import numpy as np
import pytest

import pandas._libs.index as _index
from pandas.errors import PerformanceWarning

import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
from pandas.util import testing as tm


class TestMultiIndexBasic(object):

    def test_multiindex_perf_warn(self):

        df = DataFrame({'jim': [0, 0, 1, 1],
                        'joe': ['x', 'x', 'z', 'y'],
                        'jolie': np.random.rand(4)}).set_index(['jim', 'joe'])

        with tm.assert_produces_warning(PerformanceWarning,
                                        clear=[pd.core.index]):
            df.loc[(1, 'z')]

        df = df.iloc[[2, 1, 3, 0]]
        with tm.assert_produces_warning(PerformanceWarning):
            df.loc[(0, )]

    def test_multiindex_contains_dropped(self):
        # GH 19027
        # test that dropped MultiIndex levels are not in the MultiIndex
        # despite continuing to be in the MultiIndex's levels
        idx = MultiIndex.from_product([[1, 2], [3, 4]])
        assert 2 in idx
        idx = idx.drop(2)

        # drop implementation keeps 2 in the levels
        assert 2 in idx.levels[0]
        # but it should no longer be in the index itself
        assert 2 not in idx

        # also applies to strings
        idx = MultiIndex.from_product([['a', 'b'], ['c', 'd']])
        assert 'a' in idx
        idx = idx.drop('a')
        assert 'a' in idx.levels[0]
        assert 'a' not in idx

    @pytest.mark.parametrize("data, expected", [
        (MultiIndex.from_product([(), ()]), True),
        (MultiIndex.from_product([(1, 2), (3, 4)]), True),
        (MultiIndex.from_product([('a', 'b'), (1, 2)]), False),
    ])
    def test_multiindex_is_homogeneous_type(self, data, expected):
        assert data._is_homogeneous_type is expected

    def test_indexing_over_hashtable_size_cutoff(self):
        n = 10000

        old_cutoff = _index._SIZE_CUTOFF
        _index._SIZE_CUTOFF = 20000

        s = Series(np.arange(n),
                   MultiIndex.from_arrays((["a"] * n, np.arange(n))))

        # hai it works!
        assert s[("a", 5)] == 5
        assert s[("a", 6)] == 6
        assert s[("a", 7)] == 7

        _index._SIZE_CUTOFF = old_cutoff

    def test_multi_nan_indexing(self):

        # GH 3588
        df = DataFrame({"a": ['R1', 'R2', np.nan, 'R4'],
                        'b': ["C1", "C2", "C3", "C4"],
                        "c": [10, 15, np.nan, 20]})
        result = df.set_index(['a', 'b'], drop=False)
        expected = DataFrame({"a": ['R1', 'R2', np.nan, 'R4'],
                              'b': ["C1", "C2", "C3", "C4"],
                              "c": [10, 15, np.nan, 20]},
                             index=[Index(['R1', 'R2', np.nan, 'R4'],
                                          name='a'),
                                    Index(['C1', 'C2', 'C3', 'C4'], name='b')])
        tm.assert_frame_equal(result, expected)