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

import pytest

from pandas import compat
from pandas.compat import PY3

import numpy as np

from pandas import (Series, Index, Float64Index, Int64Index, UInt64Index,
                    RangeIndex, MultiIndex, CategoricalIndex, DatetimeIndex,
                    TimedeltaIndex, PeriodIndex, IntervalIndex, isna)
from pandas.core.indexes.base import InvalidIndexError
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
from pandas.core.dtypes.common import needs_i8_conversion
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas._libs.tslib import iNaT

import pandas.util.testing as tm

import pandas as pd


class Base(object):
    """ base class for index sub-class tests """
    _holder = None
    _compat_props = ['shape', 'ndim', 'size', 'nbytes']

    def setup_indices(self):
        for name, idx in self.indices.items():
            setattr(self, name, idx)

    def verify_pickle(self, indices):
        unpickled = tm.round_trip_pickle(indices)
        assert indices.equals(unpickled)

    def test_pickle_compat_construction(self):
        # this is testing for pickle compat
        if self._holder is None:
            return

        # need an object to create with
        pytest.raises(TypeError, self._holder)

    def test_to_series(self):
        # assert that we are creating a copy of the index

        idx = self.create_index()
        s = idx.to_series()
        assert s.values is not idx.values
        assert s.index is not idx
        assert s.name == idx.name

    def test_to_series_with_arguments(self):
        # GH18699

        # index kwarg
        idx = self.create_index()
        s = idx.to_series(index=idx)

        assert s.values is not idx.values
        assert s.index is idx
        assert s.name == idx.name

        # name kwarg
        idx = self.create_index()
        s = idx.to_series(name='__test')

        assert s.values is not idx.values
        assert s.index is not idx
        assert s.name != idx.name

    def test_to_frame(self):
        # see gh-15230
        idx = self.create_index()
        name = idx.name or 0

        df = idx.to_frame()

        assert df.index is idx
        assert len(df.columns) == 1
        assert df.columns[0] == name
        assert df[name].values is not idx.values

        df = idx.to_frame(index=False)
        assert df.index is not idx

    def test_shift(self):

        # GH8083 test the base class for shift
        idx = self.create_index()
        pytest.raises(NotImplementedError, idx.shift, 1)
        pytest.raises(NotImplementedError, idx.shift, 1, 2)

    def test_create_index_existing_name(self):

        # GH11193, when an existing index is passed, and a new name is not
        # specified, the new index should inherit the previous object name
        expected = self.create_index()
        if not isinstance(expected, MultiIndex):
            expected.name = 'foo'
            result = pd.Index(expected)
            tm.assert_index_equal(result, expected)

            result = pd.Index(expected, name='bar')
            expected.name = 'bar'
            tm.assert_index_equal(result, expected)
        else:
            expected.names = ['foo', 'bar']
            result = pd.Index(expected)
            tm.assert_index_equal(
                result, Index(Index([('foo', 'one'), ('foo', 'two'),
                                     ('bar', 'one'), ('baz', 'two'),
                                     ('qux', 'one'), ('qux', 'two')],
                                    dtype='object'),
                              names=['foo', 'bar']))

            result = pd.Index(expected, names=['A', 'B'])
            tm.assert_index_equal(
                result,
                Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
                             ('baz', 'two'), ('qux', 'one'), ('qux', 'two')],
                            dtype='object'), names=['A', 'B']))

    def test_numeric_compat(self):

        idx = self.create_index()
        tm.assert_raises_regex(TypeError, "cannot perform __mul__",
                               lambda: idx * 1)
        tm.assert_raises_regex(TypeError, "cannot perform __rmul__",
                               lambda: 1 * idx)

        div_err = "cannot perform __truediv__" if PY3 \
            else "cannot perform __div__"
        tm.assert_raises_regex(TypeError, div_err, lambda: idx / 1)
        div_err = div_err.replace(' __', ' __r')
        tm.assert_raises_regex(TypeError, div_err, lambda: 1 / idx)
        tm.assert_raises_regex(TypeError, "cannot perform __floordiv__",
                               lambda: idx // 1)
        tm.assert_raises_regex(TypeError, "cannot perform __rfloordiv__",
                               lambda: 1 // idx)

    def test_logical_compat(self):
        idx = self.create_index()
        tm.assert_raises_regex(TypeError, 'cannot perform all',
                               lambda: idx.all())
        tm.assert_raises_regex(TypeError, 'cannot perform any',
                               lambda: idx.any())

    def test_boolean_context_compat(self):

