test_category.py

# -*- coding: utf-8 -*-

import pytest

import pandas.util.testing as tm
from pandas.core.indexes.api import Index, CategoricalIndex
from pandas.core.dtypes.dtypes import CategoricalDtype
from .common import Base

from pandas.compat import range, PY3

import numpy as np

from pandas import Categorical, IntervalIndex, compat
from pandas.util.testing import assert_almost_equal
import pandas.core.config as cf
import pandas as pd

if PY3:
    unicode = lambda x: x


class TestCategoricalIndex(Base):
    _holder = CategoricalIndex

    def setup_method(self, method):
        self.indices = dict(catIndex=tm.makeCategoricalIndex(100))
        self.setup_indices()

    def create_index(self, categories=None, ordered=False):
        if categories is None:
            categories = list('cab')
        return CategoricalIndex(
            list('aabbca'), categories=categories, ordered=ordered)

    def test_can_hold_identifiers(self):
        idx = self.create_index(categories=list('abcd'))
        key = idx[0]
        assert idx._can_hold_identifiers_and_holds_name(key) is True

    def test_construction(self):

        ci = self.create_index(categories=list('abcd'))
        categories = ci.categories

        result = Index(ci)
        tm.assert_index_equal(result, ci, exact=True)
        assert not result.ordered

        result = Index(ci.values)
        tm.assert_index_equal(result, ci, exact=True)
        assert not result.ordered

        # empty
        result = CategoricalIndex(categories=categories)
        tm.assert_index_equal(result.categories, Index(categories))
        tm.assert_numpy_array_equal(result.codes, np.array([], dtype='int8'))
        assert not result.ordered

        # passing categories
        result = CategoricalIndex(list('aabbca'), categories=categories)
        tm.assert_index_equal(result.categories, Index(categories))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, 2, 0], dtype='int8'))

        c = pd.Categorical(list('aabbca'))
        result = CategoricalIndex(c)
        tm.assert_index_equal(result.categories, Index(list('abc')))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, 2, 0], dtype='int8'))
        assert not result.ordered

        result = CategoricalIndex(c, categories=categories)
        tm.assert_index_equal(result.categories, Index(categories))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, 2, 0], dtype='int8'))
        assert not result.ordered

        ci = CategoricalIndex(c, categories=list('abcd'))
        result = CategoricalIndex(ci)
        tm.assert_index_equal(result.categories, Index(categories))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, 2, 0], dtype='int8'))
        assert not result.ordered

        result = CategoricalIndex(ci, categories=list('ab'))
        tm.assert_index_equal(result.categories, Index(list('ab')))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, -1, 0], dtype='int8'))
        assert not result.ordered

        result = CategoricalIndex(ci, categories=list('ab'), ordered=True)
        tm.assert_index_equal(result.categories, Index(list('ab')))
        tm.assert_numpy_array_equal(result.codes,
                                    np.array([0, 0, 1,
                                              1, -1, 0], dtype='int8'))
        assert result.ordered

        result = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True)
        expected = pd.CategoricalIndex(ci, categories=list('ab'), ordered=True,
                                       dtype='category')
        tm.assert_index_equal(result, expected, exact=True)

        # turn me to an Index
        result = Index(np.array(ci))
        assert isinstance(result, Index)
        assert not isinstance(result, CategoricalIndex)

    def test_construction_with_dtype(self):

        # specify dtype
        ci = self.create_index(categories=list('abc'))

        result = Index(np.array(ci), dtype='category')
        tm.assert_index_equal(result, ci, exact=True)

        result = Index(np.array(ci).tolist(), dtype='category')
        tm.assert_index_equal(result, ci, exact=True)

        # these are generally only equal when the categories are reordered
        ci = self.create_index()

        result = Index(
            np.array(ci), dtype='category').reorder_categories(ci.categories)
        tm.assert_index_equal(result, ci, exact=True)

