import itertools
from warnings import catch_warnings

import numpy as np
import pytest

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


@pytest.fixture
def single_level_multiindex():
    """single level MultiIndex"""
    return MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                      codes=[[0, 1, 2, 3]], names=['first'])


@pytest.fixture
def frame_random_data_integer_multi_index():
    levels = [[0, 1], [0, 1, 2]]
    codes = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
    index = MultiIndex(levels=levels, codes=codes)
    return DataFrame(np.random.randn(6, 2), index=index)


@pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
class TestMultiIndexLoc(object):

    def test_loc_getitem_series(self):
        # GH14730
        # passing a series as a key with a MultiIndex
        index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
        x = Series(index=index, data=range(9), dtype=np.float64)
        y = Series([1, 3])
        expected = Series(
            data=[0, 1, 2, 6, 7, 8],
            index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
            dtype=np.float64)
        result = x.loc[y]
        tm.assert_series_equal(result, expected)

        result = x.loc[[1, 3]]
        tm.assert_series_equal(result, expected)

        # GH15424
        y1 = Series([1, 3], index=[1, 2])
        result = x.loc[y1]
        tm.assert_series_equal(result, expected)

        empty = Series(data=[], dtype=np.float64)
        expected = Series([], index=MultiIndex(
            levels=index.levels, codes=[[], []], dtype=np.float64))
        result = x.loc[empty]
        tm.assert_series_equal(result, expected)

    def test_loc_getitem_array(self):
        # GH15434
        # passing an array as a key with a MultiIndex
        index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
        x = Series(index=index, data=range(9), dtype=np.float64)
        y = np.array([1, 3])
        expected = Series(
            data=[0, 1, 2, 6, 7, 8],
            index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
            dtype=np.float64)
        result = x.loc[y]
        tm.assert_series_equal(result, expected)

        # empty array:
        empty = np.array([])
        expected = Series([], index=MultiIndex(
            levels=index.levels, codes=[[], []], dtype=np.float64))
        result = x.loc[empty]
        tm.assert_series_equal(result, expected)

        # 0-dim array (scalar):
        scalar = np.int64(1)
        expected = Series(
            data=[0, 1, 2],
            index=['A', 'B', 'C'],
            dtype=np.float64)
        result = x.loc[scalar]
        tm.assert_series_equal(result, expected)

    def test_loc_multiindex(self):

        mi_labels = DataFrame(np.random.randn(3, 3),
                              columns=[['i', 'i', 'j'], ['A', 'A', 'B']],
                              index=[['i', 'i', 'j'], ['X', 'X', 'Y']])

        mi_int = DataFrame(np.random.randn(3, 3),
                           columns=[[2, 2, 4], [6, 8, 10]],
                           index=[[4, 4, 8], [8, 10, 12]])

        # the first row
        rs = mi_labels.loc['i']
        with catch_warnings(record=True):
            xp = mi_labels.ix['i']
        tm.assert_frame_equal(rs, xp)

        # 2nd (last) columns
        rs = mi_labels.loc[:, 'j']
        with catch_warnings(record=True):
            xp = mi_labels.ix[:, 'j']
        tm.assert_frame_equal(rs, xp)

        # corner column
        rs = mi_labels.loc['j'].loc[:, 'j']
        with catch_warnings(record=True):
            xp = mi_labels.ix['j'].ix[:, 'j']
        tm.assert_frame_equal(rs, xp)

        # with a tuple
        rs = mi_labels.loc[('i', 'X')]
        with catch_warnings(record=True):
            xp = mi_labels.ix[('i', 'X')]
        tm.assert_frame_equal(rs, xp)

        rs = mi_int.loc[4]
        with catch_warnings(record=True):
            xp = mi_int.ix[4]
        tm.assert_frame_equal(rs, xp)

        # missing label
        pytest.raises(KeyError, lambda: mi_int.loc[2])
        with catch_warnings(record=True):
            # GH 21593
            pytest.raises(KeyError, lambda: mi_int.ix[2])

    def test_loc_multiindex_indexer_none(self):

        # GH6788
        # multi-index indexer is None (meaning take all)
        attributes = ['Attribute' + str(i) for i in range(1)]
        attribute_values = ['Value' + str(i) for i in range(5)]

        index = MultiIndex.from_product([attributes, attribute_values])
        df = 0.1 * np.random.randn(10, 1 * 5) + 0.5
        df = DataFrame(df, columns=index)
        result = df[attributes]
        tm.assert_frame_equal(result, df)

        # GH 7349
        # loc with a multi-index seems to be doing fallback
        df = DataFrame(np.arange(12).reshape(-1, 1),
                       index=MultiIndex.from_product([[1, 2, 3, 4],
                                                      [1, 2, 3]]))

        expected = df.loc[([1, 2], ), :]
        result = df.loc[[1, 2]]
        tm.assert_frame_equal(result, expected)

    def test_loc_multiindex_incomplete(self):

        # GH 7399
        # incomplete indexers
        s = Series(np.arange(15, dtype='int64'),
                   MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
        expected = s.loc[:, 'a':'c']

        result = s.loc[0:4, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        result = s.loc[:4, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        result = s.loc[0:, 'a':'c']
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result, expected)