        # boolean context compat
        idx = self.create_index()

        def f():
            if idx:
                pass

        tm.assert_raises_regex(ValueError, 'The truth value of a', f)

    def test_reindex_base(self):
        idx = self.create_index()
        expected = np.arange(idx.size, 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_get_indexer_consistency(self):
        # See GH 16819
        for name, index in self.indices.items():
            if isinstance(index, IntervalIndex):
                continue

            if index.is_unique or isinstance(index, CategoricalIndex):
                indexer = index.get_indexer(index[0:2])
                assert isinstance(indexer, np.ndarray)
                assert indexer.dtype == np.intp
            else:
                e = "Reindexing only valid with uniquely valued Index objects"
                with tm.assert_raises_regex(InvalidIndexError, e):
                    indexer = index.get_indexer(index[0:2])

            indexer, _ = index.get_indexer_non_unique(index[0:2])
            assert isinstance(indexer, np.ndarray)
            assert indexer.dtype == np.intp

    def test_ndarray_compat_properties(self):
        idx = self.create_index()
        assert idx.T.equals(idx)
        assert idx.transpose().equals(idx)

        values = idx.values
        for prop in self._compat_props:
            assert getattr(idx, prop) == getattr(values, prop)

        # test for validity
        idx.nbytes
        idx.values.nbytes

    def test_repr_roundtrip(self):

        idx = self.create_index()
        tm.assert_index_equal(eval(repr(idx)), idx)

    def test_str(self):

        # test the string repr
        idx = self.create_index()
        idx.name = 'foo'
        assert "'foo'" in str(idx)
        assert idx.__class__.__name__ in str(idx)

    def test_dtype_str(self, indices):
        dtype = indices.dtype_str
        assert isinstance(dtype, compat.string_types)
        assert dtype == str(indices.dtype)

    def test_repr_max_seq_item_setting(self):
        # GH10182
        idx = self.create_index()
        idx = idx.repeat(50)
        with pd.option_context("display.max_seq_items", None):
            repr(idx)
            assert '...' not in str(idx)

    def test_wrong_number_names(self, indices):
        def testit(ind):
            ind.names = ["apple", "banana", "carrot"]
        tm.assert_raises_regex(ValueError, "^Length", testit, indices)

    def test_set_name_methods(self, indices):
        new_name = "This is the new name for this index"

        # don't tests a MultiIndex here (as its tested separated)
        if isinstance(indices, MultiIndex):
            return
        original_name = indices.name
        new_ind = indices.set_names([new_name])
        assert new_ind.name == new_name
        assert indices.name == original_name
        res = indices.rename(new_name, inplace=True)

        # should return None
        assert res is None
        assert indices.name == new_name
        assert indices.names == [new_name]
        # with tm.assert_raises_regex(TypeError, "list-like"):
        #    # should still fail even if it would be the right length
        #    ind.set_names("a")
        with tm.assert_raises_regex(ValueError, "Level must be None"):
            indices.set_names("a", level=0)

        # rename in place just leaves tuples and other containers alone
        name = ('A', 'B')
        indices.rename(name, inplace=True)
        assert indices.name == name
        assert indices.names == [name]

    def test_hash_error(self, indices):
        index = indices
        tm.assert_raises_regex(TypeError, "unhashable type: %r" %
                               type(index).__name__, hash, indices)

    def test_copy_name(self):
        # gh-12309: Check that the "name" argument
        # passed at initialization is honored.

        for name, index in compat.iteritems(self.indices):
            if isinstance(index, MultiIndex):
                continue

            first = index.__class__(index, copy=True, name='mario')
            second = first.__class__(first, copy=False)