        # make sure indexes are handled
        expected = CategoricalIndex([0, 1, 2], categories=[0, 1, 2],
                                    ordered=True)
        idx = Index(range(3))
        result = CategoricalIndex(idx, categories=idx, ordered=True)
        tm.assert_index_equal(result, expected, exact=True)

    def test_construction_with_categorical_dtype(self):
        # construction with CategoricalDtype
        # GH18109
        data, cats, ordered = 'a a b b'.split(), 'c b a'.split(), True
        dtype = CategoricalDtype(categories=cats, ordered=ordered)

        result = CategoricalIndex(data, dtype=dtype)
        expected = CategoricalIndex(data, categories=cats, ordered=ordered)
        tm.assert_index_equal(result, expected, exact=True)

        # GH 19032
        result = Index(data, dtype=dtype)
        tm.assert_index_equal(result, expected, exact=True)

        # error when combining categories/ordered and dtype kwargs
        msg = 'Cannot specify both `dtype` and `categories` or `ordered`.'
        with pytest.raises(ValueError, match=msg):
            CategoricalIndex(data, categories=cats, dtype=dtype)

        with pytest.raises(ValueError, match=msg):
            Index(data, categories=cats, dtype=dtype)

        with pytest.raises(ValueError, match=msg):
            CategoricalIndex(data, ordered=ordered, dtype=dtype)

        with pytest.raises(ValueError, match=msg):
            Index(data, ordered=ordered, dtype=dtype)

    def test_create_categorical(self):
        # https://github.com/pandas-dev/pandas/pull/17513
        # The public CI constructor doesn't hit this code path with
        # instances of CategoricalIndex, but we still want to test the code
        ci = CategoricalIndex(['a', 'b', 'c'])
        # First ci is self, second ci is data.
        result = CategoricalIndex._create_categorical(ci, ci)
        expected = Categorical(['a', 'b', 'c'])
        tm.assert_categorical_equal(result, expected)

    def test_disallow_set_ops(self):

        # GH 10039
        # set ops (+/-) raise TypeError
        idx = pd.Index(pd.Categorical(['a', 'b']))

        pytest.raises(TypeError, lambda: idx - idx)
        pytest.raises(TypeError, lambda: idx + idx)
        pytest.raises(TypeError, lambda: idx - ['a', 'b'])
        pytest.raises(TypeError, lambda: idx + ['a', 'b'])
        pytest.raises(TypeError, lambda: ['a', 'b'] - idx)
        pytest.raises(TypeError, lambda: ['a', 'b'] + 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
        pytest.raises(ValueError, lambda: ci.set_categories(
            list('cab'), inplace=True))

    def test_contains(self):

        ci = self.create_index(categories=list('cabdef'))

        assert 'a' in ci
        assert 'z' not in ci
        assert 'e' not in ci
        assert np.nan not in ci

        # assert codes NOT in index
        assert 0 not in ci
        assert 1 not in ci

        ci = CategoricalIndex(
            list('aabbca') + [np.nan], categories=list('cabdef'))
        assert np.nan in ci

    def test_min_max(self):

        ci = self.create_index(ordered=False)
        pytest.raises(TypeError, lambda: ci.min())
        pytest.raises(TypeError, lambda: ci.max())

        ci = self.create_index(ordered=True)

        assert ci.min() == 'c'
        assert ci.max() == 'b'

    def test_map(self):
        ci = pd.CategoricalIndex(list('ABABC'), categories=list('CBA'),
                                 ordered=True)
        result = ci.map(lambda x: x.lower())
        exp = pd.CategoricalIndex(list('ababc'), categories=list('cba'),
                                  ordered=True)
        tm.assert_index_equal(result, exp)

        ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'),
                                 ordered=False, name='XXX')
        result = ci.map(lambda x: x.lower())
        exp = pd.CategoricalIndex(list('ababc'), categories=list('bac'),
                                  ordered=False, name='XXX')
        tm.assert_index_equal(result, exp)

