from datetime import date, datetime, time, timedelta
import re

import numpy as np
import pytest

from pandas._libs import iNaT

from pandas.core.dtypes.common import is_float_dtype, is_integer

import pandas as pd
from pandas import (
    DataFrame,
    DatetimeIndex,
    Index,
    MultiIndex,
    Series,
    Timestamp,
    date_range,
    isna,
    notna,
)
import pandas._testing as tm
import pandas.core.common as com
from pandas.core.indexing import IndexingError

from pandas.tseries.offsets import BDay

# We pass through a TypeError raised by numpy
_slice_msg = "slice indices must be integers or None or have an __index__ method"


class TestDataFrameIndexing:
    def test_getitem(self, float_frame):
        # Slicing
        sl = float_frame[:20]
        assert len(sl.index) == 20

        # Column access
        for _, series in sl.items():
            assert len(series.index) == 20
            assert tm.equalContents(series.index, sl.index)

        for key, _ in float_frame._series.items():
            assert float_frame[key] is not None

        assert "random" not in float_frame
        with pytest.raises(KeyError, match="random"):
            float_frame["random"]

        df = float_frame.copy()
        df["$10"] = np.random.randn(len(df))

        ad = np.random.randn(len(df))
        df["@awesome_domain"] = ad

        with pytest.raises(KeyError, match=re.escape("'df[\"$10\"]'")):
            df.__getitem__('df["$10"]')

        res = df["@awesome_domain"]
        tm.assert_numpy_array_equal(ad, res.values)

    def test_getitem_dupe_cols(self):
        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
        msg = "\"None of [Index(['baf'], dtype='object')] are in the [columns]\""
        with pytest.raises(KeyError, match=re.escape(msg)):
            df[["baf"]]

    @pytest.mark.parametrize("key_type", [iter, np.array, Series, Index])
    def test_loc_iterable(self, float_frame, key_type):
        idx = key_type(["A", "B", "C"])
        result = float_frame.loc[:, idx]
        expected = float_frame.loc[:, ["A", "B", "C"]]
        tm.assert_frame_equal(result, expected)

    def test_loc_timedelta_0seconds(self):
        # GH#10583
        df = pd.DataFrame(np.random.normal(size=(10, 4)))
        df.index = pd.timedelta_range(start="0s", periods=10, freq="s")
        expected = df.loc[pd.Timedelta("0s") :, :]
        result = df.loc["0s":, :]
        tm.assert_frame_equal(expected, result)

    @pytest.mark.parametrize(
        "idx_type",
        [
            list,
            iter,
            Index,
            set,
            lambda l: dict(zip(l, range(len(l)))),
            lambda l: dict(zip(l, range(len(l)))).keys(),
        ],
        ids=["list", "iter", "Index", "set", "dict", "dict_keys"],
    )
    @pytest.mark.parametrize("levels", [1, 2])
    def test_getitem_listlike(self, idx_type, levels, float_frame):
        # GH 21294

        if levels == 1:
            frame, missing = float_frame, "food"
        else:
            # MultiIndex columns
            frame = DataFrame(
                np.random.randn(8, 3),
                columns=Index(
                    [("foo", "bar"), ("baz", "qux"), ("peek", "aboo")],
                    name=("sth", "sth2"),
                ),
            )
            missing = ("good", "food")

        keys = [frame.columns[1], frame.columns[0]]
        idx = idx_type(keys)
        idx_check = list(idx_type(keys))

        result = frame[idx]

        expected = frame.loc[:, idx_check]
        expected.columns.names = frame.columns.names

        tm.assert_frame_equal(result, expected)

        idx = idx_type(keys + [missing])
        with pytest.raises(KeyError, match="not in index"):
            frame[idx]

    @pytest.mark.parametrize(
        "val,expected", [(2 ** 63 - 1, Series([1])), (2 ** 63, Series([2]))]
    )
    def test_loc_uint64(self, val, expected):
        # see gh-19399
        df = DataFrame([1, 2], index=[2 ** 63 - 1, 2 ** 63])
        result = df.loc[val]

        expected.name = val
        tm.assert_series_equal(result, expected)

    def test_getitem_callable(self, float_frame):
        # GH 12533
        result = float_frame[lambda x: "A"]
        tm.assert_series_equal(result, float_frame.loc[:, "A"])

        result = float_frame[lambda x: ["A", "B"]]
        tm.assert_frame_equal(result, float_frame.loc[:, ["A", "B"]])

        df = float_frame[:3]
        result = df[lambda x: [True, False, True]]
        tm.assert_frame_equal(result, float_frame.iloc[[0, 2], :])

    def test_setitem_list(self, float_frame):

        float_frame["E"] = "foo"
        data = float_frame[["A", "B"]]
        float_frame[["B", "A"]] = data

        tm.assert_series_equal(float_frame["B"], data["A"], check_names=False)
        tm.assert_series_equal(float_frame["A"], data["B"], check_names=False)

        msg = "Columns must be same length as key"
        with pytest.raises(ValueError, match=msg):
            data[["A"]] = float_frame[["A", "B"]]
        newcolumndata = range(len(data.index) - 1)
        msg = (
            rf"Length of values \({len(newcolumndata)}\) "
            rf"does not match length of index \({len(data)}\)"
        )
        with pytest.raises(ValueError, match=msg):
            data["A"] = newcolumndata

        df = DataFrame(0, index=range(3), columns=["tt1", "tt2"], dtype=np.int_)
        df.loc[1, ["tt1", "tt2"]] = [1, 2]

        result = df.loc[df.index[1], ["tt1", "tt2"]]
        expected = Series([1, 2], df.columns, dtype=np.int_, name=1)
        tm.assert_series_equal(result, expected)

        df["tt1"] = df["tt2"] = "0"
        df.loc[df.index[1], ["tt1", "tt2"]] = ["1", "2"]
        result = df.loc[df.index[1], ["tt1", "tt2"]]
        expected = Series(["1", "2"], df.columns, name=1)
        tm.assert_series_equal(result, expected)

    def test_setitem_list_not_dataframe(self, float_frame):
        data = np.random.randn(len(float_frame), 2)
        float_frame[["A", "B"]] = data
        tm.assert_almost_equal(float_frame[["A", "B"]].values, data)

    def test_setitem_list_of_tuples(self, float_frame):
        tuples = list(zip(float_frame["A"], float_frame["B"]))
        float_frame["tuples"] = tuples

        result = float_frame["tuples"]
        expected = Series(tuples, index=float_frame.index, name="tuples")
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "columns,box,expected",
        [
            (
                ["A", "B", "C", "D"],
                7,
                pd.DataFrame(
                    [[7, 7, 7, 7], [7, 7, 7, 7], [7, 7, 7, 7]],
                    columns=["A", "B", "C", "D"],
                ),
            ),
            (
                ["C", "D"],
                [7, 8],
                pd.DataFrame(
                    [[1, 2, 7, 8], [3, 4, 7, 8], [5, 6, 7, 8]],
                    columns=["A", "B", "C", "D"],
                ),
            ),
            (
                ["A", "B", "C"],
                np.array([7, 8, 9], dtype=np.int64),
                pd.DataFrame(
                    [[7, 8, 9], [7, 8, 9], [7, 8, 9]], columns=["A", "B", "C"]
                ),
            ),
            (
                ["B", "C", "D"],
                [[7, 8, 9], [10, 11, 12], [13, 14, 15]],
                pd.DataFrame(
                    [[1, 7, 8, 9], [3, 10, 11, 12], [5, 13, 14, 15]],
                    columns=["A", "B", "C", "D"],
                ),
            ),
            (
                ["C", "A", "D"],
                np.array([[7, 8, 9], [10, 11, 12], [13, 14, 15]], dtype=np.int64),
                pd.DataFrame(
                    [[8, 2, 7, 9], [11, 4, 10, 12], [14, 6, 13, 15]],
                    columns=["A", "B", "C", "D"],
                ),
            ),
            (
                ["A", "C"],
                pd.DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]),
                pd.DataFrame(
                    [[7, 2, 8], [9, 4, 10], [11, 6, 12]], columns=["A", "B", "C"]
                ),
            ),
        ],
    )
    def test_setitem_list_missing_columns(self, columns, box, expected):
        # GH 29334
        df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "B"])
        df[columns] = box
        tm.assert_frame_equal(df, expected)

    def test_setitem_multi_index(self):
        # GH7655, test that assigning to a sub-frame of a frame
        # with multi-index columns aligns both rows and columns
        it = ["jim", "joe", "jolie"], ["first", "last"], ["left", "center", "right"]

        cols = MultiIndex.from_product(it)
        index = pd.date_range("20141006", periods=20)
        vals = np.random.randint(1, 1000, (len(index), len(cols)))
        df = pd.DataFrame(vals, columns=cols, index=index)

        i, j = df.index.values.copy(), it[-1][:]

        np.random.shuffle(i)
        df["jim"] = df["jolie"].loc[i, ::-1]
        tm.assert_frame_equal(df["jim"], df["jolie"])

        np.random.shuffle(j)
        df[("joe", "first")] = df[("jolie", "last")].loc[i, j]
        tm.assert_frame_equal(df[("joe", "first")], df[("jolie", "last")])

        np.random.shuffle(j)
        df[("joe", "last")] = df[("jolie", "first")].loc[i, j]
        tm.assert_frame_equal(df[("joe", "last")], df[("jolie", "first")])

    def test_setitem_callable(self):
        # GH 12533
        df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]})
        df[lambda x: "A"] = [11, 12, 13, 14]

        exp = pd.DataFrame({"A": [11, 12, 13, 14], "B": [5, 6, 7, 8]})
        tm.assert_frame_equal(df, exp)

    def test_setitem_other_callable(self):
        # GH 13299
        def inc(x):
            return x + 1

        df = pd.DataFrame([[-1, 1], [1, -1]])
        df[df > 0] = inc

        expected = pd.DataFrame([[-1, inc], [inc, -1]])
        tm.assert_frame_equal(df, expected)

    def test_getitem_boolean(
        self, float_string_frame, mixed_float_frame, mixed_int_frame, datetime_frame
    ):
        # boolean indexing
        d = datetime_frame.index[10]
        indexer = datetime_frame.index > d
        indexer_obj = indexer.astype(object)

        subindex = datetime_frame.index[indexer]
        subframe = datetime_frame[indexer]

        tm.assert_index_equal(subindex, subframe.index)
        with pytest.raises(ValueError, match="Item wrong length"):
            datetime_frame[indexer[:-1]]

        subframe_obj = datetime_frame[indexer_obj]
        tm.assert_frame_equal(subframe_obj, subframe)

        with pytest.raises(ValueError, match="Boolean array expected"):
            datetime_frame[datetime_frame]