            # Even though "copy=False", we want a new object.
            assert first is not second

            # Not using tm.assert_index_equal() since names differ.
            assert index.equals(first)

            assert first.name == 'mario'
            assert second.name == 'mario'

            s1 = Series(2, index=first)
            s2 = Series(3, index=second[:-1])

            if not isinstance(index, CategoricalIndex):
                # See gh-13365
                s3 = s1 * s2
                assert s3.index.name == 'mario'

    def test_ensure_copied_data(self):
        # Check the "copy" argument of each Index.__new__ is honoured
        # GH12309
        for name, index in compat.iteritems(self.indices):
            init_kwargs = {}
            if isinstance(index, PeriodIndex):
                # Needs "freq" specification:
                init_kwargs['freq'] = index.freq
            elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
                # RangeIndex cannot be initialized from data
                # MultiIndex and CategoricalIndex are tested separately
                continue

            index_type = index.__class__
            result = index_type(index.values, copy=True, **init_kwargs)
            tm.assert_index_equal(index, result)
            tm.assert_numpy_array_equal(index.values, result.values,
                                        check_same='copy')

            if isinstance(index, PeriodIndex):
                # .values an object array of Period, thus copied
                result = index_type(ordinal=index.asi8, copy=False,
                                    **init_kwargs)
                tm.assert_numpy_array_equal(index._ndarray_values,
                                            result._ndarray_values,
                                            check_same='same')
            elif isinstance(index, IntervalIndex):
                # checked in test_interval.py
                pass
            else:
                result = index_type(index.values, copy=False, **init_kwargs)
                tm.assert_numpy_array_equal(index.values, result.values,
                                            check_same='same')
                tm.assert_numpy_array_equal(index._ndarray_values,
                                            result._ndarray_values,
                                            check_same='same')

    def test_copy_and_deepcopy(self, indices):
        from copy import copy, deepcopy

        if isinstance(indices, MultiIndex):
            return
        for func in (copy, deepcopy):
            idx_copy = func(indices)
            assert idx_copy is not indices
            assert idx_copy.equals(indices)

        new_copy = indices.copy(deep=True, name="banana")
        assert new_copy.name == "banana"

    def test_duplicates(self, indices):
        if type(indices) is not self._holder:
            return
        if not len(indices) or isinstance(indices, MultiIndex):
            return
        idx = self._holder([indices[0]] * 5)
        assert not idx.is_unique
        assert idx.has_duplicates

    def test_unique(self, indices):
        # don't test a MultiIndex here (as its tested separated)
        # don't test a CategoricalIndex because categories change (GH 18291)
        if isinstance(indices, (MultiIndex, CategoricalIndex)):
            return

        # GH 17896
        expected = indices.drop_duplicates()
        for level in 0, indices.name, None:
            result = indices.unique(level=level)
            tm.assert_index_equal(result, expected)

        for level in 3, 'wrong':
            pytest.raises((IndexError, KeyError), indices.unique, level=level)

    def test_unique_na(self):
        idx = pd.Index([2, np.nan, 2, 1], name='my_index')
        expected = pd.Index([2, np.nan, 1], name='my_index')
        result = idx.unique()
        tm.assert_index_equal(result, expected)

    def test_get_unique_index(self, indices):
        # MultiIndex tested separately
        if not len(indices) or isinstance(indices, MultiIndex):
            return

        idx = indices[[0] * 5]
        idx_unique = indices[[0]]

        # We test against `idx_unique`, so first we make sure it's unique
        # and doesn't contain nans.
        assert idx_unique.is_unique
        try:
            assert not idx_unique.hasnans
        except NotImplementedError:
            pass

        for dropna in [False, True]:
            result = idx._get_unique_index(dropna=dropna)
            tm.assert_index_equal(result, idx_unique)

