from collections import ChainMap
from datetime import datetime, timedelta
import inspect

import numpy as np
import pytest

from pandas.core.dtypes.common import (
    is_categorical_dtype,
    is_interval_dtype,
    is_object_dtype,
)

from pandas import (
    Categorical,
    DataFrame,
    DatetimeIndex,
    Index,
    IntervalIndex,
    MultiIndex,
    RangeIndex,
    Series,
    Timestamp,
    cut,
    date_range,
    to_datetime,
)
import pandas._testing as tm


class TestDataFrameAlterAxes:
    def test_set_index_directly(self, float_string_frame):
        df = float_string_frame
        idx = Index(np.arange(len(df))[::-1])

        df.index = idx
        tm.assert_index_equal(df.index, idx)
        with pytest.raises(ValueError, match="Length mismatch"):
            df.index = idx[::2]

    def test_set_index(self, float_string_frame):
        df = float_string_frame
        idx = Index(np.arange(len(df))[::-1])

        df = df.set_index(idx)
        tm.assert_index_equal(df.index, idx)
        with pytest.raises(ValueError, match="Length mismatch"):
            df.set_index(idx[::2])

    def test_set_index_cast(self):
        # issue casting an index then set_index
        df = DataFrame(
            {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2]}, index=[2010, 2011, 2012]
        )
        df2 = df.set_index(df.index.astype(np.int32))
        tm.assert_frame_equal(df, df2)

    # A has duplicate values, C does not
    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
    @pytest.mark.parametrize("inplace", [True, False])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_drop_inplace(self, frame_of_index_cols, drop, inplace, keys):
        df = frame_of_index_cols

        if isinstance(keys, list):
            idx = MultiIndex.from_arrays([df[x] for x in keys], names=keys)
        else:
            idx = Index(df[keys], name=keys)
        expected = df.drop(keys, axis=1) if drop else df
        expected.index = idx

        if inplace:
            result = df.copy()
            result.set_index(keys, drop=drop, inplace=True)
        else:
            result = df.set_index(keys, drop=drop)

        tm.assert_frame_equal(result, expected)

    # A has duplicate values, C does not
    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_append(self, frame_of_index_cols, drop, keys):
        df = frame_of_index_cols

        keys = keys if isinstance(keys, list) else [keys]
        idx = MultiIndex.from_arrays(
            [df.index] + [df[x] for x in keys], names=[None] + keys
        )
        expected = df.drop(keys, axis=1) if drop else df.copy()
        expected.index = idx

        result = df.set_index(keys, drop=drop, append=True)

        tm.assert_frame_equal(result, expected)

    # A has duplicate values, C does not
    @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_append_to_multiindex(self, frame_of_index_cols, drop, keys):
        # append to existing multiindex
        df = frame_of_index_cols.set_index(["D"], drop=drop, append=True)

        keys = keys if isinstance(keys, list) else [keys]
        expected = frame_of_index_cols.set_index(["D"] + keys, drop=drop, append=True)

        result = df.set_index(keys, drop=drop, append=True)

        tm.assert_frame_equal(result, expected)

    def test_set_index_after_mutation(self):
        # GH1590
        df = DataFrame({"val": [0, 1, 2], "key": ["a", "b", "c"]})
        expected = DataFrame({"val": [1, 2]}, Index(["b", "c"], name="key"))

        df2 = df.loc[df.index.map(lambda indx: indx >= 1)]
        result = df2.set_index("key")
        tm.assert_frame_equal(result, expected)

    # MultiIndex constructor does not work directly on Series -> lambda
    # Add list-of-list constructor because list is ambiguous -> lambda
    # also test index name if append=True (name is duplicate here for B)
    @pytest.mark.parametrize(
        "box",
        [
            Series,
            Index,
            np.array,
            list,
            lambda x: [list(x)],
            lambda x: MultiIndex.from_arrays([x]),
        ],
    )
    @pytest.mark.parametrize(
        "append, index_name", [(True, None), (True, "B"), (True, "test"), (False, None)]
    )
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_pass_single_array(
        self, frame_of_index_cols, drop, append, index_name, box
    ):
        df = frame_of_index_cols
        df.index.name = index_name

        key = box(df["B"])
        if box == list:
            # list of strings gets interpreted as list of keys
            msg = "['one', 'two', 'three', 'one', 'two']"
            with pytest.raises(KeyError, match=msg):
                df.set_index(key, drop=drop, append=append)
        else:
            # np.array/list-of-list "forget" the name of B
            name_mi = getattr(key, "names", None)
            name = [getattr(key, "name", None)] if name_mi is None else name_mi

            result = df.set_index(key, drop=drop, append=append)

