import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
from pandas.core.indexing import IndexingError


@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)


class TestMultiIndexLoc:
    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),
            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),
            dtype="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_labels(self):
        df = DataFrame(
            np.random.randn(3, 3),
            columns=[["i", "i", "j"], ["A", "A", "B"]],
            index=[["i", "i", "j"], ["X", "X", "Y"]],
        )

        # the first 2 rows
        expected = df.iloc[[0, 1]].droplevel(0)
        result = df.loc["i"]
        tm.assert_frame_equal(result, expected)

        # 2nd (last) column
        expected = df.iloc[:, [2]].droplevel(0, axis=1)
        result = df.loc[:, "j"]
        tm.assert_frame_equal(result, expected)

        # bottom right corner
        expected = df.iloc[[2], [2]].droplevel(0).droplevel(0, axis=1)
        result = df.loc["j"].loc[:, "j"]
        tm.assert_frame_equal(result, expected)

        # with a tuple
        expected = df.iloc[[0, 1]]
        result = df.loc[("i", "X")]
        tm.assert_frame_equal(result, expected)

    def test_loc_multiindex_ints(self):
        df = DataFrame(
            np.random.randn(3, 3),
            columns=[[2, 2, 4], [6, 8, 10]],
            index=[[4, 4, 8], [8, 10, 12]],
        )
        expected = df.iloc[[0, 1]].droplevel(0)
        result = df.loc[4]
        tm.assert_frame_equal(result, expected)

    def test_loc_multiindex_missing_label_raises(self):
        df = DataFrame(
            np.random.randn(3, 3),
            columns=[[2, 2, 4], [6, 8, 10]],
            index=[[4, 4, 8], [8, 10, 12]],
        )

        with pytest.raises(KeyError, match=r"^2$"):
            df.loc[2]

    @pytest.mark.parametrize("key, pos", [([2, 4], [0, 1]), ([2], []), ([2, 3], [])])
    def test_loc_multiindex_list_missing_label(self, key, pos):
        # GH 27148 - lists with missing labels _do_ raise
        df = DataFrame(
            np.random.randn(3, 3),
            columns=[[2, 2, 4], [6, 8, 10]],
            index=[[4, 4, 8], [8, 10, 12]],
        )

        with pytest.raises(KeyError, match="not in index"):
            df.loc[key]

    def test_loc_multiindex_too_many_dims_raises(self):
        # GH 14885
        s = Series(
            range(8),
            index=MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]]),
        )

        with pytest.raises(KeyError, match=r"^\('a', 'b'\)$"):
            s.loc["a", "b"]
        with pytest.raises(KeyError, match=r"^\('a', 'd', 'g'\)$"):
            s.loc["a", "d", "g"]
        with pytest.raises(IndexingError, match="Too many indexers"):
            s.loc["a", "d", "g", "j"]

    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_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_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[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("klass", [Series, DataFrame])
    def test_multiindex_loc_one_dimensional_tuple(self, klass):
        # GH#37711
        mi = MultiIndex.from_tuples([("a", "A"), ("b", "A")])
        obj = klass([1, 2], index=mi)
        obj.loc[("a",)] = 0
        expected = klass([0, 2], index=mi)
        tm.assert_equal(obj, expected)

    @pytest.mark.parametrize("indexer", [("a",), ("a")])
    def test_multiindex_one_dimensional_tuple_columns(self, indexer):
        # GH#37711
        mi = MultiIndex.from_tuples([("a", "A"), ("b", "A")])
        obj = DataFrame([1, 2], index=mi)
        obj.loc[indexer, :] = 0
        expected = DataFrame([0, 2], index=mi)
        tm.assert_frame_equal(obj, expected)


@pytest.mark.parametrize(
    "indexer, pos",
    [
        ([], []),  # empty ok
        (["A"], slice(3)),
        (["A", "D"], []),  # "D" isnt present -> raise
        (["D", "E"], []),  # no values found -> raise
        (["D"], []),  # same, with single item list: GH 27148
        (pd.IndexSlice[:, ["foo"]], slice(2, None, 3)),
        (pd.IndexSlice[:, ["foo", "bah"]], slice(2, None, 3)),
    ],
)
def test_loc_getitem_duplicates_multiindex_missing_indexers(indexer, pos):
    # 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()
    expected = s.iloc[pos]

    if expected.size == 0 and indexer != []:
        with pytest.raises(KeyError, match=str(indexer)):
            s.loc[indexer]
    else:
        result = s.loc[indexer]
        tm.assert_series_equal(result, expected)


def test_series_loc_getitem_fancy(multiindex_year_month_day_dataframe_random_data):
    s = multiindex_year_month_day_dataframe_random_data["A"]
    expected = s.reindex(s.index[49:51])
    result = s.loc[[(2000, 3, 10), (2000, 3, 13)]]
    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),
        }
    ).set_index(["a", "b"])
    expected = df.loc[0, 0]
    result = df.loc[(0, 0), :]
    tm.assert_series_equal(result, expected)


def test_loc_getitem_int(frame_random_data_integer_multi_index):
    df = frame_random_data_integer_multi_index
    result = df.loc[1]
    expected = df[-3:]
    expected.index = expected.index.droplevel(0)
    tm.assert_frame_equal(result, expected)


def test_loc_getitem_int_raises_exception(frame_random_data_integer_multi_index):
    df = frame_random_data_integer_multi_index
    with pytest.raises(KeyError, match=r"^3$"):
        df.loc[3]


def test_loc_getitem_lowerdim_corner(multiindex_dataframe_random_data):
    df = multiindex_dataframe_random_data