        # test that Series work
        indexer_obj = Series(indexer_obj, datetime_frame.index)

        subframe_obj = datetime_frame[indexer_obj]
        tm.assert_frame_equal(subframe_obj, subframe)

        # test that Series indexers reindex
        # we are producing a warning that since the passed boolean
        # key is not the same as the given index, we will reindex
        # not sure this is really necessary
        with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
            indexer_obj = indexer_obj.reindex(datetime_frame.index[::-1])
            subframe_obj = datetime_frame[indexer_obj]
            tm.assert_frame_equal(subframe_obj, subframe)

        # test df[df > 0]
        for df in [
            datetime_frame,
            float_string_frame,
            mixed_float_frame,
            mixed_int_frame,
        ]:
            if df is float_string_frame:
                continue

            data = df._get_numeric_data()
            bif = df[df > 0]
            bifw = DataFrame(
                {c: np.where(data[c] > 0, data[c], np.nan) for c in data.columns},
                index=data.index,
                columns=data.columns,
            )

            # add back other columns to compare
            for c in df.columns:
                if c not in bifw:
                    bifw[c] = df[c]
            bifw = bifw.reindex(columns=df.columns)

            tm.assert_frame_equal(bif, bifw, check_dtype=False)
            for c in df.columns:
                if bif[c].dtype != bifw[c].dtype:
                    assert bif[c].dtype == df[c].dtype

    def test_getitem_boolean_casting(self, datetime_frame):

        # don't upcast if we don't need to
        df = datetime_frame.copy()
        df["E"] = 1
        df["E"] = df["E"].astype("int32")
        df["E1"] = df["E"].copy()
        df["F"] = 1
        df["F"] = df["F"].astype("int64")
        df["F1"] = df["F"].copy()

        casted = df[df > 0]
        result = casted.dtypes
        expected = Series(
            [np.dtype("float64")] * 4
            + [np.dtype("int32")] * 2
            + [np.dtype("int64")] * 2,
            index=["A", "B", "C", "D", "E", "E1", "F", "F1"],
        )
        tm.assert_series_equal(result, expected)

        # int block splitting
        df.loc[df.index[1:3], ["E1", "F1"]] = 0
        casted = df[df > 0]
        result = casted.dtypes
        expected = Series(
            [np.dtype("float64")] * 4
            + [np.dtype("int32")]
            + [np.dtype("float64")]
            + [np.dtype("int64")]
            + [np.dtype("float64")],
            index=["A", "B", "C", "D", "E", "E1", "F", "F1"],
        )
        tm.assert_series_equal(result, expected)

    def test_getitem_boolean_list(self):
        df = DataFrame(np.arange(12).reshape(3, 4))

        def _checkit(lst):
            result = df[lst]
            expected = df.loc[df.index[lst]]
            tm.assert_frame_equal(result, expected)

        _checkit([True, False, True])
        _checkit([True, True, True])
        _checkit([False, False, False])

    def test_getitem_boolean_iadd(self):
        arr = np.random.randn(5, 5)

        df = DataFrame(arr.copy(), columns=["A", "B", "C", "D", "E"])

        df[df < 0] += 1
        arr[arr < 0] += 1

        tm.assert_almost_equal(df.values, arr)

    def test_boolean_index_empty_corner(self):
        # #2096
        blah = DataFrame(np.empty([0, 1]), columns=["A"], index=DatetimeIndex([]))

        # both of these should succeed trivially
        k = np.array([], bool)

        blah[k]
        blah[k] = 0

    def test_getitem_ix_mixed_integer(self):
        df = DataFrame(
            np.random.randn(4, 3), index=[1, 10, "C", "E"], columns=[1, 2, 3]
        )

        result = df.iloc[:-1]
        expected = df.loc[df.index[:-1]]
        tm.assert_frame_equal(result, expected)

        result = df.loc[[1, 10]]
        expected = df.loc[Index([1, 10])]
        tm.assert_frame_equal(result, expected)

        # 11320
        df = pd.DataFrame(
            {
                "rna": (1.5, 2.2, 3.2, 4.5),
                -1000: [11, 21, 36, 40],
                0: [10, 22, 43, 34],
                1000: [0, 10, 20, 30],
            },
            columns=["rna", -1000, 0, 1000],
        )
        result = df[[1000]]
        expected = df.iloc[:, [3]]
        tm.assert_frame_equal(result, expected)
        result = df[[-1000]]
        expected = df.iloc[:, [1]]
        tm.assert_frame_equal(result, expected)

    def test_getattr(self, float_frame):
        tm.assert_series_equal(float_frame.A, float_frame["A"])
        msg = "'DataFrame' object has no attribute 'NONEXISTENT_NAME'"
        with pytest.raises(AttributeError, match=msg):
            float_frame.NONEXISTENT_NAME

    def test_setattr_column(self):
        df = DataFrame({"foobar": 1}, index=range(10))

        df.foobar = 5
        assert (df.foobar == 5).all()

    def test_setitem(self, float_frame):
        # not sure what else to do here
        series = float_frame["A"][::2]
        float_frame["col5"] = series
        assert "col5" in float_frame

        assert len(series) == 15
        assert len(float_frame) == 30

        exp = np.ravel(np.column_stack((series.values, [np.nan] * 15)))
        exp = Series(exp, index=float_frame.index, name="col5")
        tm.assert_series_equal(float_frame["col5"], exp)

        series = float_frame["A"]
        float_frame["col6"] = series
        tm.assert_series_equal(series, float_frame["col6"], check_names=False)

        # set ndarray
        arr = np.random.randn(len(float_frame))
        float_frame["col9"] = arr
        assert (float_frame["col9"] == arr).all()

        float_frame["col7"] = 5
        assert (float_frame["col7"] == 5).all()

        float_frame["col0"] = 3.14
        assert (float_frame["col0"] == 3.14).all()

        float_frame["col8"] = "foo"
        assert (float_frame["col8"] == "foo").all()

        # this is partially a view (e.g. some blocks are view)
        # so raise/warn
        smaller = float_frame[:2]

        msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
        with pytest.raises(com.SettingWithCopyError, match=msg):
            smaller["col10"] = ["1", "2"]

        assert smaller["col10"].dtype == np.object_
        assert (smaller["col10"] == ["1", "2"]).all()

        # dtype changing GH4204
        df = DataFrame([[0, 0]])
        df.iloc[0] = np.nan
        expected = DataFrame([[np.nan, np.nan]])
        tm.assert_frame_equal(df, expected)

        df = DataFrame([[0, 0]])
        df.loc[0] = np.nan
        tm.assert_frame_equal(df, expected)

    @pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
    def test_setitem_dtype(self, dtype, float_frame):
        arr = np.random.randn(len(float_frame))

        float_frame[dtype] = np.array(arr, dtype=dtype)
        assert float_frame[dtype].dtype.name == dtype

    def test_setitem_tuple(self, float_frame):
        float_frame["A", "B"] = float_frame["A"]
        tm.assert_series_equal(
            float_frame["A", "B"], float_frame["A"], check_names=False
        )

    def test_setitem_always_copy(self, float_frame):
        s = float_frame["A"].copy()
        float_frame["E"] = s

        float_frame["E"][5:10] = np.nan
        assert notna(s[5:10]).all()

    def test_setitem_boolean(self, float_frame):
        df = float_frame.copy()
        values = float_frame.values

        df[df["A"] > 0] = 4
        values[values[:, 0] > 0] = 4
        tm.assert_almost_equal(df.values, values)

        # test that column reindexing works
        series = df["A"] == 4
        series = series.reindex(df.index[::-1])
        df[series] = 1
        values[values[:, 0] == 4] = 1
        tm.assert_almost_equal(df.values, values)

        df[df > 0] = 5
        values[values > 0] = 5
        tm.assert_almost_equal(df.values, values)

        df[df == 5] = 0
        values[values == 5] = 0
        tm.assert_almost_equal(df.values, values)

        # a df that needs alignment first
        df[df[:-1] < 0] = 2
        np.putmask(values[:-1], values[:-1] < 0, 2)
        tm.assert_almost_equal(df.values, values)

        # indexed with same shape but rows-reversed df
        df[df[::-1] == 2] = 3
        values[values == 2] = 3
        tm.assert_almost_equal(df.values, values)

        msg = "Must pass DataFrame or 2-d ndarray with boolean values only"
        with pytest.raises(TypeError, match=msg):
            df[df * 0] = 2

        # index with DataFrame
        mask = df > np.abs(df)
        expected = df.copy()
        df[df > np.abs(df)] = np.nan
        expected.values[mask.values] = np.nan
        tm.assert_frame_equal(df, expected)

