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131 lines
4.6 KiB
131 lines
4.6 KiB
import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_datetime64tz_dtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.api import NumericIndex
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from pandas.tests.base.common import allow_na_ops
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def test_unique(index_or_series_obj):
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obj = index_or_series_obj
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obj = np.repeat(obj, range(1, len(obj) + 1))
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result = obj.unique()
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# dict.fromkeys preserves the order
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unique_values = list(dict.fromkeys(obj.values))
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if isinstance(obj, pd.MultiIndex):
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expected = pd.MultiIndex.from_tuples(unique_values)
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expected.names = obj.names
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tm.assert_index_equal(result, expected, exact=True)
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elif isinstance(obj, pd.Index) and obj._is_backward_compat_public_numeric_index:
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expected = NumericIndex(unique_values, dtype=obj.dtype)
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tm.assert_index_equal(result, expected, exact=True)
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elif isinstance(obj, pd.Index):
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expected = pd.Index(unique_values, dtype=obj.dtype)
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if is_datetime64tz_dtype(obj.dtype):
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expected = expected.normalize()
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tm.assert_index_equal(result, expected, exact=True)
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else:
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expected = np.array(unique_values)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("null_obj", [np.nan, None])
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def test_unique_null(null_obj, index_or_series_obj):
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obj = index_or_series_obj
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if not allow_na_ops(obj):
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pytest.skip("type doesn't allow for NA operations")
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elif len(obj) < 1:
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pytest.skip("Test doesn't make sense on empty data")
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elif isinstance(obj, pd.MultiIndex):
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pytest.skip(f"MultiIndex can't hold '{null_obj}'")
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values = obj._values
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values[0:2] = null_obj
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klass = type(obj)
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repeated_values = np.repeat(values, range(1, len(values) + 1))
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obj = klass(repeated_values, dtype=obj.dtype)
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result = obj.unique()
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unique_values_raw = dict.fromkeys(obj.values)
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# because np.nan == np.nan is False, but None == None is True
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# np.nan would be duplicated, whereas None wouldn't
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unique_values_not_null = [val for val in unique_values_raw if not pd.isnull(val)]
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unique_values = [null_obj] + unique_values_not_null
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if isinstance(obj, pd.Index) and obj._is_backward_compat_public_numeric_index:
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expected = NumericIndex(unique_values, dtype=obj.dtype)
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tm.assert_index_equal(result, expected, exact=True)
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elif isinstance(obj, pd.Index):
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expected = pd.Index(unique_values, dtype=obj.dtype)
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if is_datetime64tz_dtype(obj.dtype):
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result = result.normalize()
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expected = expected.normalize()
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tm.assert_index_equal(result, expected, exact=True)
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else:
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expected = np.array(unique_values, dtype=obj.dtype)
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tm.assert_numpy_array_equal(result, expected)
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def test_nunique(index_or_series_obj):
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obj = index_or_series_obj
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obj = np.repeat(obj, range(1, len(obj) + 1))
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expected = len(obj.unique())
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assert obj.nunique(dropna=False) == expected
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@pytest.mark.parametrize("null_obj", [np.nan, None])
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def test_nunique_null(null_obj, index_or_series_obj):
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obj = index_or_series_obj
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if not allow_na_ops(obj):
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pytest.skip("type doesn't allow for NA operations")
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elif isinstance(obj, pd.MultiIndex):
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pytest.skip(f"MultiIndex can't hold '{null_obj}'")
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values = obj._values
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values[0:2] = null_obj
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klass = type(obj)
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repeated_values = np.repeat(values, range(1, len(values) + 1))
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obj = klass(repeated_values, dtype=obj.dtype)
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if isinstance(obj, pd.CategoricalIndex):
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assert obj.nunique() == len(obj.categories)
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assert obj.nunique(dropna=False) == len(obj.categories) + 1
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else:
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num_unique_values = len(obj.unique())
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assert obj.nunique() == max(0, num_unique_values - 1)
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assert obj.nunique(dropna=False) == max(0, num_unique_values)
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@pytest.mark.single_cpu
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@pytest.mark.xfail(
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reason="Flaky in the CI. Remove once CI has a single build: GH 44584", strict=False
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)
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def test_unique_bad_unicode(index_or_series):
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# regression test for #34550
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uval = "\ud83d" # smiley emoji
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obj = index_or_series([uval] * 2)
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result = obj.unique()
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if isinstance(obj, pd.Index):
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expected = pd.Index(["\ud83d"], dtype=object)
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tm.assert_index_equal(result, expected, exact=True)
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else:
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expected = np.array(["\ud83d"], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("dropna", [True, False])
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def test_nunique_dropna(dropna):
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# GH37566
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ser = pd.Series(["yes", "yes", pd.NA, np.nan, None, pd.NaT])
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res = ser.nunique(dropna)
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assert res == 1 if dropna else 5
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