    # test setup - check key not in dataframe
    with pytest.raises(KeyError, match=r"^\('bar', 'three'\)$"):
        df.loc[("bar", "three"), "B"]

    # in theory should be inserting in a sorted space????
    df.loc[("bar", "three"), "B"] = 0
    expected = 0
    result = df.sort_index().loc[("bar", "three"), "B"]
    assert result == expected


def test_loc_setitem_single_column_slice():
    # case from https://github.com/pandas-dev/pandas/issues/27841
    df = DataFrame(
        "string",
        index=list("abcd"),
        columns=MultiIndex.from_product([["Main"], ("another", "one")]),
    )
    df["labels"] = "a"
    df.loc[:, "labels"] = df.index
    tm.assert_numpy_array_equal(np.asarray(df["labels"]), np.asarray(df.index))

    # test with non-object block
    df = DataFrame(
        np.nan,
        index=range(4),
        columns=MultiIndex.from_tuples([("A", "1"), ("A", "2"), ("B", "1")]),
    )
    expected = df.copy()
    df.loc[:, "B"] = np.arange(4)
    expected.iloc[:, 2] = np.arange(4)
    tm.assert_frame_equal(df, expected)


def test_loc_nan_multiindex():
    # GH 5286
    tups = [
        ("Good Things", "C", np.nan),
        ("Good Things", "R", np.nan),
        ("Bad Things", "C", np.nan),
        ("Bad Things", "T", np.nan),
        ("Okay Things", "N", "B"),
        ("Okay Things", "N", "D"),
        ("Okay Things", "B", np.nan),
        ("Okay Things", "D", np.nan),
    ]
    df = DataFrame(
        np.ones((8, 4)),
        columns=Index(["d1", "d2", "d3", "d4"]),
        index=MultiIndex.from_tuples(tups, names=["u1", "u2", "u3"]),
    )
    result = df.loc["Good Things"].loc["C"]
    expected = DataFrame(
        np.ones((1, 4)),
        index=Index([np.nan], dtype="object", name="u3"),
        columns=Index(["d1", "d2", "d3", "d4"], dtype="object"),
    )
    tm.assert_frame_equal(result, expected)


def test_loc_period_string_indexing():
    # GH 9892
    a = pd.period_range("2013Q1", "2013Q4", freq="Q")
    i = (1111, 2222, 3333)
    idx = pd.MultiIndex.from_product((a, i), names=("Periode", "CVR"))
    df = pd.DataFrame(
        index=idx,
        columns=(
            "OMS",
            "OMK",
            "RES",
            "DRIFT_IND",
            "OEVRIG_IND",
            "FIN_IND",
            "VARE_UD",
            "LOEN_UD",
            "FIN_UD",
        ),
    )
    result = df.loc[("2013Q1", 1111), "OMS"]
    expected = pd.Series(
        [np.nan],
        dtype=object,
        name="OMS",
        index=pd.MultiIndex.from_tuples(
            [(pd.Period("2013Q1"), 1111)], names=["Periode", "CVR"]
        ),
    )
    tm.assert_series_equal(result, expected)


def test_loc_datetime_mask_slicing():
    # GH 16699
    dt_idx = pd.to_datetime(["2017-05-04", "2017-05-05"])
    m_idx = pd.MultiIndex.from_product([dt_idx, dt_idx], names=["Idx1", "Idx2"])
    df = pd.DataFrame(
        data=[[1, 2], [3, 4], [5, 6], [7, 6]], index=m_idx, columns=["C1", "C2"]
    )
    result = df.loc[(dt_idx[0], (df.index.get_level_values(1) > "2017-05-04")), "C1"]
    expected = pd.Series(
        [3],
        name="C1",
        index=MultiIndex.from_tuples(
            [(pd.Timestamp("2017-05-04"), pd.Timestamp("2017-05-05"))],
            names=["Idx1", "Idx2"],
        ),
    )
    tm.assert_series_equal(result, expected)


def test_loc_datetime_series_tuple_slicing():
    # https://github.com/pandas-dev/pandas/issues/35858
    date = pd.Timestamp("2000")
    ser = pd.Series(
        1, index=pd.MultiIndex.from_tuples([("a", date)], names=["a", "b"]), name="c",
    )
    result = ser.loc[:, [date]]
    tm.assert_series_equal(result, ser)


def test_loc_with_mi_indexer():
    # https://github.com/pandas-dev/pandas/issues/35351
    df = DataFrame(
        data=[["a", 1], ["a", 0], ["b", 1], ["c", 2]],
        index=MultiIndex.from_tuples(
            [(0, 1), (1, 0), (1, 1), (1, 1)], names=["index", "date"]
        ),
        columns=["author", "price"],
    )
    idx = MultiIndex.from_tuples([(0, 1), (1, 1)], names=["index", "date"])
    result = df.loc[idx, :]
    expected = DataFrame(
        [["a", 1], ["b", 1], ["c", 2]],
        index=MultiIndex.from_tuples([(0, 1), (1, 1), (1, 1)], names=["index", "date"]),
        columns=["author", "price"],
    )
    tm.assert_frame_equal(result, expected)