        # GH 12766: Return an index not an array
        tm.assert_index_equal(ci.map(lambda x: 1),
                              Index(np.array([1] * 5, dtype=np.int64),
                                    name='XXX'))

        # change categories dtype
        ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'),
                                 ordered=False)

        def f(x):
            return {'A': 10, 'B': 20, 'C': 30}.get(x)

        result = ci.map(f)
        exp = pd.CategoricalIndex([10, 20, 10, 20, 30],
                                  categories=[20, 10, 30],
                                  ordered=False)
        tm.assert_index_equal(result, exp)

        result = ci.map(pd.Series([10, 20, 30], index=['A', 'B', 'C']))
        tm.assert_index_equal(result, exp)

        result = ci.map({'A': 10, 'B': 20, 'C': 30})
        tm.assert_index_equal(result, exp)

    def test_map_with_categorical_series(self):
        # GH 12756
        a = pd.Index([1, 2, 3, 4])
        b = pd.Series(["even", "odd", "even", "odd"],
                      dtype="category")
        c = pd.Series(["even", "odd", "even", "odd"])

        exp = CategoricalIndex(["odd", "even", "odd", np.nan])
        tm.assert_index_equal(a.map(b), exp)
        exp = pd.Index(["odd", "even", "odd", np.nan])
        tm.assert_index_equal(a.map(c), exp)

    @pytest.mark.parametrize('klass', [list, tuple, np.array, pd.Series])
    def test_where(self, klass):
        i = self.create_index()
        cond = [True] * len(i)
        expected = i
        result = i.where(klass(cond))
        tm.assert_index_equal(result, expected)

        cond = [False] + [True] * (len(i) - 1)
        expected = CategoricalIndex([np.nan] + i[1:].tolist(),
                                    categories=i.categories)
        result = i.where(klass(cond))
        tm.assert_index_equal(result, expected)

    def test_append(self):

        ci = self.create_index()
        categories = ci.categories

        # append cats with the same categories
        result = ci[:3].append(ci[3:])
        tm.assert_index_equal(result, ci, exact=True)

        foos = [ci[:1], ci[1:3], ci[3:]]
        result = foos[0].append(foos[1:])
        tm.assert_index_equal(result, ci, exact=True)

        # empty
        result = ci.append([])
        tm.assert_index_equal(result, ci, exact=True)

        # appending with different categories or reoreded is not ok
        pytest.raises(
            TypeError,
            lambda: ci.append(ci.values.set_categories(list('abcd'))))
        pytest.raises(
            TypeError,
            lambda: ci.append(ci.values.reorder_categories(list('abc'))))

        # with objects
        result = ci.append(Index(['c', 'a']))
        expected = CategoricalIndex(list('aabbcaca'), categories=categories)
        tm.assert_index_equal(result, expected, exact=True)

        # invalid objects
        pytest.raises(TypeError, lambda: ci.append(Index(['a', 'd'])))

        # GH14298 - if base object is not categorical -> coerce to object
        result = Index(['c', 'a']).append(ci)
        expected = Index(list('caaabbca'))
        tm.assert_index_equal(result, expected, exact=True)

    def test_append_to_another(self):
        # hits _concat_index_asobject
        fst = Index(['a', 'b'])
        snd = CategoricalIndex(['d', 'e'])
        result = fst.append(snd)
        expected = Index(['a', 'b', 'd', 'e'])
        tm.assert_index_equal(result, expected)

    def test_insert(self):

        ci = self.create_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
        pytest.raises(TypeError, lambda: ci.insert(0, 'd'))

        # 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_delete(self):

        ci = self.create_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 pytest.raises((IndexError, ValueError)):
            # Either depending on NumPy version
            ci.delete(10)

    def test_astype(self):

        ci = self.create_index()
        result = ci.astype(object)
        tm.assert_index_equal(result, Index(np.array(ci)))

        # this IS equal, but not the same class
        assert result.equals(ci)
        assert isinstance(result, Index)
        assert not isinstance(result, CategoricalIndex)

