Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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123 lines
4.1 KiB
123 lines
4.1 KiB
import numpy as np
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import pytest
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from pandas._libs import iNaT
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from pandas.core.dtypes.common import is_datetime64tz_dtype, needs_i8_conversion
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import pandas as pd
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import pandas._testing as tm
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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)
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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)
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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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if needs_i8_conversion(obj.dtype):
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values[0:2] = iNaT
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else:
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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):
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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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elif isinstance(obj, pd.CategoricalIndex):
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expected = expected.set_categories(unique_values_not_null)
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tm.assert_index_equal(result, expected)
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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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if needs_i8_conversion(obj.dtype):
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values[0:2] = iNaT
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else:
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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.parametrize(
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"idx_or_series_w_bad_unicode", [pd.Index(["\ud83d"] * 2), pd.Series(["\ud83d"] * 2)]
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)
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def test_unique_bad_unicode(idx_or_series_w_bad_unicode):
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# regression test for #34550
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obj = idx_or_series_w_bad_unicode
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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)
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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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