            # only valid column keys are dropped
            # since B is always passed as array above, nothing is dropped
            expected = df.set_index(["B"], drop=False, append=append)
            expected.index.names = [index_name] + name if append else name

            tm.assert_frame_equal(result, expected)

    # MultiIndex constructor does not work directly on Series -> lambda
    # also test index name if append=True (name is duplicate here for A & B)
    @pytest.mark.parametrize(
        "box", [Series, Index, np.array, list, lambda x: MultiIndex.from_arrays([x])]
    )
    @pytest.mark.parametrize(
        "append, index_name",
        [(True, None), (True, "A"), (True, "B"), (True, "test"), (False, None)],
    )
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_pass_arrays(
        self, frame_of_index_cols, drop, append, index_name, box
    ):
        df = frame_of_index_cols
        df.index.name = index_name

        keys = ["A", box(df["B"])]
        # np.array/list "forget" the name of B
        names = ["A", None if box in [np.array, list, tuple, iter] else "B"]

        result = df.set_index(keys, drop=drop, append=append)

        # only valid column keys are dropped
        # since B is always passed as array above, only A is dropped, if at all
        expected = df.set_index(["A", "B"], drop=False, append=append)
        expected = expected.drop("A", axis=1) if drop else expected
        expected.index.names = [index_name] + names if append else names

        tm.assert_frame_equal(result, expected)

    # MultiIndex constructor does not work directly on Series -> lambda
    # We also emulate a "constructor" for the label -> lambda
    # also test index name if append=True (name is duplicate here for A)
    @pytest.mark.parametrize(
        "box2",
        [
            Series,
            Index,
            np.array,
            list,
            iter,
            lambda x: MultiIndex.from_arrays([x]),
            lambda x: x.name,
        ],
    )
    @pytest.mark.parametrize(
        "box1",
        [
            Series,
            Index,
            np.array,
            list,
            iter,
            lambda x: MultiIndex.from_arrays([x]),
            lambda x: x.name,
        ],
    )
    @pytest.mark.parametrize(
        "append, index_name", [(True, None), (True, "A"), (True, "test"), (False, None)]
    )
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_pass_arrays_duplicate(
        self, frame_of_index_cols, drop, append, index_name, box1, box2
    ):
        df = frame_of_index_cols
        df.index.name = index_name

        keys = [box1(df["A"]), box2(df["A"])]
        result = df.set_index(keys, drop=drop, append=append)

        # if either box is iter, it has been consumed; re-read
        keys = [box1(df["A"]), box2(df["A"])]

        # need to adapt first drop for case that both keys are 'A' --
        # cannot drop the same column twice;
        # use "is" because == would give ambiguous Boolean error for containers
        first_drop = (
            False if (keys[0] is "A" and keys[1] is "A") else drop  # noqa: F632
        )
        # to test against already-tested behaviour, we add sequentially,
        # hence second append always True; must wrap keys in list, otherwise
        # box = list would be interpreted as keys
        expected = df.set_index([keys[0]], drop=first_drop, append=append)
        expected = expected.set_index([keys[1]], drop=drop, append=True)
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("append", [True, False])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_pass_multiindex(self, frame_of_index_cols, drop, append):
        df = frame_of_index_cols
        keys = MultiIndex.from_arrays([df["A"], df["B"]], names=["A", "B"])

        result = df.set_index(keys, drop=drop, append=append)

        # setting with a MultiIndex will never drop columns
        expected = df.set_index(["A", "B"], drop=False, append=append)

        tm.assert_frame_equal(result, expected)

    def test_set_index_verify_integrity(self, frame_of_index_cols):
        df = frame_of_index_cols

        with pytest.raises(ValueError, match="Index has duplicate keys"):
            df.set_index("A", verify_integrity=True)
        # with MultiIndex
        with pytest.raises(ValueError, match="Index has duplicate keys"):
            df.set_index([df["A"], df["A"]], verify_integrity=True)