        # nans:
        if not indices._can_hold_na:
            return

        if needs_i8_conversion(indices):
            vals = indices.asi8[[0] * 5]
            vals[0] = iNaT
        else:
            vals = indices.values[[0] * 5]
            vals[0] = np.nan

        vals_unique = vals[:2]
        idx_nan = indices._shallow_copy(vals)
        idx_unique_nan = indices._shallow_copy(vals_unique)
        assert idx_unique_nan.is_unique

        assert idx_nan.dtype == indices.dtype
        assert idx_unique_nan.dtype == indices.dtype

        for dropna, expected in zip([False, True],
                                    [idx_unique_nan,
                                     idx_unique]):
            for i in [idx_nan, idx_unique_nan]:
                result = i._get_unique_index(dropna=dropna)
                tm.assert_index_equal(result, expected)

    def test_sort(self, indices):
        pytest.raises(TypeError, indices.sort)

    def test_mutability(self, indices):
        if not len(indices):
            return
        pytest.raises(TypeError, indices.__setitem__, 0, indices[0])

    def test_view(self, indices):
        assert indices.view().name == indices.name

    def test_compat(self, indices):
        assert indices.tolist() == list(indices)

    def test_memory_usage(self):
        for name, index in compat.iteritems(self.indices):
            result = index.memory_usage()
            if len(index):
                index.get_loc(index[0])
                result2 = index.memory_usage()
                result3 = index.memory_usage(deep=True)

                # RangeIndex, IntervalIndex
                # don't have engines
                if not isinstance(index, (RangeIndex, IntervalIndex)):
                    assert result2 > result

                if index.inferred_type == 'object':
                    assert result3 > result2

            else:

                # we report 0 for no-length
                assert result == 0

    def test_argsort(self):
        for k, ind in self.indices.items():

            # separately tested
            if k in ['catIndex']:
                continue

            result = ind.argsort()
            expected = np.array(ind).argsort()
            tm.assert_numpy_array_equal(result, expected, check_dtype=False)

    def test_numpy_argsort(self):
        for k, ind in self.indices.items():
            result = np.argsort(ind)
            expected = ind.argsort()
            tm.assert_numpy_array_equal(result, expected)

            # these are the only two types that perform
            # pandas compatibility input validation - the
            # rest already perform separate (or no) such
            # validation via their 'values' attribute as
            # defined in pandas.core.indexes/base.py - they
            # cannot be changed at the moment due to
            # backwards compatibility concerns
            if isinstance(type(ind), (CategoricalIndex, RangeIndex)):
                msg = "the 'axis' parameter is not supported"
                tm.assert_raises_regex(ValueError, msg,
                                       np.argsort, ind, axis=1)

                msg = "the 'kind' parameter is not supported"
                tm.assert_raises_regex(ValueError, msg, np.argsort,
                                       ind, kind='mergesort')

                msg = "the 'order' parameter is not supported"
                tm.assert_raises_regex(ValueError, msg, np.argsort,
                                       ind, order=('a', 'b'))

    def test_pickle(self, indices):
        self.verify_pickle(indices)
        original_name, indices.name = indices.name, 'foo'
        self.verify_pickle(indices)
        indices.name = original_name

    def test_take(self):
        indexer = [4, 3, 0, 2]
        for k, ind in self.indices.items():

            # separate
            if k in ['boolIndex', 'tuples', 'empty']:
                continue

            result = ind.take(indexer)
            expected = ind[indexer]
            assert result.equals(expected)

            if not isinstance(ind,
                              (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
                # GH 10791
                with pytest.raises(AttributeError):
                    ind.freq

    def test_take_invalid_kwargs(self):
        idx = self.create_index()
        indices = [1, 2]

        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')

    def test_repeat(self):
        rep = 2
        i = self.create_index()
        expected = pd.Index(i.values.repeat(rep), name=i.name)
        tm.assert_index_equal(i.repeat(rep), expected)

        i = self.create_index()
        rep = np.arange(len(i))
        expected = pd.Index(i.values.repeat(rep), name=i.name)
        tm.assert_index_equal(i.repeat(rep), expected)

    def test_numpy_repeat(self):
        rep = 2
        i = self.create_index()
        expected = i.repeat(rep)
        tm.assert_index_equal(np.repeat(i, rep), expected)

        msg = "the 'axis' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, np.repeat,
                               i, rep, axis=0)