        # set from DataFrame
        expected = df.copy()
        df[df > np.abs(df)] = df * 2
        np.putmask(expected.values, mask.values, df.values * 2)
        tm.assert_frame_equal(df, expected)

    @pytest.mark.parametrize(
        "mask_type",
        [lambda df: df > np.abs(df) / 2, lambda df: (df > np.abs(df) / 2).values],
        ids=["dataframe", "array"],
    )
    def test_setitem_boolean_mask(self, mask_type, float_frame):

        # Test for issue #18582
        df = float_frame.copy()
        mask = mask_type(df)

        # index with boolean mask
        result = df.copy()
        result[mask] = np.nan

        expected = df.copy()
        expected.values[np.array(mask)] = np.nan
        tm.assert_frame_equal(result, expected)

    def test_setitem_cast(self, float_frame):
        float_frame["D"] = float_frame["D"].astype("i8")
        assert float_frame["D"].dtype == np.int64

        # #669, should not cast?
        # this is now set to int64, which means a replacement of the column to
        # the value dtype (and nothing to do with the existing dtype)
        float_frame["B"] = 0
        assert float_frame["B"].dtype == np.int64

        # cast if pass array of course
        float_frame["B"] = np.arange(len(float_frame))
        assert issubclass(float_frame["B"].dtype.type, np.integer)

        float_frame["foo"] = "bar"
        float_frame["foo"] = 0
        assert float_frame["foo"].dtype == np.int64

        float_frame["foo"] = "bar"
        float_frame["foo"] = 2.5
        assert float_frame["foo"].dtype == np.float64

        float_frame["something"] = 0
        assert float_frame["something"].dtype == np.int64
        float_frame["something"] = 2
        assert float_frame["something"].dtype == np.int64
        float_frame["something"] = 2.5
        assert float_frame["something"].dtype == np.float64

        # GH 7704
        # dtype conversion on setting
        df = DataFrame(np.random.rand(30, 3), columns=tuple("ABC"))
        df["event"] = np.nan
        df.loc[10, "event"] = "foo"
        result = df.dtypes
        expected = Series(
            [np.dtype("float64")] * 3 + [np.dtype("object")],
            index=["A", "B", "C", "event"],
        )
        tm.assert_series_equal(result, expected)

        # Test that data type is preserved . #5782
        df = DataFrame({"one": np.arange(6, dtype=np.int8)})
        df.loc[1, "one"] = 6
        assert df.dtypes.one == np.dtype(np.int8)
        df.one = np.int8(7)
        assert df.dtypes.one == np.dtype(np.int8)

    def test_setitem_boolean_column(self, float_frame):
        expected = float_frame.copy()
        mask = float_frame["A"] > 0

        float_frame.loc[mask, "B"] = 0
        expected.values[mask.values, 1] = 0

        tm.assert_frame_equal(float_frame, expected)

    def test_frame_setitem_timestamp(self):
        # GH#2155
        columns = date_range(start="1/1/2012", end="2/1/2012", freq=BDay())
        data = DataFrame(columns=columns, index=range(10))
        t = datetime(2012, 11, 1)
        ts = Timestamp(t)
        data[ts] = np.nan  # works, mostly a smoke-test
        assert np.isnan(data[ts]).all()

    def test_setitem_corner(self, float_frame):
        # corner case
        df = DataFrame({"B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]}, index=np.arange(3))
        del df["B"]
        df["B"] = [1.0, 2.0, 3.0]
        assert "B" in df
        assert len(df.columns) == 2

        df["A"] = "beginning"
        df["E"] = "foo"
        df["D"] = "bar"
        df[datetime.now()] = "date"
        df[datetime.now()] = 5.0

        # what to do when empty frame with index
        dm = DataFrame(index=float_frame.index)
        dm["A"] = "foo"
        dm["B"] = "bar"
        assert len(dm.columns) == 2
        assert dm.values.dtype == np.object_

        # upcast
        dm["C"] = 1
        assert dm["C"].dtype == np.int64

        dm["E"] = 1.0
        assert dm["E"].dtype == np.float64

        # set existing column
        dm["A"] = "bar"
        assert "bar" == dm["A"][0]

        dm = DataFrame(index=np.arange(3))
        dm["A"] = 1
        dm["foo"] = "bar"
        del dm["foo"]
        dm["foo"] = "bar"
        assert dm["foo"].dtype == np.object_

        dm["coercable"] = ["1", "2", "3"]
        assert dm["coercable"].dtype == np.object_

    def test_setitem_corner2(self):
        data = {
            "title": ["foobar", "bar", "foobar"] + ["foobar"] * 17,
            "cruft": np.random.random(20),
        }

        df = DataFrame(data)
        ix = df[df["title"] == "bar"].index

        df.loc[ix, ["title"]] = "foobar"
        df.loc[ix, ["cruft"]] = 0

        assert df.loc[1, "title"] == "foobar"
        assert df.loc[1, "cruft"] == 0

    def test_setitem_ambig(self):
        # Difficulties with mixed-type data
        from decimal import Decimal

        # Created as float type
        dm = DataFrame(index=range(3), columns=range(3))

        coercable_series = Series([Decimal(1) for _ in range(3)], index=range(3))
        uncoercable_series = Series(["foo", "bzr", "baz"], index=range(3))

        dm[0] = np.ones(3)
        assert len(dm.columns) == 3

        dm[1] = coercable_series
        assert len(dm.columns) == 3

        dm[2] = uncoercable_series
        assert len(dm.columns) == 3
        assert dm[2].dtype == np.object_

    def test_setitem_clear_caches(self):
        # see gh-304
        df = DataFrame(
            {"x": [1.1, 2.1, 3.1, 4.1], "y": [5.1, 6.1, 7.1, 8.1]}, index=[0, 1, 2, 3]
        )
        df.insert(2, "z", np.nan)

        # cache it
        foo = df["z"]
        df.loc[df.index[2:], "z"] = 42

        expected = Series([np.nan, np.nan, 42, 42], index=df.index, name="z")

        assert df["z"] is not foo
        tm.assert_series_equal(df["z"], expected)

    def test_setitem_None(self, float_frame):
        # GH #766
        float_frame[None] = float_frame["A"]
        tm.assert_series_equal(
            float_frame.iloc[:, -1], float_frame["A"], check_names=False
        )
        tm.assert_series_equal(
            float_frame.loc[:, None], float_frame["A"], check_names=False
        )
        tm.assert_series_equal(float_frame[None], float_frame["A"], check_names=False)
        repr(float_frame)

    def test_setitem_empty(self):
        # GH 9596
        df = pd.DataFrame(
            {"a": ["1", "2", "3"], "b": ["11", "22", "33"], "c": ["111", "222", "333"]}
        )

        result = df.copy()
        result.loc[result.b.isna(), "a"] = result.a
        tm.assert_frame_equal(result, df)

    @pytest.mark.parametrize("dtype", ["float", "int64"])
    @pytest.mark.parametrize("kwargs", [dict(), dict(index=[1]), dict(columns=["A"])])
    def test_setitem_empty_frame_with_boolean(self, dtype, kwargs):
        # see gh-10126
        kwargs["dtype"] = dtype
        df = DataFrame(**kwargs)

        df2 = df.copy()
        df[df > df2] = 47
        tm.assert_frame_equal(df, df2)

    def test_setitem_with_empty_listlike(self):
        # GH #17101
        index = pd.Index([], name="idx")
        result = pd.DataFrame(columns=["A"], index=index)
        result["A"] = []
        expected = pd.DataFrame(columns=["A"], index=index)
        tm.assert_index_equal(result.index, expected.index)

    def test_setitem_scalars_no_index(self):
        # GH16823 / 17894
        df = DataFrame()
        df["foo"] = 1
        expected = DataFrame(columns=["foo"]).astype(np.int64)
        tm.assert_frame_equal(df, expected)

    def test_getitem_empty_frame_with_boolean(self):
        # Test for issue #11859

        df = pd.DataFrame()
        df2 = df[df > 0]
        tm.assert_frame_equal(df, df2)

    def test_slice_floats(self):
        index = [52195.504153, 52196.303147, 52198.369883]
        df = DataFrame(np.random.rand(3, 2), index=index)

        s1 = df.loc[52195.1:52196.5]
        assert len(s1) == 2

        s1 = df.loc[52195.1:52196.6]
        assert len(s1) == 2

        s1 = df.loc[52195.1:52198.9]
        assert len(s1) == 3

    def test_getitem_fancy_slice_integers_step(self):
        df = DataFrame(np.random.randn(10, 5))

        # this is OK
        result = df.iloc[:8:2]  # noqa
        df.iloc[:8:2] = np.nan
        assert isna(df.iloc[:8:2]).values.all()

    def test_getitem_setitem_integer_slice_keyerrors(self):
        df = DataFrame(np.random.randn(10, 5), index=range(0, 20, 2))

        # this is OK
        cp = df.copy()
        cp.iloc[4:10] = 0
        assert (cp.iloc[4:10] == 0).values.all()

        # so is this
        cp = df.copy()
        cp.iloc[3:11] = 0
        assert (cp.iloc[3:11] == 0).values.all()

        result = df.iloc[2:6]
        result2 = df.loc[3:11]
        expected = df.reindex([4, 6, 8, 10])

        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

        # non-monotonic, raise KeyError
        df2 = df.iloc[list(range(5)) + list(range(5, 10))[::-1]]
        with pytest.raises(KeyError, match=r"^3$"):
            df2.loc[3:11]
        with pytest.raises(KeyError, match=r"^3$"):
            df2.loc[3:11] = 0

    def test_fancy_getitem_slice_mixed(self, float_frame, float_string_frame):
        sliced = float_string_frame.iloc[:, -3:]
        assert sliced["D"].dtype == np.float64

