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import numpy as np import pytest from pandas._config import using_pyarrow_string_dtype from pandas._libs import index as libindex from pandas._libs.arrays import NDArrayBacked import pandas as pd from pandas import ( Categorical, CategoricalDtype, ) import pandas._testing as tm from pandas.core.indexes.api import ( CategoricalIndex, Index, ) class TestCategoricalIndex: @pytest.fixture def simple_index(self) -> CategoricalIndex: return CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False) def test_can_hold_identifiers(self): idx = CategoricalIndex(list("aabbca"), categories=None, ordered=False) key = idx[0] assert idx._can_hold_identifiers_and_holds_name(key) is True def test_insert(self, simple_index): ci = simple_index categories = ci.categories # test 0th element result = ci.insert(0, "a") expected = CategoricalIndex(list("aaabbca"), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test Nth element that follows Python list behavior result = ci.insert(-1, "a") expected = CategoricalIndex(list("aabbcaa"), categories=categories) tm.assert_index_equal(result, expected, exact=True) # test empty result = CategoricalIndex([], categories=categories).insert(0, "a") expected = CategoricalIndex(["a"], categories=categories) tm.assert_index_equal(result, expected, exact=True) # invalid -> cast to object expected = ci.astype(object).insert(0, "d") result = ci.insert(0, "d").astype(object) tm.assert_index_equal(result, expected, exact=True) # GH 18295 (test missing) expected = CategoricalIndex(["a", np.nan, "a", "b", "c", "b"]) for na in (np.nan, pd.NaT, None): result = CategoricalIndex(list("aabcb")).insert(1, na) tm.assert_index_equal(result, expected) def test_insert_na_mismatched_dtype(self): ci = CategoricalIndex([0, 1, 1]) result = ci.insert(0, pd.NaT) expected = Index([pd.NaT, 0, 1, 1], dtype=object) tm.assert_index_equal(result, expected) def test_delete(self, simple_index): ci = simple_index categories = ci.categories result = ci.delete(0) expected = CategoricalIndex(list("abbca"), categories=categories) tm.assert_index_equal(result, expected, exact=True) result = ci.delete(-1) expected = CategoricalIndex(list("aabbc"), categories=categories) tm.assert_index_equal(result, expected, exact=True) with tm.external_error_raised((IndexError, ValueError)): # Either depending on NumPy version ci.delete(10) @pytest.mark.parametrize( "data, non_lexsorted_data", [[[1, 2, 3], [9, 0, 1, 2, 3]], [list("abc"), list("fabcd")]], ) def test_is_monotonic(self, data, non_lexsorted_data): c = CategoricalIndex(data) assert c.is_monotonic_increasing is True assert c.is_monotonic_decreasing is False c = CategoricalIndex(data, ordered=True) assert c.is_monotonic_increasing is True assert c.is_monotonic_decreasing is False c = CategoricalIndex(data, categories=reversed(data)) assert c.is_monotonic_increasing is False assert c.is_monotonic_decreasing is True c = CategoricalIndex(data, categories=reversed(data), ordered=True) assert c.is_monotonic_increasing is False assert c.is_monotonic_decreasing is True # test when data is neither monotonic increasing nor decreasing reordered_data = [data[0], data[2], data[1]] c = CategoricalIndex(reordered_data, categories=reversed(data)) assert c.is_monotonic_increasing is False assert c.is_monotonic_decreasing is False # non lexsorted categories categories = non_lexsorted_data c = CategoricalIndex(categories[:2], categories=categories) assert c.is_monotonic_increasing is True assert c.is_monotonic_decreasing is False c = CategoricalIndex(categories[1:3], categories=categories) assert c.is_monotonic_increasing is True assert c.is_monotonic_decreasing is False def test_has_duplicates(self): idx = CategoricalIndex([0, 0, 0], name="foo") assert idx.is_unique is False assert idx.has_duplicates is True idx = CategoricalIndex([0, 1], categories=[2, 