        # interval
        ii = IntervalIndex.from_arrays(left=[-0.001, 2.0],
                                       right=[2, 4],
                                       closed='right')

        ci = CategoricalIndex(Categorical.from_codes(
            [0, 1, -1], categories=ii, ordered=True))

        result = ci.astype('interval')
        expected = ii.take([0, 1, -1])
        tm.assert_index_equal(result, expected)

        result = IntervalIndex(result.values)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize('name', [None, 'foo'])
    @pytest.mark.parametrize('dtype_ordered', [True, False])
    @pytest.mark.parametrize('index_ordered', [True, False])
    def test_astype_category(self, name, dtype_ordered, index_ordered):
        # GH 18630
        index = self.create_index(ordered=index_ordered)
        if name:
            index = index.rename(name)

        # standard categories
        dtype = CategoricalDtype(ordered=dtype_ordered)
        result = index.astype(dtype)
        expected = CategoricalIndex(index.tolist(),
                                    name=name,
                                    categories=index.categories,
                                    ordered=dtype_ordered)
        tm.assert_index_equal(result, expected)

        # non-standard categories
        dtype = CategoricalDtype(index.unique().tolist()[:-1], dtype_ordered)
        result = index.astype(dtype)
        expected = CategoricalIndex(index.tolist(), name=name, dtype=dtype)
        tm.assert_index_equal(result, expected)

        if dtype_ordered is False:
            # dtype='category' can't specify ordered, so only test once
            result = index.astype('category')
            expected = index
            tm.assert_index_equal(result, expected)

    def test_reindex_base(self):
        # Determined by cat ordering.
        idx = CategoricalIndex(list("cab"), categories=list("cab"))
        expected = np.arange(len(idx), dtype=np.intp)

        actual = idx.get_indexer(idx)
        tm.assert_numpy_array_equal(expected, actual)

        with tm.assert_raises_regex(ValueError, "Invalid fill method"):
            idx.get_indexer(idx, method="invalid")

    def test_reindexing(self):
        np.random.seed(123456789)

        ci = self.create_index()
        oidx = Index(np.array(ci))

        for n in [1, 2, 5, len(ci)]:
            finder = oidx[np.random.randint(0, len(ci), size=n)]
            expected = oidx.get_indexer_non_unique(finder)[0]

            actual = ci.get_indexer(finder)
            tm.assert_numpy_array_equal(expected, actual)

        # see gh-17323
        #
        # Even when indexer is equal to the
        # members in the index, we should
        # respect duplicates instead of taking
        # the fast-track path.
        for finder in [list("aabbca"), list("aababca")]:
            expected = oidx.get_indexer_non_unique(finder)[0]

            actual = ci.get_indexer(finder)
            tm.assert_numpy_array_equal(expected, actual)

    def test_reindex_dtype(self):
        c = CategoricalIndex(['a', 'b', 'c', 'a'])
        res, indexer = c.reindex(['a', 'c'])
        tm.assert_index_equal(res, Index(['a', 'a', 'c']), exact=True)
        tm.assert_numpy_array_equal(indexer,
                                    np.array([0, 3, 2], dtype=np.intp))

        c = CategoricalIndex(['a', 'b', 'c', 'a'])
        res, indexer = c.reindex(Categorical(['a', 'c']))

        exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c'])
        tm.assert_index_equal(res, exp, exact=True)
        tm.assert_numpy_array_equal(indexer,
                                    np.array([0, 3, 2], dtype=np.intp))

        c = CategoricalIndex(['a', 'b', 'c', 'a'],
                             categories=['a', 'b', 'c', 'd'])
        res, indexer = c.reindex(['a', 'c'])
        exp = Index(['a', 'a', 'c'], dtype='object')
        tm.assert_index_equal(res, exp, exact=True)
        tm.assert_numpy_array_equal(indexer,
                                    np.array([0, 3, 2], dtype=np.intp))