        # GH 7400
        # multiindexer gettitem with list of indexers skips wrong element
        s = Series(np.arange(15, dtype='int64'),
                   MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
        expected = s.iloc[[6, 7, 8, 12, 13, 14]]
        result = s.loc[2:4:2, 'a':'c']
        tm.assert_series_equal(result, expected)

    def test_get_loc_single_level(self, single_level_multiindex):
        single_level = single_level_multiindex
        s = Series(np.random.randn(len(single_level)),
                   index=single_level)
        for k in single_level.values:
            s[k]

    def test_loc_getitem_int_slice(self):
        # GH 3053
        # loc should treat integer slices like label slices

        index = MultiIndex.from_tuples([t for t in itertools.product(
            [6, 7, 8], ['a', 'b'])])
        df = DataFrame(np.random.randn(6, 6), index, index)
        result = df.loc[6:8, :]
        expected = df
        tm.assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([t
                                        for t in itertools.product(
                                            [10, 20, 30], ['a', 'b'])])
        df = DataFrame(np.random.randn(6, 6), index, index)
        result = df.loc[20:30, :]
        expected = df.iloc[2:]
        tm.assert_frame_equal(result, expected)

        # doc examples
        result = df.loc[10, :]
        expected = df.iloc[0:2]
        expected.index = ['a', 'b']
        tm.assert_frame_equal(result, expected)

        result = df.loc[:, 10]
        # expected = df.ix[:,10] (this fails)
        expected = df[10]
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        'indexer_type_1',
        (list, tuple, set, slice, np.ndarray, Series, Index))
    @pytest.mark.parametrize(
        'indexer_type_2',
        (list, tuple, set, slice, np.ndarray, Series, Index))
    def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2):
        # GH #19686
        # .loc should work with nested indexers which can be
        # any list-like objects (see `pandas.api.types.is_list_like`) or slices

        def convert_nested_indexer(indexer_type, keys):
            if indexer_type == np.ndarray:
                return np.array(keys)
            if indexer_type == slice:
                return slice(*keys)
            return indexer_type(keys)

        a = [10, 20, 30]
        b = [1, 2, 3]
        index = MultiIndex.from_product([a, b])
        df = DataFrame(
            np.arange(len(index), dtype='int64'),
            index=index, columns=['Data'])

        keys = ([10, 20], [2, 3])
        types = (indexer_type_1, indexer_type_2)

        # check indexers with all the combinations of nested objects
        # of all the valid types
        indexer = tuple(
            convert_nested_indexer(indexer_type, k)
            for indexer_type, k in zip(types, keys))

        result = df.loc[indexer, 'Data']
        expected = Series(
            [1, 2, 4, 5], name='Data',
            index=MultiIndex.from_product(keys))

        tm.assert_series_equal(result, expected)


@pytest.mark.parametrize('indexer, is_level1, expected_error', [
    ([], False, None),  # empty ok
    (['A'], False, None),
    (['A', 'D'], False, None),
    (['D'], False, r"\['D'\] not in index"),  # not any values found
    (pd.IndexSlice[:, ['foo']], True, None),
    (pd.IndexSlice[:, ['foo', 'bah']], True, None)
])
def test_loc_getitem_duplicates_multiindex_missing_indexers(indexer, is_level1,
                                                            expected_error):
    # GH 7866
    # multi-index slicing with missing indexers
    idx = MultiIndex.from_product([['A', 'B', 'C'],
                                   ['foo', 'bar', 'baz']],
                                  names=['one', 'two'])
    s = Series(np.arange(9, dtype='int64'), index=idx).sort_index()

    if indexer == []:
        expected = s.iloc[[]]
    elif is_level1:
        expected = Series([0, 3, 6], index=MultiIndex.from_product(
            [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
    else:
        exp_idx = MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
                                          names=['one', 'two'])
        expected = Series(np.arange(3, dtype='int64'),
                          index=exp_idx).sort_index()

    if expected_error is not None:
        with pytest.raises(KeyError, match=expected_error):
            s.loc[indexer]
    else:
        result = s.loc[indexer]
        tm.assert_series_equal(result, expected)


@pytest.mark.filterwarnings("ignore:\\n.ix:DeprecationWarning")
@pytest.mark.parametrize('indexer', [
    lambda s: s.loc[[(2000, 3, 10), (2000, 3, 13)]],
    lambda s: s.ix[[(2000, 3, 10), (2000, 3, 13)]]
])
def test_series_loc_getitem_fancy(
        multiindex_year_month_day_dataframe_random_data, indexer):
    s = multiindex_year_month_day_dataframe_random_data['A']
    expected = s.reindex(s.index[49:51])

    result = indexer(s)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize('columns_indexer', [
    ([], slice(None)),
    (['foo'], [])
])
def test_loc_getitem_duplicates_multiindex_empty_indexer(columns_indexer):
    # GH 8737
    # empty indexer
    multi_index = MultiIndex.from_product((['foo', 'bar', 'baz'],
                                           ['alpha', 'beta']))
    df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
    df = df.sort_index(level=0, axis=1)

    expected = DataFrame(index=range(5), columns=multi_index.reindex([])[0])
    result = df.loc[:, columns_indexer]
    tm.assert_frame_equal(result, expected)


def test_loc_getitem_duplicates_multiindex_non_scalar_type_object():
    # regression from < 0.14.0
    # GH 7914
    df = DataFrame([[np.mean, np.median], ['mean', 'median']],
                   columns=MultiIndex.from_tuples([('functs', 'mean'),
                                                   ('functs', 'median')]),
                   index=['function', 'name'])
    result = df.loc['function', ('functs', 'mean')]
    expected = np.mean
    assert result == expected


def test_loc_getitem_tuple_plus_slice():
    # GH 671
    df = DataFrame({'a': np.arange(10),
                    'b': np.arange(10),
                    'c': np.random.randn(10),
                    'd': np.random.randn(10)}