3], name="foo") assert idx.is_unique is False assert idx.has_duplicates is True idx = CategoricalIndex([0, 1, 2, 3], categories=[1, 2, 3], name="foo") assert idx.is_unique is True assert idx.has_duplicates is False @pytest.mark.parametrize( "data, categories, expected", [ ( [1, 1, 1], [1, 2, 3], { "first": np.array([False, True, True]), "last": np.array([True, True, False]), False: np.array([True, True, True]), }, ), ( [1, 1, 1], list("abc"), { "first": np.array([False, True, True]), "last": np.array([True, True, False]), False: np.array([True, True, True]), }, ), ( [2, "a", "b"], list("abc"), { "first": np.zeros(shape=(3), dtype=np.bool_), "last": np.zeros(shape=(3), dtype=np.bool_), False: np.zeros(shape=(3), dtype=np.bool_), }, ), ( list("abb"), list("abc"), { "first": np.array([False, False, True]), "last": np.array([False, True, False]), False: np.array([False, True, True]), }, ), ], ) def test_drop_duplicates(self, data, categories, expected): idx = CategoricalIndex(data, categories=categories, name="foo") for keep, e in expected.items(): tm.assert_numpy_array_equal(idx.duplicated(keep=keep), e) e = idx[~e] result = idx.drop_duplicates(keep=keep) tm.assert_index_equal(result, e) @pytest.mark.parametrize( "data, categories, expected_data", [ ([1, 1, 1], [1, 2, 3], [1]), ([1, 1, 1], list("abc"), [np.nan]), ([1, 2, "a"], [1, 2, 3], [1, 2, np.nan]), ([2, "a", "b"], list("abc"), [np.nan, "a", "b"]), ], ) def test_unique(self, data, categories, expected_data, ordered): dtype = CategoricalDtype(categories, ordered=ordered) idx = CategoricalIndex(data, dtype=dtype) expected = CategoricalIndex(expected_data, dtype=dtype) tm.assert_index_equal(idx.unique(), expected) @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr doesn't roundtrip") def test_repr_roundtrip(self): ci = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True) str(ci) tm.assert_index_equal(eval(repr(ci)), ci, exact=True) # formatting str(ci) # long format # this is not reprable ci = CategoricalIndex(np.random.default_rng(2).integers(0, 5, size=100)) str(ci) def test_isin(self): ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"]) tm.assert_numpy_array_equal( ci.isin(["c"]), np.array([False, False, False, True, False, False]) ) tm.assert_numpy_array_equal( ci.isin(["c", "a", "b"]), np.array([True] * 5 + [False]) ) tm.assert_numpy_array_equal( ci.isin(["c", "a", "b", np.nan]), np.array([True] * 6) ) # mismatched categorical -> coerced to ndarray so doesn't matter result = ci.isin(ci.set_categories(list("abcdefghi"))) expected = np.array([True] * 6) tm.assert_numpy_array_equal(result, expected) result = ci.isin(ci.set_categories(list("defghi"))) expected = np.array([False] * 5 + [True]) tm.assert_numpy_array_equal(result, expected) def test_isin_overlapping_intervals(self): # GH 34974 idx = pd.IntervalIndex([pd.Interval(0, 2), pd.Interval(0, 1)]) result = CategoricalIndex(idx).isin(idx) expected = np.array([True, True]) tm.assert_numpy_array_equal(result, expected) def test_identical(self): ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True) ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True) assert ci1.identical(ci1) assert ci1.identical(ci1.copy()) assert not ci1.identical(ci2) def test_ensure_copied_data(self): # gh-12309: Check the "copy" argument of each # Index.