        c = CategoricalIndex(['a', 'b', 'c', 'a'],
                             categories=['a', 'b', 'c', 'd'])
        res, indexer = c.reindex(Categorical(['a', 'c']))
        exp = CategoricalIndex(['a', 'a', 'c'], categories=['a', 'c'])
        tm.assert_index_equal(res, exp, exact=True)
        tm.assert_numpy_array_equal(indexer,
                                    np.array([0, 3, 2], dtype=np.intp))

    def test_reindex_empty_index(self):
        # See GH16770
        c = CategoricalIndex([])
        res, indexer = c.reindex(['a', 'b'])
        tm.assert_index_equal(res, Index(['a', 'b']), exact=True)
        tm.assert_numpy_array_equal(indexer,
                                    np.array([-1, -1], dtype=np.intp))

    @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
        assert not c.is_monotonic_decreasing

        c = CategoricalIndex(data, ordered=True)
        assert c.is_monotonic_increasing
        assert not c.is_monotonic_decreasing

        c = CategoricalIndex(data, categories=reversed(data))
        assert not c.is_monotonic_increasing
        assert c.is_monotonic_decreasing

        c = CategoricalIndex(data, categories=reversed(data), ordered=True)
        assert not c.is_monotonic_increasing
        assert c.is_monotonic_decreasing

        # 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 not c.is_monotonic_increasing
        assert not c.is_monotonic_decreasing

        # non lexsorted categories
        categories = non_lexsorted_data

        c = CategoricalIndex(categories[:2], categories=categories)
        assert c.is_monotonic_increasing
        assert not c.is_monotonic_decreasing

        c = CategoricalIndex(categories[1:3], categories=categories)
        assert c.is_monotonic_increasing
        assert not c.is_monotonic_decreasing

    @pytest.mark.parametrize('values, expected', [
        ([1, 2, 3], True),
        ([1, 3, 1], False),
        (list('abc'), True),
        (list('aba'), False)])
    def test_is_unique(self, values, expected):
        ci = CategoricalIndex(values)
        assert ci.is_unique is expected

    def test_duplicates(self):

        idx = CategoricalIndex([0, 0, 0], name='foo')
        assert not idx.is_unique
        assert idx.has_duplicates

        expected = CategoricalIndex([0], name='foo')
        tm.assert_index_equal(idx.drop_duplicates(), expected)
        tm.assert_index_equal(idx.unique(), expected)

    def test_get_indexer(self):

        idx1 = CategoricalIndex(list('aabcde'), categories=list('edabc'))
        idx2 = CategoricalIndex(list('abf'))

        for indexer in [idx2, list('abf'), Index(list('abf'))]:
            r1 = idx1.get_indexer(idx2)
            assert_almost_equal(r1, np.array([0, 1, 2, -1], dtype=np.intp))

        pytest.raises(NotImplementedError,
                      lambda: idx2.get_indexer(idx1, method='pad'))
        pytest.raises(NotImplementedError,
                      lambda: idx2.get_indexer(idx1, method='backfill'))
        pytest.raises(NotImplementedError,
                      lambda: idx2.get_indexer(idx1, method='nearest'))

    def test_get_loc(self):
        # GH 12531
        cidx1 = CategoricalIndex(list('abcde'), categories=list('edabc'))
        idx1 = Index(list('abcde'))
        assert cidx1.get_loc('a') == idx1.get_loc('a')
        assert cidx1.get_loc('e') == idx1.get_loc('e')

        for i in [cidx1, idx1]:
            with pytest.raises(KeyError):
                i.get_loc('NOT-EXIST')

        # non-unique
        cidx2 = CategoricalIndex(list('aacded'), categories=list('edabc'))
        idx2 = Index(list('aacded'))