    @pytest.mark.parametrize('klass', [list, tuple, np.array, Series])
    def test_where(self, klass):
        i = self.create_index()

        cond = [True] * len(i)
        result = i.where(klass(cond))
        expected = i
        tm.assert_index_equal(result, expected)

        cond = [False] + [True] * len(i[1:])
        expected = pd.Index([i._na_value] + i[1:].tolist(), dtype=i.dtype)
        result = i.where(klass(cond))
        tm.assert_index_equal(result, expected)

    def test_setops_errorcases(self):
        for name, idx in compat.iteritems(self.indices):
            # # non-iterable input
            cases = [0.5, 'xxx']
            methods = [idx.intersection, idx.union, idx.difference,
                       idx.symmetric_difference]

            for method in methods:
                for case in cases:
                    tm.assert_raises_regex(TypeError,
                                           "Input must be Index "
                                           "or array-like",
                                           method, case)

    def test_intersection_base(self):
        for name, idx in compat.iteritems(self.indices):
            first = idx[:5]
            second = idx[:3]
            intersect = first.intersection(second)

            if isinstance(idx, CategoricalIndex):
                pass
            else:
                assert tm.equalContents(intersect, second)

            # GH 10149
            cases = [klass(second.values)
                     for klass in [np.array, Series, list]]
            for case in cases:
                if isinstance(idx, PeriodIndex):
                    msg = "can only call with other PeriodIndex-ed objects"
                    with tm.assert_raises_regex(ValueError, msg):
                        result = first.intersection(case)
                elif isinstance(idx, CategoricalIndex):
                    pass
                else:
                    result = first.intersection(case)
                    assert tm.equalContents(result, second)

            if isinstance(idx, MultiIndex):
                msg = "other must be a MultiIndex or a list of tuples"
                with tm.assert_raises_regex(TypeError, msg):
                    result = first.intersection([1, 2, 3])

    def test_union_base(self):
        for name, idx in compat.iteritems(self.indices):
            first = idx[3:]
            second = idx[:5]
            everything = idx
            union = first.union(second)
            assert tm.equalContents(union, everything)

            # GH 10149
            cases = [klass(second.values)
                     for klass in [np.array, Series, list]]
            for case in cases:
                if isinstance(idx, PeriodIndex):
                    msg = "can only call with other PeriodIndex-ed objects"
                    with tm.assert_raises_regex(ValueError, msg):
                        result = first.union(case)
                elif isinstance(idx, CategoricalIndex):
                    pass
                else:
                    result = first.union(case)
                    assert tm.equalContents(result, everything)

            if isinstance(idx, MultiIndex):
                msg = "other must be a MultiIndex or a list of tuples"
                with tm.assert_raises_regex(TypeError, msg):
                    result = first.union([1, 2, 3])

    def test_difference_base(self):
        for name, idx in compat.iteritems(self.indices):
            first = idx[2:]
            second = idx[:4]
            answer = idx[4:]
            result = first.difference(second)

            if isinstance(idx, CategoricalIndex):
                pass
            else:
                assert tm.equalContents(result, answer)

            # GH 10149
            cases = [klass(second.values)
                     for klass in [np.array, Series, list]]
            for case in cases:
                if isinstance(idx, PeriodIndex):
                    msg = "can only call with other PeriodIndex-ed objects"
                    with tm.assert_raises_regex(ValueError, msg):
                        result = first.difference(case)
                elif isinstance(idx, CategoricalIndex):
                    pass
                elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
                    assert result.__class__ == answer.__class__
                    tm.assert_numpy_array_equal(result.sort_values().asi8,
                                                answer.sort_values().asi8)
                else:
                    result = first.difference(case)
                    assert tm.equalContents(result, answer)

            if isinstance(idx, MultiIndex):
                msg = "other must be a MultiIndex or a list of tuples"
                with tm.assert_raises_regex(TypeError, msg):
                    result = first.difference([1, 2, 3])

    def test_symmetric_difference(self):
        for name, idx in compat.iteritems(self.indices):
            first = idx[1:]
            second = idx[:-1]
            if isinstance(idx, CategoricalIndex):
                pass
            else:
                answer = idx[[0, -1]]
                result = first.symmetric_difference(second)
                assert tm.equalContents(result, answer)