        # get view with single block
        # setting it triggers setting with copy
        sliced = float_frame.iloc[:, -3:]

        msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
        with pytest.raises(com.SettingWithCopyError, match=msg):
            sliced["C"] = 4.0

        assert (float_frame["C"] == 4).all()

    def test_setitem_slice_position(self):
        # GH#31469
        df = pd.DataFrame(np.zeros((100, 1)))
        df[-4:] = 1
        arr = np.zeros((100, 1))
        arr[-4:] = 1
        expected = pd.DataFrame(arr)
        tm.assert_frame_equal(df, expected)

    def test_getitem_setitem_non_ix_labels(self):
        df = tm.makeTimeDataFrame()

        start, end = df.index[[5, 10]]

        result = df.loc[start:end]
        result2 = df[start:end]
        expected = df[5:11]
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

        result = df.copy()
        result.loc[start:end] = 0
        result2 = df.copy()
        result2[start:end] = 0
        expected = df.copy()
        expected[5:11] = 0
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

    def test_ix_multi_take(self):
        df = DataFrame(np.random.randn(3, 2))
        rs = df.loc[df.index == 0, :]
        xp = df.reindex([0])
        tm.assert_frame_equal(rs, xp)

        # FIXME: dont leave commented-out
        """ #1321
        df = DataFrame(np.random.randn(3, 2))
        rs = df.loc[df.index==0, df.columns==1]
        xp = df.reindex([0], [1])
        tm.assert_frame_equal(rs, xp)
        """

    def test_getitem_fancy_scalar(self, float_frame):
        f = float_frame
        ix = f.loc

        # individual value
        for col in f.columns:
            ts = f[col]
            for idx in f.index[::5]:
                assert ix[idx, col] == ts[idx]

    def test_setitem_fancy_scalar(self, float_frame):
        f = float_frame
        expected = float_frame.copy()
        ix = f.loc

        # individual value
        for j, col in enumerate(f.columns):
            ts = f[col]  # noqa
            for idx in f.index[::5]:
                i = f.index.get_loc(idx)
                val = np.random.randn()
                expected.values[i, j] = val

                ix[idx, col] = val
                tm.assert_frame_equal(f, expected)

    def test_getitem_fancy_boolean(self, float_frame):
        f = float_frame
        ix = f.loc

        expected = f.reindex(columns=["B", "D"])
        result = ix[:, [False, True, False, True]]
        tm.assert_frame_equal(result, expected)

        expected = f.reindex(index=f.index[5:10], columns=["B", "D"])
        result = ix[f.index[5:10], [False, True, False, True]]
        tm.assert_frame_equal(result, expected)

        boolvec = f.index > f.index[7]
        expected = f.reindex(index=f.index[boolvec])
        result = ix[boolvec]
        tm.assert_frame_equal(result, expected)
        result = ix[boolvec, :]
        tm.assert_frame_equal(result, expected)

        result = ix[boolvec, f.columns[2:]]
        expected = f.reindex(index=f.index[boolvec], columns=["C", "D"])
        tm.assert_frame_equal(result, expected)

    def test_setitem_fancy_boolean(self, float_frame):
        # from 2d, set with booleans
        frame = float_frame.copy()
        expected = float_frame.copy()

        mask = frame["A"] > 0
        frame.loc[mask] = 0.0
        expected.values[mask.values] = 0.0
        tm.assert_frame_equal(frame, expected)

        frame = float_frame.copy()
        expected = float_frame.copy()
        frame.loc[mask, ["A", "B"]] = 0.0
        expected.values[mask.values, :2] = 0.0
        tm.assert_frame_equal(frame, expected)

    def test_getitem_fancy_ints(self, float_frame):
        result = float_frame.iloc[[1, 4, 7]]
        expected = float_frame.loc[float_frame.index[[1, 4, 7]]]
        tm.assert_frame_equal(result, expected)

        result = float_frame.iloc[:, [2, 0, 1]]
        expected = float_frame.loc[:, float_frame.columns[[2, 0, 1]]]
        tm.assert_frame_equal(result, expected)

    def test_getitem_setitem_fancy_exceptions(self, float_frame):
        ix = float_frame.iloc
        with pytest.raises(IndexingError, match="Too many indexers"):
            ix[:, :, :]

        with pytest.raises(IndexError, match="too many indices for array"):
            # GH#32257 we let numpy do validation, get their exception
            ix[:, :, :] = 1

    def test_getitem_setitem_boolean_misaligned(self, float_frame):
        # boolean index misaligned labels
        mask = float_frame["A"][::-1] > 1

        result = float_frame.loc[mask]
        expected = float_frame.loc[mask[::-1]]
        tm.assert_frame_equal(result, expected)

        cp = float_frame.copy()
        expected = float_frame.copy()
        cp.loc[mask] = 0
        expected.loc[mask] = 0
        tm.assert_frame_equal(cp, expected)

    def test_getitem_setitem_boolean_multi(self):
        df = DataFrame(np.random.randn(3, 2))

        # get
        k1 = np.array([True, False, True])
        k2 = np.array([False, True])
        result = df.loc[k1, k2]
        expected = df.loc[[0, 2], [1]]
        tm.assert_frame_equal(result, expected)

        expected = df.copy()
        df.loc[np.array([True, False, True]), np.array([False, True])] = 5
        expected.loc[[0, 2], [1]] = 5
        tm.assert_frame_equal(df, expected)

    def test_getitem_setitem_float_labels(self):
        index = Index([1.5, 2, 3, 4, 5])
        df = DataFrame(np.random.randn(5, 5), index=index)

        result = df.loc[1.5:4]
        expected = df.reindex([1.5, 2, 3, 4])
        tm.assert_frame_equal(result, expected)
        assert len(result) == 4

        result = df.loc[4:5]
        expected = df.reindex([4, 5])  # reindex with int
        tm.assert_frame_equal(result, expected, check_index_type=False)
        assert len(result) == 2

        result = df.loc[4:5]
        expected = df.reindex([4.0, 5.0])  # reindex with float
        tm.assert_frame_equal(result, expected)
        assert len(result) == 2

        # loc_float changes this to work properly
        result = df.loc[1:2]
        expected = df.iloc[0:2]
        tm.assert_frame_equal(result, expected)

        df.loc[1:2] = 0
        result = df[1:2]
        assert (result == 0).all().all()

        # #2727
        index = Index([1.0, 2.5, 3.5, 4.5, 5.0])
        df = DataFrame(np.random.randn(5, 5), index=index)

        # positional slicing only via iloc!
        msg = (
            "cannot do positional indexing on Float64Index with "
            r"these indexers \[1.0\] of type float"
        )
        with pytest.raises(TypeError, match=msg):
            df.iloc[1.0:5]

        result = df.iloc[4:5]
        expected = df.reindex([5.0])
        tm.assert_frame_equal(result, expected)
        assert len(result) == 1

        cp = df.copy()

        with pytest.raises(TypeError, match=_slice_msg):
            cp.iloc[1.0:5] = 0

        with pytest.raises(TypeError, match=msg):
            result = cp.iloc[1.0:5] == 0  # noqa

        assert result.values.all()
        assert (cp.iloc[0:1] == df.iloc[0:1]).values.all()

        cp = df.copy()
        cp.iloc[4:5] = 0
        assert (cp.iloc[4:5] == 0).values.all()
        assert (cp.iloc[0:4] == df.iloc[0:4]).values.all()

        # float slicing
        result = df.loc[1.0:5]
        expected = df
        tm.assert_frame_equal(result, expected)
        assert len(result) == 5

        result = df.loc[1.1:5]
        expected = df.reindex([2.5, 3.5, 4.5, 5.0])
        tm.assert_frame_equal(result, expected)
        assert len(result) == 4

        result = df.loc[4.51:5]
        expected = df.reindex([5.0])
        tm.assert_frame_equal(result, expected)
        assert len(result) == 1

        result = df.loc[1.0:5.0]
        expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0])
        tm.assert_frame_equal(result, expected)
        assert len(result) == 5

        cp = df.copy()
        cp.loc[1.0:5.0] = 0
        result = cp.loc[1.0:5.0]
        assert (result == 0).values.all()

    def test_setitem_single_column_mixed(self):
        df = DataFrame(
            np.random.randn(5, 3),
            index=["a", "b", "c", "d", "e"],
            columns=["foo", "bar", "baz"],
        )
        df["str"] = "qux"
        df.loc[df.index[::2], "str"] = np.nan
        expected = np.array([np.nan, "qux", np.nan, "qux", np.nan], dtype=object)
        tm.assert_almost_equal(df["str"].values, expected)

    def test_setitem_single_column_mixed_datetime(self):
        df = DataFrame(
            np.random.randn(5, 3),
            index=["a", "b", "c", "d", "e"],
            columns=["foo", "bar", "baz"],
        )

        df["timestamp"] = Timestamp("20010102")

        # check our dtypes
        result = df.dtypes
        expected = Series(
            [np.dtype("float64")] * 3 + [np.dtype("datetime64[ns]")],
            index=["foo", "bar", "baz", "timestamp"],
        )
        tm.assert_series_equal(result, expected)

        # GH#16674 iNaT is treated as an integer when given by the user
        df.loc["b", "timestamp"] = iNaT
        assert not isna(df.loc["b", "timestamp"])
        assert df["timestamp"].dtype == np.object_
        assert df.loc["b", "timestamp"] == iNaT