        # results in bool array
        res = cidx2.get_loc('d')
        tm.assert_numpy_array_equal(res, idx2.get_loc('d'))
        tm.assert_numpy_array_equal(res, np.array([False, False, False,
                                                   True, False, True]))
        # unique element results in scalar
        res = cidx2.get_loc('e')
        assert res == idx2.get_loc('e')
        assert res == 4

        for i in [cidx2, idx2]:
            with pytest.raises(KeyError):
                i.get_loc('NOT-EXIST')

        # non-unique, slicable
        cidx3 = CategoricalIndex(list('aabbb'), categories=list('abc'))
        idx3 = Index(list('aabbb'))

        # results in slice
        res = cidx3.get_loc('a')
        assert res == idx3.get_loc('a')
        assert res == slice(0, 2, None)

        res = cidx3.get_loc('b')
        assert res == idx3.get_loc('b')
        assert res == slice(2, 5, None)

        for i in [cidx3, idx3]:
            with pytest.raises(KeyError):
                i.get_loc('c')

    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
        if PY3:
            str(ci)
        else:
            compat.text_type(ci)

        # long format
        # this is not reprable
        ci = CategoricalIndex(np.random.randint(0, 5, size=100))
        if PY3:
            str(ci)
        else:
            compat.text_type(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_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.value is not an ndarray.
        _base = lambda ar: ar if ar.base is None else ar.base

        for index in self.indices.values():
            result = CategoricalIndex(index.values, copy=True)
            tm.assert_index_equal(index, result)
            assert _base(index.values) is not _base(result.values)

            result = CategoricalIndex(index.values, copy=False)
            assert _base(index.values) is _base(result.values)

    def test_equals_categorical(self):
        ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True)
        ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'],
                               ordered=True)

        assert ci1.equals(ci1)
        assert not ci1.equals(ci2)
        assert ci1.equals(ci1.astype(object))
        assert ci1.astype(object).equals(ci1)

        assert (ci1 == ci1).all()
        assert not (ci1 != ci1).all()
        assert not (ci1 > ci1).all()
        assert not (ci1 < ci1).all()
        assert (ci1 <= ci1).all()
        assert (ci1 >= ci1).all()

        assert not (ci1 == 1).all()
        assert (ci1 == Index(['a', 'b'])).all()
        assert (ci1 == ci1.values).all()

        # invalid comparisons
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            ci1 == Index(['a', 'b', 'c'])
        pytest.raises(TypeError, lambda: ci1 == ci2)
        pytest.raises(
            TypeError, lambda: ci1 == Categorical(ci1.values, ordered=False))
        pytest.raises(
            TypeError,
            lambda: ci1 == Categorical(ci1.values, categories=list('abc')))

        # tests
        # make sure that we are testing for category inclusion properly
        ci = CategoricalIndex(list('aabca'), categories=['c', 'a', 'b'])
        assert not ci.equals(list('aabca'))
        # Same categories, but different order
        # Unordered
        assert ci.equals(CategoricalIndex(list('aabca')))
        # Ordered
        assert not ci.equals(CategoricalIndex(list('aabca'), ordered=True))
        assert ci.equals(ci.copy())

        ci = CategoricalIndex(list('aabca') + [np.nan],
                              categories=['c', 'a', 'b'])
        assert not ci.equals(list('aabca'))
        assert not ci.equals(CategoricalIndex(list('aabca')))
        assert ci.equals(ci.copy())

        ci = CategoricalIndex(list('aabca') + [np.nan],
                              categories=['c', 'a', 'b'])
        assert not ci.equals(list('aabca') + [np.nan])
        assert ci.equals(CategoricalIndex(list('aabca') + [np.nan]))
        assert not ci.equals(CategoricalIndex(list('aabca') + [np.nan],
                                              ordered=True))
        assert ci.equals(ci.copy())

    def test_equals_categoridcal_unordered(self):
        # https://github.com/pandas-dev/pandas/issues/16603
        a = pd.CategoricalIndex(['A'], categories=['A', 'B'])
        b = pd.CategoricalIndex(['A'], categories=['B', 'A'])
        c = pd.CategoricalIndex(['C'], categories=['B', 'A'])
        assert a.equals(b)
        assert not a.equals(c)
        assert not b.equals(c)

    def test_string_categorical_index_repr(self):
        # short
        idx = pd.CategoricalIndex(['a', 'bb', 'ccc'])
        if PY3:
            expected = u"""CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa
            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'a', u'bb', u'ccc'], categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')"""  # noqa
            assert unicode(idx) == expected