__new__ is honored. # # Must be tested separately from other indexes because # self.values is not an ndarray. index = CategoricalIndex(list("ab") * 5) result = CategoricalIndex(index.values, copy=True) tm.assert_index_equal(index, result) assert not np.shares_memory(result._data._codes, index._data._codes) result = CategoricalIndex(index.values, copy=False) assert result._data._codes is index._data._codes class TestCategoricalIndex2: def test_view_i8(self): # GH#25464 ci = CategoricalIndex(list("ab") * 50) msg = "When changing to a larger dtype, its size must be a divisor" with pytest.raises(ValueError, match=msg): ci.view("i8") with pytest.raises(ValueError, match=msg): ci._data.view("i8") ci = ci[:-4] # length divisible by 8 res = ci.view("i8") expected = ci._data.codes.view("i8") tm.assert_numpy_array_equal(res, expected) cat = ci._data tm.assert_numpy_array_equal(cat.view("i8"), expected) @pytest.mark.parametrize( "dtype, engine_type", [ (np.int8, libindex.Int8Engine), (np.int16, libindex.Int16Engine), (np.int32, libindex.Int32Engine), (np.int64, libindex.Int64Engine), ], ) def test_engine_type(self, dtype, engine_type): if dtype != np.int64: # num. of uniques required to push CategoricalIndex.codes to a # dtype (128 categories required for .codes dtype to be int16 etc.) num_uniques = {np.int8: 1, np.int16: 128, np.int32: 32768}[dtype] ci = CategoricalIndex(range(num_uniques)) else: # having 232 - 231 categories would be very memory-intensive, # so we cheat a bit with the dtype ci = CategoricalIndex(range(32768)) # == 216 - 2(16 - 1) arr = ci.values._ndarray.astype("int64") NDArrayBacked.__init__(ci._data, arr, ci.dtype) assert np.issubdtype(ci.codes.dtype, dtype) assert isinstance(ci._engine, engine_type) @pytest.mark.parametrize( "func,op_name", [ (lambda idx: idx - idx, "__sub__"), (lambda idx: idx + idx, "__add__"), (lambda idx: idx - ["a", "b"], "__sub__"), (lambda idx: idx + ["a", "b"], "__add__"), (lambda idx: ["a", "b"] - idx, "__rsub__"), (lambda idx: ["a", "b"] + idx, "__radd__"), ], ) def test_disallow_addsub_ops(self, func, op_name): # GH 10039 # set ops (+/-) raise TypeError idx = Index(Categorical(["a", "b"])) cat_or_list = "'(Categorical\|list)' and '(Categorical\|list)'" msg = "\|".join( [ f"cannot perform {op_name} with this index type: CategoricalIndex", "can only concatenate list", rf"unsupported operand type\(s\) for [\+-]: {cat_or_list}", ] ) with pytest.raises(TypeError, match=msg): func(idx) def test_method_delegation(self): ci = CategoricalIndex(list("aabbca"), categories=list("cabdef")) result = ci.set_categories(list("cab")) tm.assert_index_equal( result, CategoricalIndex(list("aabbca"), categories=list("cab")) ) ci = CategoricalIndex(list("aabbca"), categories=list("cab")) result = ci.rename_categories(list("efg")) tm.assert_index_equal( result, CategoricalIndex(list("ffggef"), categories=list("efg")) ) # GH18862 (let rename_categories take callables) result = ci.rename_categories(lambda x: x.upper()) tm.assert_index_equal( result, CategoricalIndex(list("AABBCA"), categories=list("CAB")) ) ci = CategoricalIndex(list("aabbca"), categories=list("cab")) result = ci.add_categories(["d"]) tm.assert_index_equal( result, CategoricalIndex(list("aabbca"), categories=list("cabd")) ) ci = CategoricalIndex(list("aabbca"), categories=list("cab")) result = ci.remove_categories(["c"]) tm.assert_index_equal( result, CategoricalIndex(list("aabb") + [np.nan] + ["a"], categories=list("ab")), ) ci = CategoricalIndex(list("aabbca"), categories=list("cabdef")) result = ci.as_unordered() tm.assert_index_equal(result, ci) ci = CategoricalIndex(list("aabbca"), categories=list("cabdef")) result = ci.as_ordered() tm.assert_index_equal( result, CategoricalIndex(list("aabbca"), categories=list("cabdef"), ordered=True), ) # invalid msg = "cannot use inplace with CategoricalIndex" with pytest.raises(ValueError, match=msg): ci.set_categories(list("cab"), inplace=True) def test_remove_maintains_order(self): ci = CategoricalIndex(list("abcdda"), categories=list("abcd")) result = ci.reorder_categories(["d", "c", "b", "a"], ordered=True) tm.assert_index_equal( result, CategoricalIndex(list("abcdda"), categories=list("dcba"), ordered=True), ) result = result.remove_categories(["c"]) tm.assert_index_equal( result, CategoricalIndex( ["a", "b", np.nan, "d", "d", "a"], categories=list("dba"), ordered=True ), )

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