        # allow this syntax
        df.loc["c", "timestamp"] = np.nan
        assert isna(df.loc["c", "timestamp"])

        # allow this syntax
        df.loc["d", :] = np.nan
        assert not isna(df.loc["c", :]).all()

        # FIXME: don't leave commented-out
        # as of GH 3216 this will now work!
        # try to set with a list like item
        # pytest.raises(
        #    Exception, df.loc.__setitem__, ('d', 'timestamp'), [np.nan])

    def test_setitem_mixed_datetime(self):
        # GH 9336
        expected = DataFrame(
            {
                "a": [0, 0, 0, 0, 13, 14],
                "b": [
                    datetime(2012, 1, 1),
                    1,
                    "x",
                    "y",
                    datetime(2013, 1, 1),
                    datetime(2014, 1, 1),
                ],
            }
        )
        df = pd.DataFrame(0, columns=list("ab"), index=range(6))
        df["b"] = pd.NaT
        df.loc[0, "b"] = datetime(2012, 1, 1)
        df.loc[1, "b"] = 1
        df.loc[[2, 3], "b"] = "x", "y"
        A = np.array(
            [
                [13, np.datetime64("2013-01-01T00:00:00")],
                [14, np.datetime64("2014-01-01T00:00:00")],
            ]
        )
        df.loc[[4, 5], ["a", "b"]] = A
        tm.assert_frame_equal(df, expected)

    def test_setitem_frame_float(self, float_frame):
        piece = float_frame.loc[float_frame.index[:2], ["A", "B"]]
        float_frame.loc[float_frame.index[-2] :, ["A", "B"]] = piece.values
        result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values
        expected = piece.values
        tm.assert_almost_equal(result, expected)

    def test_setitem_frame_mixed(self, float_string_frame):
        # GH 3216

        # already aligned
        f = float_string_frame.copy()
        piece = DataFrame(
            [[1.0, 2.0], [3.0, 4.0]], index=f.index[0:2], columns=["A", "B"]
        )
        key = (f.index[slice(None, 2)], ["A", "B"])
        f.loc[key] = piece
        tm.assert_almost_equal(f.loc[f.index[0:2], ["A", "B"]].values, piece.values)

        # rows unaligned
        f = float_string_frame.copy()
        piece = DataFrame(
            [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]],
            index=list(f.index[0:2]) + ["foo", "bar"],
            columns=["A", "B"],
        )
        key = (f.index[slice(None, 2)], ["A", "B"])
        f.loc[key] = piece
        tm.assert_almost_equal(
            f.loc[f.index[0:2:], ["A", "B"]].values, piece.values[0:2]
        )

        # key is unaligned with values
        f = float_string_frame.copy()
        piece = f.loc[f.index[:2], ["A"]]
        piece.index = f.index[-2:]
        key = (f.index[slice(-2, None)], ["A", "B"])
        f.loc[key] = piece
        piece["B"] = np.nan
        tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values)

        # ndarray
        f = float_string_frame.copy()
        piece = float_string_frame.loc[f.index[:2], ["A", "B"]]
        key = (f.index[slice(-2, None)], ["A", "B"])
        f.loc[key] = piece.values
        tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values)

    def test_setitem_frame_upcast(self):
        # needs upcasting
        df = DataFrame([[1, 2, "foo"], [3, 4, "bar"]], columns=["A", "B", "C"])
        df2 = df.copy()
        df2.loc[:, ["A", "B"]] = df.loc[:, ["A", "B"]] + 0.5
        expected = df.reindex(columns=["A", "B"])
        expected += 0.5
        expected["C"] = df["C"]
        tm.assert_frame_equal(df2, expected)

    def test_setitem_frame_align(self, float_frame):
        piece = float_frame.loc[float_frame.index[:2], ["A", "B"]]
        piece.index = float_frame.index[-2:]
        piece.columns = ["A", "B"]
        float_frame.loc[float_frame.index[-2:], ["A", "B"]] = piece
        result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values
        expected = piece.values
        tm.assert_almost_equal(result, expected)

    def test_getitem_setitem_ix_duplicates(self):
        # #1201
        df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"])

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

        result = df.loc["bar"]
        expected = df.iloc[[2, 4]]
        tm.assert_frame_equal(result, expected)

        result = df.loc["baz"]
        expected = df.iloc[3]
        tm.assert_series_equal(result, expected)

    def test_getitem_ix_boolean_duplicates_multiple(self):
        # #1201
        df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"])

        result = df.loc[["bar"]]
        exp = df.iloc[[2, 4]]
        tm.assert_frame_equal(result, exp)

        result = df.loc[df[1] > 0]
        exp = df[df[1] > 0]
        tm.assert_frame_equal(result, exp)

        result = df.loc[df[0] > 0]
        exp = df[df[0] > 0]
        tm.assert_frame_equal(result, exp)

    def test_getitem_setitem_ix_bool_keyerror(self):
        # #2199
        df = DataFrame({"a": [1, 2, 3]})

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

        msg = "cannot use a single bool to index into setitem"
        with pytest.raises(KeyError, match=msg):
            df.loc[False] = 0
        with pytest.raises(KeyError, match=msg):
            df.loc[True] = 0

    def test_getitem_list_duplicates(self):
        # #1943
        df = DataFrame(np.random.randn(4, 4), columns=list("AABC"))
        df.columns.name = "foo"

        result = df[["B", "C"]]
        assert result.columns.name == "foo"

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

    def test_get_value(self, float_frame):
        for idx in float_frame.index:
            for col in float_frame.columns:
                result = float_frame._get_value(idx, col)
                expected = float_frame[col][idx]
                assert result == expected

    def test_lookup_float(self, float_frame):
        df = float_frame
        rows = list(df.index) * len(df.columns)
        cols = list(df.columns) * len(df.index)
        result = df.lookup(rows, cols)

        expected = np.array([df.loc[r, c] for r, c in zip(rows, cols)])
        tm.assert_numpy_array_equal(result, expected)

    def test_lookup_mixed(self, float_string_frame):
        df = float_string_frame
        rows = list(df.index) * len(df.columns)
        cols = list(df.columns) * len(df.index)
        result = df.lookup(rows, cols)

        expected = np.array(
            [df.loc[r, c] for r, c in zip(rows, cols)], dtype=np.object_
        )
        tm.assert_almost_equal(result, expected)

    def test_lookup_bool(self):
        df = DataFrame(
            {
                "label": ["a", "b", "a", "c"],
                "mask_a": [True, True, False, True],
                "mask_b": [True, False, False, False],
                "mask_c": [False, True, False, True],
            }
        )
        df["mask"] = df.lookup(df.index, "mask_" + df["label"])

        exp_mask = np.array(
            [df.loc[r, c] for r, c in zip(df.index, "mask_" + df["label"])]
        )

        tm.assert_series_equal(df["mask"], pd.Series(exp_mask, name="mask"))
        assert df["mask"].dtype == np.bool_

    def test_lookup_raises(self, float_frame):
        with pytest.raises(KeyError, match="'One or more row labels was not found'"):
            float_frame.lookup(["xyz"], ["A"])

        with pytest.raises(KeyError, match="'One or more column labels was not found'"):
            float_frame.lookup([float_frame.index[0]], ["xyz"])

        with pytest.raises(ValueError, match="same size"):
            float_frame.lookup(["a", "b", "c"], ["a"])

    def test_lookup_requires_unique_axes(self):
        # GH#33041 raise with a helpful error message
        df = pd.DataFrame(np.random.randn(6).reshape(3, 2), columns=["A", "A"])

        rows = [0, 1]
        cols = ["A", "A"]

        # homogeneous-dtype case
        with pytest.raises(ValueError, match="requires unique index and columns"):
            df.lookup(rows, cols)
        with pytest.raises(ValueError, match="requires unique index and columns"):
            df.T.lookup(cols, rows)

        # heterogeneous dtype
        df["B"] = 0
        with pytest.raises(ValueError, match="requires unique index and columns"):
            df.lookup(rows, cols)

    def test_set_value(self, float_frame):
        for idx in float_frame.index:
            for col in float_frame.columns:
                float_frame._set_value(idx, col, 1)
                assert float_frame[col][idx] == 1

    def test_set_value_resize(self, float_frame):

        res = float_frame._set_value("foobar", "B", 0)
        assert res is None
        assert float_frame.index[-1] == "foobar"
        assert float_frame._get_value("foobar", "B") == 0

        float_frame.loc["foobar", "qux"] = 0
        assert float_frame._get_value("foobar", "qux") == 0

        res = float_frame.copy()
        res._set_value("foobar", "baz", "sam")
        assert res["baz"].dtype == np.object_

        res = float_frame.copy()
        res._set_value("foobar", "baz", True)
        assert res["baz"].dtype == np.object_

        res = float_frame.copy()
        res._set_value("foobar", "baz", 5)
        assert is_float_dtype(res["baz"])
        assert isna(res["baz"].drop(["foobar"])).all()
        msg = "could not convert string to float: 'sam'"
        with pytest.raises(ValueError, match=msg):
            res._set_value("foobar", "baz", "sam")

    def test_reindex_with_multi_index(self):
        # https://github.com/pandas-dev/pandas/issues/29896
        # tests for reindexing a multi-indexed DataFrame with a new MultiIndex
        #
        # confirms that we can reindex a multi-indexed DataFrame with a new
        # MultiIndex object correctly when using no filling, backfilling, and
        # padding
        #
        # The DataFrame, `df`, used in this test is:
        #       c
        #  a b
        # -1 0  A
        #    1  B
        #    2  C
        #    3  D
        #    4  E
        #    5  F
        #    6  G
        #  0 0  A
        #    1  B
        #    2  C
        #    3  D
        #    4  E
        #    5  F
        #    6  G
        #  1 0  A
        #    1  B
        #    2  C
        #    3  D
        #    4  E
        #    5  F
        #    6  G
        #
        # and the other MultiIndex, `new_multi_index`, is:
        # 0: 0 0.5
        # 1:   2.0
        # 2:   5.0
        # 3:   5.8
        df = pd.DataFrame(
            {
                "a": [-1] * 7 + [0] * 7 + [1] * 7,
                "b": list(range(7)) * 3,
                "c": ["A", "B", "C", "D", "E", "F", "G"] * 3,
            }
        ).set_index(["a", "b"])
        new_index = [0.5, 2.0, 5.0, 5.8]
        new_multi_index = MultiIndex.from_product([[0], new_index], names=["a", "b"])