        # multiple lines
        idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 10)
        if PY3:
            expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
                  'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb',
                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',
                  u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',
                  u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc',
                  u'a', u'bb', u'ccc', u'a', u'bb', u'ccc'],
                 categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category')"""  # noqa

            assert unicode(idx) == expected

        # truncated
        idx = pd.CategoricalIndex(['a', 'bb', 'ccc'] * 100)
        if PY3:
            expected = u"""CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
                  ...
                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a', u'bb',
                  u'ccc', u'a',
                  ...
                  u'ccc', u'a', u'bb', u'ccc', u'a', u'bb', u'ccc', u'a',
                  u'bb', u'ccc'],
                 categories=[u'a', u'bb', u'ccc'], ordered=False, dtype='category', length=300)"""  # noqa

            assert unicode(idx) == expected

        # larger categories
        idx = pd.CategoricalIndex(list('abcdefghijklmmo'))
        if PY3:
            expected = u"""CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
                  'm', 'm', 'o'],
                 categories=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', ...], ordered=False, dtype='category')"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', u'i', u'j',
                  u'k', u'l', u'm', u'm', u'o'],
                 categories=[u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', ...], ordered=False, dtype='category')"""  # noqa

            assert unicode(idx) == expected

        # short
        idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'])
        if PY3:
            expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa
            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')"""  # noqa
            assert unicode(idx) == expected

        # multiple lines
        idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10)
        if PY3:
            expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
                  'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい',
                  u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう',
                  u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう'],
                 categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')"""  # noqa

            assert unicode(idx) == expected

        # truncated
        idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100)
        if PY3:
            expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
                  ...
                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい',
                  u'ううう', u'あ',
                  ...
                  u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう'],
                 categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)"""  # noqa

            assert unicode(idx) == expected

        # larger categories
        idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ'))
        if PY3:
            expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し',
                  'す', 'せ', 'そ'],
                 categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')"""  # noqa

            assert repr(idx) == expected
        else:
            expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', u'け', u'こ',
                  u'さ', u'し', u'す', u'せ', u'そ'],
                 categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')"""  # noqa

            assert unicode(idx) == expected

        # Emable Unicode option -----------------------------------------
        with cf.option_context('display.unicode.east_asian_width', True):

            # short
            idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'])
            if PY3:
                expected = u"""CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa
                assert repr(idx) == expected
            else:
                expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう'], categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')"""  # noqa
                assert unicode(idx) == expected

            # multiple lines
            idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 10)
            if PY3:
                expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
                  'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa

                assert repr(idx) == expected
            else:
                expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ', u'いい', u'ううう'],
                 categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category')"""  # noqa

                assert unicode(idx) == expected

            # truncated
            idx = pd.CategoricalIndex([u'あ', u'いい', u'ううう'] * 100)
            if PY3:
                expected = u"""CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                  'ううう', 'あ',
                  ...
                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
                  'あ', 'いい', 'ううう'],
                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa

                assert repr(idx) == expected
            else:
                expected = u"""CategoricalIndex([u'あ', u'いい', u'ううう', u'あ', u'いい', u'ううう', u'あ',
                  u'いい', u'ううう', u'あ',
                  ...
                  u'ううう', u'あ', u'いい', u'ううう', u'あ', u'いい',
                  u'ううう', u'あ', u'いい', u'ううう'],
                 categories=[u'あ', u'いい', u'ううう'], ordered=False, dtype='category', length=300)"""  # noqa