            # GH 10149
            cases = [klass(second.values)
                     for klass in [np.array, Series, list]]
            for case in cases:
                if isinstance(idx, PeriodIndex):
                    msg = "can only call with other PeriodIndex-ed objects"
                    with tm.assert_raises_regex(ValueError, msg):
                        result = first.symmetric_difference(case)
                elif isinstance(idx, CategoricalIndex):
                    pass
                else:
                    result = first.symmetric_difference(case)
                    assert tm.equalContents(result, answer)

            if isinstance(idx, MultiIndex):
                msg = "other must be a MultiIndex or a list of tuples"
                with tm.assert_raises_regex(TypeError, msg):
                    first.symmetric_difference([1, 2, 3])

    def test_insert_base(self):

        for name, idx in compat.iteritems(self.indices):
            result = idx[1:4]

            if not len(idx):
                continue

            # test 0th element
            assert idx[0:4].equals(result.insert(0, idx[0]))

    def test_delete_base(self):

        for name, idx in compat.iteritems(self.indices):

            if not len(idx):
                continue

            if isinstance(idx, RangeIndex):
                # tested in class
                continue

            expected = idx[1:]
            result = idx.delete(0)
            assert result.equals(expected)
            assert result.name == expected.name

            expected = idx[:-1]
            result = idx.delete(-1)
            assert result.equals(expected)
            assert result.name == expected.name

            with pytest.raises((IndexError, ValueError)):
                # either depending on numpy version
                result = idx.delete(len(idx))

    def test_equals(self):

        for name, idx in compat.iteritems(self.indices):
            assert idx.equals(idx)
            assert idx.equals(idx.copy())
            assert idx.equals(idx.astype(object))

            assert not idx.equals(list(idx))
            assert not idx.equals(np.array(idx))

            # Cannot pass in non-int64 dtype to RangeIndex
            if not isinstance(idx, RangeIndex):
                same_values = Index(idx, dtype=object)
                assert idx.equals(same_values)
                assert same_values.equals(idx)

            if idx.nlevels == 1:
                # do not test MultiIndex
                assert not idx.equals(pd.Series(idx))

    def test_equals_op(self):
        # GH9947, GH10637
        index_a = self.create_index()
        if isinstance(index_a, PeriodIndex):
            return

        n = len(index_a)
        index_b = index_a[0:-1]
        index_c = index_a[0:-1].append(index_a[-2:-1])
        index_d = index_a[0:1]
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == index_b
        expected1 = np.array([True] * n)
        expected2 = np.array([True] * (n - 1) + [False])
        tm.assert_numpy_array_equal(index_a == index_a, expected1)
        tm.assert_numpy_array_equal(index_a == index_c, expected2)

        # test comparisons with numpy arrays
        array_a = np.array(index_a)
        array_b = np.array(index_a[0:-1])
        array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
        array_d = np.array(index_a[0:1])
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == array_b
        tm.assert_numpy_array_equal(index_a == array_a, expected1)
        tm.assert_numpy_array_equal(index_a == array_c, expected2)

        # test comparisons with Series
        series_a = Series(array_a)
        series_b = Series(array_b)
        series_c = Series(array_c)
        series_d = Series(array_d)
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == series_b

        tm.assert_numpy_array_equal(index_a == series_a, expected1)
        tm.assert_numpy_array_equal(index_a == series_c, expected2)

        # cases where length is 1 for one of them
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == index_d
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == series_d
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            index_a == array_d
        msg = "Can only compare identically-labeled Series objects"
        with tm.assert_raises_regex(ValueError, msg):
            series_a == series_d
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            series_a == array_d

        # comparing with a scalar should broadcast; note that we are excluding
        # MultiIndex because in this case each item in the index is a tuple of
        # length 2, and therefore is considered an array of length 2 in the
        # comparison instead of a scalar
        if not isinstance(index_a, MultiIndex):
            expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
            # assuming the 2nd to last item is unique in the data
            item = index_a[-2]
            tm.assert_numpy_array_equal(index_a == item, expected3)
            tm.assert_series_equal(series_a == item, Series(expected3))

    def test_numpy_ufuncs(self):
        # test ufuncs of numpy 1.9.2. see:
        # http://docs.scipy.org/doc/numpy/reference/ufuncs.html