        # reindexing w/o a `method` value
        reindexed = df.reindex(new_multi_index)
        expected = pd.DataFrame(
            {"a": [0] * 4, "b": new_index, "c": [np.nan, "C", "F", np.nan]}
        ).set_index(["a", "b"])
        tm.assert_frame_equal(expected, reindexed)

        # reindexing with backfilling
        expected = pd.DataFrame(
            {"a": [0] * 4, "b": new_index, "c": ["B", "C", "F", "G"]}
        ).set_index(["a", "b"])
        reindexed_with_backfilling = df.reindex(new_multi_index, method="bfill")
        tm.assert_frame_equal(expected, reindexed_with_backfilling)

        reindexed_with_backfilling = df.reindex(new_multi_index, method="backfill")
        tm.assert_frame_equal(expected, reindexed_with_backfilling)

        # reindexing with padding
        expected = pd.DataFrame(
            {"a": [0] * 4, "b": new_index, "c": ["A", "C", "F", "F"]}
        ).set_index(["a", "b"])
        reindexed_with_padding = df.reindex(new_multi_index, method="pad")
        tm.assert_frame_equal(expected, reindexed_with_padding)

        reindexed_with_padding = df.reindex(new_multi_index, method="ffill")
        tm.assert_frame_equal(expected, reindexed_with_padding)

    def test_set_value_with_index_dtype_change(self):
        df_orig = DataFrame(np.random.randn(3, 3), index=range(3), columns=list("ABC"))

        # this is actually ambiguous as the 2 is interpreted as a positional
        # so column is not created
        df = df_orig.copy()
        df._set_value("C", 2, 1.0)
        assert list(df.index) == list(df_orig.index) + ["C"]
        # assert list(df.columns) == list(df_orig.columns) + [2]

        df = df_orig.copy()
        df.loc["C", 2] = 1.0
        assert list(df.index) == list(df_orig.index) + ["C"]
        # assert list(df.columns) == list(df_orig.columns) + [2]

        # create both new
        df = df_orig.copy()
        df._set_value("C", "D", 1.0)
        assert list(df.index) == list(df_orig.index) + ["C"]
        assert list(df.columns) == list(df_orig.columns) + ["D"]

        df = df_orig.copy()
        df.loc["C", "D"] = 1.0
        assert list(df.index) == list(df_orig.index) + ["C"]
        assert list(df.columns) == list(df_orig.columns) + ["D"]

    def test_get_set_value_no_partial_indexing(self):
        # partial w/ MultiIndex raise exception
        index = MultiIndex.from_tuples([(0, 1), (0, 2), (1, 1), (1, 2)])
        df = DataFrame(index=index, columns=range(4))
        with pytest.raises(KeyError, match=r"^0$"):
            df._get_value(0, 1)

    # TODO: rename?  remove?
    def test_single_element_ix_dont_upcast(self, float_frame):
        float_frame["E"] = 1
        assert issubclass(float_frame["E"].dtype.type, (int, np.integer))

        result = float_frame.loc[float_frame.index[5], "E"]
        assert is_integer(result)

        # GH 11617
        df = pd.DataFrame(dict(a=[1.23]))
        df["b"] = 666

        result = df.loc[0, "b"]
        assert is_integer(result)

        expected = Series([666], [0], name="b")
        result = df.loc[[0], "b"]
        tm.assert_series_equal(result, expected)

    def test_iloc_row(self):
        df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))

        result = df.iloc[1]
        exp = df.loc[2]
        tm.assert_series_equal(result, exp)

        result = df.iloc[2]
        exp = df.loc[4]
        tm.assert_series_equal(result, exp)

        # slice
        result = df.iloc[slice(4, 8)]
        expected = df.loc[8:14]
        tm.assert_frame_equal(result, expected)

        # verify slice is view
        # setting it makes it raise/warn
        msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
        with pytest.raises(com.SettingWithCopyError, match=msg):
            result[2] = 0.0

        exp_col = df[2].copy()
        exp_col[4:8] = 0.0
        tm.assert_series_equal(df[2], exp_col)

        # list of integers
        result = df.iloc[[1, 2, 4, 6]]
        expected = df.reindex(df.index[[1, 2, 4, 6]])
        tm.assert_frame_equal(result, expected)

    def test_iloc_col(self):

        df = DataFrame(np.random.randn(4, 10), columns=range(0, 20, 2))

        result = df.iloc[:, 1]
        exp = df.loc[:, 2]
        tm.assert_series_equal(result, exp)

        result = df.iloc[:, 2]
        exp = df.loc[:, 4]
        tm.assert_series_equal(result, exp)

        # slice
        result = df.iloc[:, slice(4, 8)]
        expected = df.loc[:, 8:14]
        tm.assert_frame_equal(result, expected)

        # verify slice is view
        # and that we are setting a copy
        msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
        with pytest.raises(com.SettingWithCopyError, match=msg):
            result[8] = 0.0

        assert (df[8] == 0).all()

        # list of integers
        result = df.iloc[:, [1, 2, 4, 6]]
        expected = df.reindex(columns=df.columns[[1, 2, 4, 6]])
        tm.assert_frame_equal(result, expected)

    def test_iloc_duplicates(self):

        df = DataFrame(np.random.rand(3, 3), columns=list("ABC"), index=list("aab"))

        result = df.iloc[0]
        assert isinstance(result, Series)
        tm.assert_almost_equal(result.values, df.values[0])

        result = df.T.iloc[:, 0]
        assert isinstance(result, Series)
        tm.assert_almost_equal(result.values, df.values[0])

        # #2259
        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1, 1, 2])
        result = df.iloc[:, [0]]
        expected = df.take([0], axis=1)
        tm.assert_frame_equal(result, expected)

    def test_loc_duplicates(self):
        # gh-17105

        # insert a duplicate element to the index
        trange = pd.date_range(
            start=pd.Timestamp(year=2017, month=1, day=1),
            end=pd.Timestamp(year=2017, month=1, day=5),
        )

        trange = trange.insert(loc=5, item=pd.Timestamp(year=2017, month=1, day=5))

        df = pd.DataFrame(0, index=trange, columns=["A", "B"])
        bool_idx = np.array([False, False, False, False, False, True])

        # assignment
        df.loc[trange[bool_idx], "A"] = 6

        expected = pd.DataFrame(
            {"A": [0, 0, 0, 0, 6, 6], "B": [0, 0, 0, 0, 0, 0]}, index=trange
        )
        tm.assert_frame_equal(df, expected)

        # in-place
        df = pd.DataFrame(0, index=trange, columns=["A", "B"])
        df.loc[trange[bool_idx], "A"] += 6
        tm.assert_frame_equal(df, expected)

    @pytest.mark.parametrize(
        "method,expected_values",
        [
            ("nearest", [0, 1, 1, 2]),
            ("pad", [np.nan, 0, 1, 1]),
            ("backfill", [0, 1, 2, 2]),
        ],
    )
    def test_reindex_methods(self, method, expected_values):
        df = pd.DataFrame({"x": list(range(5))})
        target = np.array([-0.1, 0.9, 1.1, 1.5])

        expected = pd.DataFrame({"x": expected_values}, index=target)
        actual = df.reindex(target, method=method)
        tm.assert_frame_equal(expected, actual)

        actual = df.reindex(target, method=method, tolerance=1)
        tm.assert_frame_equal(expected, actual)
        actual = df.reindex(target, method=method, tolerance=[1, 1, 1, 1])
        tm.assert_frame_equal(expected, actual)

        e2 = expected[::-1]
        actual = df.reindex(target[::-1], method=method)
        tm.assert_frame_equal(e2, actual)

        new_order = [3, 0, 2, 1]
        e2 = expected.iloc[new_order]
        actual = df.reindex(target[new_order], method=method)
        tm.assert_frame_equal(e2, actual)

        switched_method = (
            "pad" if method == "backfill" else "backfill" if method == "pad" else method
        )
        actual = df[::-1].reindex(target, method=switched_method)
        tm.assert_frame_equal(expected, actual)

    def test_reindex_methods_nearest_special(self):
        df = pd.DataFrame({"x": list(range(5))})
        target = np.array([-0.1, 0.9, 1.1, 1.5])

        expected = pd.DataFrame({"x": [0, 1, 1, np.nan]}, index=target)
        actual = df.reindex(target, method="nearest", tolerance=0.2)
        tm.assert_frame_equal(expected, actual)

        expected = pd.DataFrame({"x": [0, np.nan, 1, np.nan]}, index=target)
        actual = df.reindex(target, method="nearest", tolerance=[0.5, 0.01, 0.4, 0.1])
        tm.assert_frame_equal(expected, actual)

    def test_reindex_nearest_tz(self, tz_aware_fixture):
        # GH26683
        tz = tz_aware_fixture
        idx = pd.date_range("2019-01-01", periods=5, tz=tz)
        df = pd.DataFrame({"x": list(range(5))}, index=idx)

        expected = df.head(3)
        actual = df.reindex(idx[:3], method="nearest")
        tm.assert_frame_equal(expected, actual)