                assert unicode(idx) == expected

            # larger categories
            idx = pd.CategoricalIndex(list(u'あいうえおかきくけこさしすせそ'))
            if PY3:
                expected = u"""CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ',
                  'さ', 'し', 'す', 'せ', 'そ'],
                 categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')"""  # noqa

                assert repr(idx) == expected
            else:
                expected = u"""CategoricalIndex([u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く',
                  u'け', u'こ', u'さ', u'し', u'す', u'せ', u'そ'],
                 categories=[u'あ', u'い', u'う', u'え', u'お', u'か', u'き', u'く', ...], ordered=False, dtype='category')"""  # noqa

                assert unicode(idx) == expected

    def test_fillna_categorical(self):
        # GH 11343
        idx = CategoricalIndex([1.0, np.nan, 3.0, 1.0], name='x')
        # fill by value in categories
        exp = CategoricalIndex([1.0, 1.0, 3.0, 1.0], name='x')
        tm.assert_index_equal(idx.fillna(1.0), exp)

        # fill by value not in categories raises ValueError
        with tm.assert_raises_regex(ValueError,
                                    'fill value must be in categories'):
            idx.fillna(2.0)

    def test_take_fill_value(self):
        # GH 12631

        # numeric category
        idx = pd.CategoricalIndex([1, 2, 3], name='xxx')
        result = idx.take(np.array([1, 0, -1]))
        expected = pd.CategoricalIndex([2, 1, 3], name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        expected = pd.CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3],
                                       name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        expected = pd.CategoricalIndex([2, 1, 3], name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        # object category
        idx = pd.CategoricalIndex(list('CBA'), categories=list('ABC'),
                                  ordered=True, name='xxx')
        result = idx.take(np.array([1, 0, -1]))
        expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'),
                                       ordered=True, name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        expected = pd.CategoricalIndex(['B', 'C', np.nan],
                                       categories=list('ABC'), ordered=True,
                                       name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'),
                                       ordered=True, name='xxx')
        tm.assert_index_equal(result, expected)
        tm.assert_categorical_equal(result.values, expected.values)

        msg = ('When allow_fill=True and fill_value is not None, '
               'all indices must be >= -1')
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -2]), fill_value=True)
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -5]), fill_value=True)

        with pytest.raises(IndexError):
            idx.take(np.array([1, -5]))

    def test_take_fill_value_datetime(self):

        # datetime category
        idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'],
                               name='xxx')
        idx = pd.CategoricalIndex(idx)
        result = idx.take(np.array([1, 0, -1]))
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx')
        expected = pd.CategoricalIndex(expected)
        tm.assert_index_equal(result, expected)

        # fill_value
        result = idx.take(np.array([1, 0, -1]), fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'],
                                    name='xxx')
        exp_cats = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'])
        expected = pd.CategoricalIndex(expected, categories=exp_cats)
        tm.assert_index_equal(result, expected)

        # allow_fill=False
        result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                          fill_value=True)
        expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                    name='xxx')
        expected = pd.CategoricalIndex(expected)
        tm.assert_index_equal(result, expected)

        msg = ('When allow_fill=True and fill_value is not None, '
               'all indices must be >= -1')
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -2]), fill_value=True)
        with tm.assert_raises_regex(ValueError, msg):
            idx.take(np.array([1, 0, -5]), fill_value=True)

        with pytest.raises(IndexError):
            idx.take(np.array([1, -5]))

    def test_take_invalid_kwargs(self):
        idx = pd.CategoricalIndex([1, 2, 3], name='foo')
        indices = [1, 0, -1]

        msg = r"take\(\) got an unexpected keyword argument 'foo'"
        tm.assert_raises_regex(TypeError, msg, idx.take,
                               indices, foo=2)

        msg = "the 'out' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, idx.take,
                               indices, out=indices)

        msg = "the 'mode' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, idx.take,
                               indices, mode='clip')