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import numpy as np
import pytest
from pandas._config import using_pyarrow_string_dtype
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
@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="decoding fails")
def test_unique_bad_unicode(index_or_series):
# regression test for #34550
uval = "\ud83d" # smiley emoji
obj = index_or_series([uval] * 2)
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
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