    def test_reindex_nearest_tz_empty_frame(self):
        # https://github.com/pandas-dev/pandas/issues/31964
        dti = pd.DatetimeIndex(["2016-06-26 14:27:26+00:00"])
        df = pd.DataFrame(index=pd.DatetimeIndex(["2016-07-04 14:00:59+00:00"]))
        expected = pd.DataFrame(index=dti)
        result = df.reindex(dti, method="nearest")
        tm.assert_frame_equal(result, expected)

    def test_reindex_frame_add_nat(self):
        rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s")
        df = DataFrame({"A": np.random.randn(len(rng)), "B": rng})

        result = df.reindex(range(15))
        assert np.issubdtype(result["B"].dtype, np.dtype("M8[ns]"))

        mask = com.isna(result)["B"]
        assert mask[-5:].all()
        assert not mask[:-5].any()

    def test_reindex_limit(self):
        # GH 28631
        data = [["A", "A", "A"], ["B", "B", "B"], ["C", "C", "C"], ["D", "D", "D"]]
        exp_data = [
            ["A", "A", "A"],
            ["B", "B", "B"],
            ["C", "C", "C"],
            ["D", "D", "D"],
            ["D", "D", "D"],
            [np.nan, np.nan, np.nan],
        ]
        df = DataFrame(data)
        result = df.reindex([0, 1, 2, 3, 4, 5], method="ffill", limit=1)
        expected = DataFrame(exp_data)
        tm.assert_frame_equal(result, expected)

    def test_set_dataframe_column_ns_dtype(self):
        x = DataFrame([datetime.now(), datetime.now()])
        assert x[0].dtype == np.dtype("M8[ns]")

    def test_non_monotonic_reindex_methods(self):
        dr = pd.date_range("2013-08-01", periods=6, freq="B")
        data = np.random.randn(6, 1)
        df = pd.DataFrame(data, index=dr, columns=list("A"))
        df_rev = pd.DataFrame(data, index=dr[[3, 4, 5] + [0, 1, 2]], columns=list("A"))
        # index is not monotonic increasing or decreasing
        msg = "index must be monotonic increasing or decreasing"
        with pytest.raises(ValueError, match=msg):
            df_rev.reindex(df.index, method="pad")
        with pytest.raises(ValueError, match=msg):
            df_rev.reindex(df.index, method="ffill")
        with pytest.raises(ValueError, match=msg):
            df_rev.reindex(df.index, method="bfill")
        with pytest.raises(ValueError, match=msg):
            df_rev.reindex(df.index, method="nearest")

    def test_reindex_level(self):
        from itertools import permutations

        icol = ["jim", "joe", "jolie"]

        def verify_first_level(df, level, idx, check_index_type=True):
            def f(val):
                return np.nonzero((df[level] == val).to_numpy())[0]

            i = np.concatenate(list(map(f, idx)))
            left = df.set_index(icol).reindex(idx, level=level)
            right = df.iloc[i].set_index(icol)
            tm.assert_frame_equal(left, right, check_index_type=check_index_type)

        def verify(df, level, idx, indexer, check_index_type=True):
            left = df.set_index(icol).reindex(idx, level=level)
            right = df.iloc[indexer].set_index(icol)
            tm.assert_frame_equal(left, right, check_index_type=check_index_type)

        df = pd.DataFrame(
            {
                "jim": list("B" * 4 + "A" * 2 + "C" * 3),
                "joe": list("abcdeabcd")[::-1],
                "jolie": [10, 20, 30] * 3,
                "joline": np.random.randint(0, 1000, 9),
            }
        )

        target = [
            ["C", "B", "A"],
            ["F", "C", "A", "D"],
            ["A"],
            ["A", "B", "C"],
            ["C", "A", "B"],
            ["C", "B"],
            ["C", "A"],
            ["A", "B"],
            ["B", "A", "C"],
        ]

        for idx in target:
            verify_first_level(df, "jim", idx)

        # reindex by these causes different MultiIndex levels
        for idx in [["D", "F"], ["A", "C", "B"]]:
            verify_first_level(df, "jim", idx, check_index_type=False)

        verify(df, "joe", list("abcde"), [3, 2, 1, 0, 5, 4, 8, 7, 6])
        verify(df, "joe", list("abcd"), [3, 2, 1, 0, 5, 8, 7, 6])
        verify(df, "joe", list("abc"), [3, 2, 1, 8, 7, 6])
        verify(df, "joe", list("eca"), [1, 3, 4, 6, 8])
        verify(df, "joe", list("edc"), [0, 1, 4, 5, 6])
        verify(df, "joe", list("eadbc"), [3, 0, 2, 1, 4, 5, 8, 7, 6])
        verify(df, "joe", list("edwq"), [0, 4, 5])
        verify(df, "joe", list("wq"), [], check_index_type=False)

        df = DataFrame(
            {
                "jim": ["mid"] * 5 + ["btm"] * 8 + ["top"] * 7,
                "joe": ["3rd"] * 2
                + ["1st"] * 3
                + ["2nd"] * 3
                + ["1st"] * 2
                + ["3rd"] * 3
                + ["1st"] * 2
                + ["3rd"] * 3
                + ["2nd"] * 2,
                # this needs to be jointly unique with jim and joe or
                # reindexing will fail ~1.5% of the time, this works
                # out to needing unique groups of same size as joe
                "jolie": np.concatenate(
                    [
                        np.random.choice(1000, x, replace=False)
                        for x in [2, 3, 3, 2, 3, 2, 3, 2]
                    ]
                ),
                "joline": np.random.randn(20).round(3) * 10,
            }
        )

        for idx in permutations(df["jim"].unique()):
            for i in range(3):
                verify_first_level(df, "jim", idx[: i + 1])

        i = [2, 3, 4, 0, 1, 8, 9, 5, 6, 7, 10, 11, 12, 13, 14, 18, 19, 15, 16, 17]
        verify(df, "joe", ["1st", "2nd", "3rd"], i)

        i = [0, 1, 2, 3, 4, 10, 11, 12, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 13, 14]
        verify(df, "joe", ["3rd", "2nd", "1st"], i)

        i = [0, 1, 5, 6, 7, 10, 11, 12, 18, 19, 15, 16, 17]
        verify(df, "joe", ["2nd", "3rd"], i)

        i = [0, 1, 2, 3, 4, 10, 11, 12, 8, 9, 15, 16, 17, 13, 14]
        verify(df, "joe", ["3rd", "1st"], i)

    def test_getitem_ix_float_duplicates(self):
        df = pd.DataFrame(
            np.random.randn(3, 3), index=[0.1, 0.2, 0.2], columns=list("abc")
        )
        expect = df.iloc[1:]
        tm.assert_frame_equal(df.loc[0.2], expect)

        expect = df.iloc[1:, 0]
        tm.assert_series_equal(df.loc[0.2, "a"], expect)

        df.index = [1, 0.2, 0.2]
        expect = df.iloc[1:]
        tm.assert_frame_equal(df.loc[0.2], expect)

        expect = df.iloc[1:, 0]
        tm.assert_series_equal(df.loc[0.2, "a"], expect)

        df = pd.DataFrame(
            np.random.randn(4, 3), index=[1, 0.2, 0.2, 1], columns=list("abc")
        )
        expect = df.iloc[1:-1]
        tm.assert_frame_equal(df.loc[0.2], expect)

        expect = df.iloc[1:-1, 0]
        tm.assert_series_equal(df.loc[0.2, "a"], expect)

        df.index = [0.1, 0.2, 2, 0.2]
        expect = df.iloc[[1, -1]]
        tm.assert_frame_equal(df.loc[0.2], expect)

        expect = df.iloc[[1, -1], 0]
        tm.assert_series_equal(df.loc[0.2, "a"], expect)

    def test_setitem_with_unaligned_tz_aware_datetime_column(self):
        # GH 12981
        # Assignment of unaligned offset-aware datetime series.
        # Make sure timezone isn't lost
        column = pd.Series(
            pd.date_range("2015-01-01", periods=3, tz="utc"), name="dates"
        )
        df = pd.DataFrame({"dates": column})
        df["dates"] = column[[1, 0, 2]]
        tm.assert_series_equal(df["dates"], column)

        df = pd.DataFrame({"dates": column})
        df.loc[[0, 1, 2], "dates"] = column[[1, 0, 2]]
        tm.assert_series_equal(df["dates"], column)

    def test_setitem_datetime_coercion(self):
        # gh-1048
        df = pd.DataFrame({"c": [pd.Timestamp("2010-10-01")] * 3})
        df.loc[0:1, "c"] = np.datetime64("2008-08-08")
        assert pd.Timestamp("2008-08-08") == df.loc[0, "c"]
        assert pd.Timestamp("2008-08-08") == df.loc[1, "c"]
        df.loc[2, "c"] = date(2005, 5, 5)
        assert pd.Timestamp("2005-05-05") == df.loc[2, "c"]

    def test_setitem_datetimelike_with_inference(self):
        # GH 7592
        # assignment of timedeltas with NaT