        # some functions are skipped because it may return different result
        # for unicode input depending on numpy version

        for name, idx in compat.iteritems(self.indices):
            for func in [np.exp, np.exp2, np.expm1, np.log, np.log2, np.log10,
                         np.log1p, np.sqrt, np.sin, np.cos, np.tan, np.arcsin,
                         np.arccos, np.arctan, np.sinh, np.cosh, np.tanh,
                         np.arcsinh, np.arccosh, np.arctanh, np.deg2rad,
                         np.rad2deg]:
                if isinstance(idx, DatetimeIndexOpsMixin):
                    # raise TypeError or ValueError (PeriodIndex)
                    # PeriodIndex behavior should be changed in future version
                    with pytest.raises(Exception):
                        with np.errstate(all='ignore'):
                            func(idx)
                elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
                    # coerces to float (e.g. np.sin)
                    with np.errstate(all='ignore'):
                        result = func(idx)
                        exp = Index(func(idx.values), name=idx.name)

                    tm.assert_index_equal(result, exp)
                    assert isinstance(result, pd.Float64Index)
                else:
                    # raise AttributeError or TypeError
                    if len(idx) == 0:
                        continue
                    else:
                        with pytest.raises(Exception):
                            with np.errstate(all='ignore'):
                                func(idx)

            for func in [np.isfinite, np.isinf, np.isnan, np.signbit]:
                if isinstance(idx, DatetimeIndexOpsMixin):
                    # raise TypeError or ValueError (PeriodIndex)
                    with pytest.raises(Exception):
                        func(idx)
                elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
                    # Results in bool array
                    result = func(idx)
                    assert isinstance(result, np.ndarray)
                    assert not isinstance(result, Index)
                else:
                    if len(idx) == 0:
                        continue
                    else:
                        with pytest.raises(Exception):
                            func(idx)

    def test_hasnans_isnans(self):
        # GH 11343, added tests for hasnans / isnans
        for name, index in self.indices.items():
            if isinstance(index, MultiIndex):
                pass
            else:
                idx = index.copy()

                # cases in indices doesn't include NaN
                expected = np.array([False] * len(idx), dtype=bool)
                tm.assert_numpy_array_equal(idx._isnan, expected)
                assert not idx.hasnans

                idx = index.copy()
                values = idx.values

                if len(index) == 0:
                    continue
                elif isinstance(index, DatetimeIndexOpsMixin):
                    values[1] = iNaT
                elif isinstance(index, (Int64Index, UInt64Index)):
                    continue
                else:
                    values[1] = np.nan

                if isinstance(index, PeriodIndex):
                    idx = index.__class__(values, freq=index.freq)
                else:
                    idx = index.__class__(values)

                expected = np.array([False] * len(idx), dtype=bool)
                expected[1] = True
                tm.assert_numpy_array_equal(idx._isnan, expected)
                assert idx.hasnans

    def test_fillna(self):
        # GH 11343
        for name, index in self.indices.items():
            if len(index) == 0:
                pass
            elif isinstance(index, MultiIndex):
                idx = index.copy()
                msg = "isna is not defined for MultiIndex"
                with tm.assert_raises_regex(NotImplementedError, msg):
                    idx.fillna(idx[0])
            else:
                idx = index.copy()
                result = idx.fillna(idx[0])
                tm.assert_index_equal(result, idx)
                assert result is not idx

                msg = "'value' must be a scalar, passed: "
                with tm.assert_raises_regex(TypeError, msg):
                    idx.fillna([idx[0]])

                idx = index.copy()
                values = idx.values

                if isinstance(index, DatetimeIndexOpsMixin):
                    values[1] = iNaT
                elif isinstance(index, (Int64Index, UInt64Index)):
                    continue
                else:
                    values[1] = np.nan

                if isinstance(index, PeriodIndex):
                    idx = index.__class__(values, freq=index.freq)
                else:
                    idx = index.__class__(values)

                expected = np.array([False] * len(idx), dtype=bool)
                expected[1] = True
                tm.assert_numpy_array_equal(idx._isnan, expected)
                assert idx.hasnans

