
assert obj.nunique(dropna=False) == len(obj.categories) + 1
|
|
else:
|
|
num_unique_values = len(obj.unique())
|
|
assert obj.nunique() == max(0, num_unique_values - 1)
|
|
assert obj.nunique(dropna=False) == max(0, num_unique_values)
|
|
|
|
|
|
@pytest.mark.single_cpu
|
|
def test_unique_bad_unicode(index_or_series):
|
|
# regression test for #34550
|
|
uval = "\ud83d" # smiley emoji
|
|
|
|
obj = index_or_series([uval] * 2, dtype=object)
|
|
result = obj.unique()
|
|
|
|
if isinstance(obj, pd.Index):
|
|
expected = pd.Index(["\ud83d"], dtype=object)
|
|
tm.assert_index_equal(result, expected, exact=True)
|
|
else:
|
|
expected = np.array(["\ud83d"], dtype=object)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("dropna", [True, False])
|
|
def test_nunique_dropna(dropna):
|
|
# GH37566
|
|
ser = pd.Series(["yes", "yes", pd.NA, np.nan, None, pd.NaT])
|
|
res = ser.nunique(dropna)
|
|
assert res == 1 if dropna else 5
|