        one_hour = timedelta(hours=1)
        df = DataFrame(index=date_range("20130101", periods=4))
        df["A"] = np.array([1 * one_hour] * 4, dtype="m8[ns]")
        df.loc[:, "B"] = np.array([2 * one_hour] * 4, dtype="m8[ns]")
        df.loc[df.index[:3], "C"] = np.array([3 * one_hour] * 3, dtype="m8[ns]")
        df.loc[:, "D"] = np.array([4 * one_hour] * 4, dtype="m8[ns]")
        df.loc[df.index[:3], "E"] = np.array([5 * one_hour] * 3, dtype="m8[ns]")
        df["F"] = np.timedelta64("NaT")
        df.loc[df.index[:-1], "F"] = np.array([6 * one_hour] * 3, dtype="m8[ns]")
        df.loc[df.index[-3] :, "G"] = date_range("20130101", periods=3)
        df["H"] = np.datetime64("NaT")
        result = df.dtypes
        expected = Series(
            [np.dtype("timedelta64[ns]")] * 6 + [np.dtype("datetime64[ns]")] * 2,
            index=list("ABCDEFGH"),
        )
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("idxer", ["var", ["var"]])
    def test_setitem_datetimeindex_tz(self, idxer, tz_naive_fixture):
        # GH 11365
        tz = tz_naive_fixture
        idx = date_range(start="2015-07-12", periods=3, freq="H", tz=tz)
        expected = DataFrame(1.2, index=idx, columns=["var"])
        result = DataFrame(index=idx, columns=["var"])
        result.loc[:, idxer] = expected
        tm.assert_frame_equal(result, expected)

    def test_at_time_between_time_datetimeindex(self):
        index = date_range("2012-01-01", "2012-01-05", freq="30min")
        df = DataFrame(np.random.randn(len(index), 5), index=index)
        akey = time(12, 0, 0)
        bkey = slice(time(13, 0, 0), time(14, 0, 0))
        ainds = [24, 72, 120, 168]
        binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172]

        result = df.at_time(akey)
        expected = df.loc[akey]
        expected2 = df.iloc[ainds]
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result, expected2)
        assert len(result) == 4

        result = df.between_time(bkey.start, bkey.stop)
        expected = df.loc[bkey]
        expected2 = df.iloc[binds]
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result, expected2)
        assert len(result) == 12

        result = df.copy()
        result.loc[akey] = 0
        result = result.loc[akey]
        expected = df.loc[akey].copy()
        expected.loc[:] = 0
        tm.assert_frame_equal(result, expected)

        result = df.copy()
        result.loc[akey] = 0
        result.loc[akey] = df.iloc[ainds]
        tm.assert_frame_equal(result, df)

        result = df.copy()
        result.loc[bkey] = 0
        result = result.loc[bkey]
        expected = df.loc[bkey].copy()
        expected.loc[:] = 0
        tm.assert_frame_equal(result, expected)

        result = df.copy()
        result.loc[bkey] = 0
        result.loc[bkey] = df.iloc[binds]
        tm.assert_frame_equal(result, df)

    def test_index_namedtuple(self):
        from collections import namedtuple

        IndexType = namedtuple("IndexType", ["a", "b"])
        idx1 = IndexType("foo", "bar")
        idx2 = IndexType("baz", "bof")
        index = Index([idx1, idx2], name="composite_index", tupleize_cols=False)
        df = DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])

        result = df.loc[IndexType("foo", "bar")]["A"]
        assert result == 1

    @pytest.mark.parametrize("tpl", [tuple([1]), tuple([1, 2])])
    def test_index_single_double_tuples(self, tpl):
        # GH 20991
        idx = pd.Index([tuple([1]), tuple([1, 2])], name="A", tupleize_cols=False)
        df = DataFrame(index=idx)

        result = df.loc[[tpl]]
        idx = pd.Index([tpl], name="A", tupleize_cols=False)
        expected = DataFrame(index=idx)
        tm.assert_frame_equal(result, expected)

    def test_boolean_indexing(self):
        idx = list(range(3))
        cols = ["A", "B", "C"]
        df1 = DataFrame(
            index=idx,
            columns=cols,
            data=np.array(
                [[0.0, 0.5, 1.0], [1.5, 2.0, 2.5], [3.0, 3.5, 4.0]], dtype=float
            ),
        )
        df2 = DataFrame(index=idx, columns=cols, data=np.ones((len(idx), len(cols))))

        expected = DataFrame(
            index=idx,
            columns=cols,
            data=np.array([[0.0, 0.5, 1.0], [1.5, 2.0, -1], [-1, -1, -1]], dtype=float),
        )

        df1[df1 > 2.0 * df2] = -1
        tm.assert_frame_equal(df1, expected)
        with pytest.raises(ValueError, match="Item wrong length"):
            df1[df1.index[:-1] > 2] = -1

    def test_boolean_indexing_mixed(self):
        df = DataFrame(
            {
                0: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
                1: {
                    35: np.nan,
                    40: 0.32632316859446198,
                    43: np.nan,
                    49: 0.32632316859446198,
                    50: 0.39114724480578139,
                },
                2: {
                    35: np.nan,
                    40: np.nan,
                    43: 0.29012581014105987,
                    49: np.nan,
                    50: np.nan,
                },
                3: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
                4: {
                    35: 0.34215328467153283,
                    40: np.nan,
                    43: np.nan,
                    49: np.nan,
                    50: np.nan,
                },
                "y": {35: 0, 40: 0, 43: 0, 49: 0, 50: 1},
            }
        )

        # mixed int/float ok
        df2 = df.copy()
        df2[df2 > 0.3] = 1
        expected = df.copy()
        expected.loc[40, 1] = 1
        expected.loc[49, 1] = 1
        expected.loc[50, 1] = 1
        expected.loc[35, 4] = 1
        tm.assert_frame_equal(df2, expected)

        df["foo"] = "test"
        msg = "not supported between instances|unorderable types"

        with pytest.raises(TypeError, match=msg):
            df[df > 0.3] = 1

    def test_type_error_multiindex(self):
        # See gh-12218
        df = DataFrame(
            columns=["i", "c", "x", "y"],
            data=[[0, 0, 1, 2], [1, 0, 3, 4], [0, 1, 1, 2], [1, 1, 3, 4]],
        )
        dg = df.pivot_table(index="i", columns="c", values=["x", "y"])

        with pytest.raises(TypeError, match="unhashable type"):
            dg[:, 0]

        index = Index(range(2), name="i")
        columns = MultiIndex(
            levels=[["x", "y"], [0, 1]], codes=[[0, 1], [0, 0]], names=[None, "c"]
        )
        expected = DataFrame([[1, 2], [3, 4]], columns=columns, index=index)

        result = dg.loc[:, (slice(None), 0)]
        tm.assert_frame_equal(result, expected)

        name = ("x", 0)
        index = Index(range(2), name="i")
        expected = Series([1, 3], index=index, name=name)

        result = dg["x", 0]
        tm.assert_series_equal(result, expected)

    def test_interval_index(self):
        # GH 19977
        index = pd.interval_range(start=0, periods=3)
        df = pd.DataFrame(
            [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"]
        )

        expected = 1
        result = df.loc[0.5, "A"]
        tm.assert_almost_equal(result, expected)

        index = pd.interval_range(start=0, periods=3, closed="both")
        df = pd.DataFrame(
            [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"]
        )

        index_exp = pd.interval_range(start=0, periods=2, freq=1, closed="both")
        expected = pd.Series([1, 4], index=index_exp, name="A")
        result = df.loc[1, "A"]
        tm.assert_series_equal(result, expected)


class TestDataFrameIndexingUInt64:
    def test_setitem(self, uint64_frame):

        df = uint64_frame
        idx = df["A"].rename("foo")

        # setitem
        df["C"] = idx
        tm.assert_series_equal(df["C"], Series(idx, name="C"))

        df["D"] = "foo"
        df["D"] = idx
        tm.assert_series_equal(df["D"], Series(idx, name="D"))
        del df["D"]

        # With NaN: because uint64 has no NaN element,
        # the column should be cast to object.
        df2 = df.copy()
        df2.iloc[1, 1] = pd.NaT
        df2.iloc[1, 2] = pd.NaT
        result = df2["B"]
        tm.assert_series_equal(notna(result), Series([True, False, True], name="B"))
        tm.assert_series_equal(
            df2.dtypes,
            Series(
                [np.dtype("uint64"), np.dtype("O"), np.dtype("O")],
                index=["A", "B", "C"],
            ),
        )

    def test_set_reset(self):

        idx = Index([2 ** 63, 2 ** 63 + 5, 2 ** 63 + 10], name="foo")

        # set/reset
        df = DataFrame({"A": [0, 1, 2]}, index=idx)
        result = df.reset_index()
        assert result["foo"].dtype == np.dtype("uint64")

        df = result.set_index("foo")
        tm.assert_index_equal(df.index, idx)


def test_object_casting_indexing_wraps_datetimelike():
    # GH#31649, check the indexing methods all the way down the stack
    df = pd.DataFrame(
        {
            "A": [1, 2],
            "B": pd.date_range("2000", periods=2),
            "C": pd.timedelta_range("1 Day", periods=2),
        }
    )

    ser = df.loc[0]
    assert isinstance(ser.values[1], pd.Timestamp)
    assert isinstance(ser.values[2], pd.Timedelta)

    ser = df.iloc[0]
    assert isinstance(ser.values[1], pd.Timestamp)
    assert isinstance(ser.values[2], pd.Timedelta)

    ser = df.xs(0, axis=0)
    assert isinstance(ser.values[1], pd.Timestamp)
    assert isinstance(ser.values[2], pd.Timedelta)

    mgr = df._mgr
    mgr._rebuild_blknos_and_blklocs()
    arr = mgr.fast_xs(0)
    assert isinstance(arr[1], pd.Timestamp)
    assert isinstance(arr[2], pd.Timedelta)

    blk = mgr.blocks[mgr.blknos[1]]
    assert blk.dtype == "M8[ns]"  # we got the right block
    val = blk.iget((0, 0))
    assert isinstance(val, pd.Timestamp)

    blk = mgr.blocks[mgr.blknos[2]]
    assert blk.dtype == "m8[ns]"  # we got the right block
    val = blk.iget((0, 0))
    assert isinstance(val, pd.Timedelta)