    def test_nulls(self):
        # this is really a smoke test for the methods
        # as these are adequately tested for function elsewhere

        for name, index in self.indices.items():
            if len(index) == 0:
                tm.assert_numpy_array_equal(
                    index.isna(), np.array([], dtype=bool))
            elif isinstance(index, MultiIndex):
                idx = index.copy()
                msg = "isna is not defined for MultiIndex"
                with tm.assert_raises_regex(NotImplementedError, msg):
                    idx.isna()
            else:

                if not index.hasnans:
                    tm.assert_numpy_array_equal(
                        index.isna(), np.zeros(len(index), dtype=bool))
                    tm.assert_numpy_array_equal(
                        index.notna(), np.ones(len(index), dtype=bool))
                else:
                    result = isna(index)
                    tm.assert_numpy_array_equal(index.isna(), result)
                    tm.assert_numpy_array_equal(index.notna(), ~result)

    def test_empty(self):
        # GH 15270
        index = self.create_index()
        assert not index.empty
        assert index[:0].empty

    def test_join_self_unique(self, join_type):
        index = self.create_index()
        if index.is_unique:
            joined = index.join(index, how=join_type)
            assert (index == joined).all()

    def test_searchsorted_monotonic(self, indices):
        # GH17271
        # not implemented for tuple searches in MultiIndex
        # or Intervals searches in IntervalIndex
        if isinstance(indices, (MultiIndex, IntervalIndex)):
            return

        # nothing to test if the index is empty
        if indices.empty:
            return
        value = indices[0]

        # determine the expected results (handle dupes for 'right')
        expected_left, expected_right = 0, (indices == value).argmin()
        if expected_right == 0:
            # all values are the same, expected_right should be length
            expected_right = len(indices)

        # test _searchsorted_monotonic in all cases
        # test searchsorted only for increasing
        if indices.is_monotonic_increasing:
            ssm_left = indices._searchsorted_monotonic(value, side='left')
            assert expected_left == ssm_left

            ssm_right = indices._searchsorted_monotonic(value, side='right')
            assert expected_right == ssm_right

            ss_left = indices.searchsorted(value, side='left')
            assert expected_left == ss_left

            ss_right = indices.searchsorted(value, side='right')
            assert expected_right == ss_right

        elif indices.is_monotonic_decreasing:
            ssm_left = indices._searchsorted_monotonic(value, side='left')
            assert expected_left == ssm_left

            ssm_right = indices._searchsorted_monotonic(value, side='right')
            assert expected_right == ssm_right

        else:
            # non-monotonic should raise.
            with pytest.raises(ValueError):
                indices._searchsorted_monotonic(value, side='left')

    def test_map(self):
        # callable
        index = self.create_index()

        # we don't infer UInt64
        if isinstance(index, pd.UInt64Index):
            expected = index.astype('int64')
        else:
            expected = index

        result = index.map(lambda x: x)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "mapper",
        [
            lambda values, index: {i: e for e, i in zip(values, index)},
            lambda values, index: pd.Series(values, index)])
    def test_map_dictlike(self, mapper):

        index = self.create_index()
        if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
            pytest.skip("skipping tests for {}".format(type(index)))

        identity = mapper(index.values, index)

        # we don't infer to UInt64 for a dict
        if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
            expected = index.astype('int64')
        else:
            expected = index

        result = index.map(identity)
        tm.assert_index_equal(result, expected)

        # empty mappable
        expected = pd.Index([np.nan] * len(index))
        result = index.map(mapper(expected, index))
        tm.assert_index_equal(result, expected)

    def test_putmask_with_wrong_mask(self):
        # GH18368
        index = self.create_index()

        with pytest.raises(ValueError):
            index.putmask(np.ones(len(index) + 1, np.bool), 1)

        with pytest.raises(ValueError):
            index.putmask(np.ones(len(index) - 1, np.bool), 1)

        with pytest.raises(ValueError):
            index.putmask('foo', 1)

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

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

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

        if ordered is False:
            # dtype='category' defaults to ordered=False, so only test once
            result = index.astype('category', copy=copy)
            expected = CategoricalIndex(index.values, name=name)
            tm.assert_index_equal(result, expected)