    @pytest.mark.parametrize("append", [True, False])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_raise_keys(self, frame_of_index_cols, drop, append):
        df = frame_of_index_cols

        with pytest.raises(KeyError, match="['foo', 'bar', 'baz']"):
            # column names are A-E, as well as one tuple
            df.set_index(["foo", "bar", "baz"], drop=drop, append=append)

        # non-existent key in list with arrays
        with pytest.raises(KeyError, match="X"):
            df.set_index([df["A"], df["B"], "X"], drop=drop, append=append)

        msg = "[('foo', 'foo', 'foo', 'bar', 'bar')]"
        # tuples always raise KeyError
        with pytest.raises(KeyError, match=msg):
            df.set_index(tuple(df["A"]), drop=drop, append=append)

        # also within a list
        with pytest.raises(KeyError, match=msg):
            df.set_index(["A", df["A"], tuple(df["A"])], drop=drop, append=append)

    @pytest.mark.parametrize("append", [True, False])
    @pytest.mark.parametrize("drop", [True, False])
    @pytest.mark.parametrize("box", [set], ids=["set"])
    def test_set_index_raise_on_type(self, frame_of_index_cols, box, drop, append):
        df = frame_of_index_cols

        msg = 'The parameter "keys" may be a column key, .*'
        # forbidden type, e.g. set
        with pytest.raises(TypeError, match=msg):
            df.set_index(box(df["A"]), drop=drop, append=append)

        # forbidden type in list, e.g. set
        with pytest.raises(TypeError, match=msg):
            df.set_index(["A", df["A"], box(df["A"])], drop=drop, append=append)

    # MultiIndex constructor does not work directly on Series -> lambda
    @pytest.mark.parametrize(
        "box",
        [Series, Index, np.array, iter, lambda x: MultiIndex.from_arrays([x])],
        ids=["Series", "Index", "np.array", "iter", "MultiIndex"],
    )
    @pytest.mark.parametrize("length", [4, 6], ids=["too_short", "too_long"])
    @pytest.mark.parametrize("append", [True, False])
    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_raise_on_len(
        self, frame_of_index_cols, box, length, drop, append
    ):
        # GH 24984
        df = frame_of_index_cols  # has length 5

        values = np.random.randint(0, 10, (length,))

        msg = "Length mismatch: Expected 5 rows, received array of length.*"

        # wrong length directly
        with pytest.raises(ValueError, match=msg):
            df.set_index(box(values), drop=drop, append=append)

        # wrong length in list
        with pytest.raises(ValueError, match=msg):
            df.set_index(["A", df.A, box(values)], drop=drop, append=append)

    def test_set_index_custom_label_type(self):
        # GH 24969

        class Thing:
            def __init__(self, name, color):
                self.name = name
                self.color = color

            def __str__(self) -> str:
                return f"<Thing {repr(self.name)}>"

            # necessary for pretty KeyError
            __repr__ = __str__

        thing1 = Thing("One", "red")
        thing2 = Thing("Two", "blue")
        df = DataFrame({thing1: [0, 1], thing2: [2, 3]})
        expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))

        # use custom label directly
        result = df.set_index(thing2)
        tm.assert_frame_equal(result, expected)

        # custom label wrapped in list
        result = df.set_index([thing2])
        tm.assert_frame_equal(result, expected)

        # missing key
        thing3 = Thing("Three", "pink")
        msg = "<Thing 'Three'>"
        with pytest.raises(KeyError, match=msg):
            # missing label directly
            df.set_index(thing3)

        with pytest.raises(KeyError, match=msg):
            # missing label in list
            df.set_index([thing3])

    def test_set_index_custom_label_hashable_iterable(self):
        # GH 24969

        # actual example discussed in GH 24984 was e.g. for shapely.geometry
        # objects (e.g. a collection of Points) that can be both hashable and
        # iterable; using frozenset as a stand-in for testing here

        class Thing(frozenset):
            # need to stabilize repr for KeyError (due to random order in sets)
            def __repr__(self) -> str:
                tmp = sorted(self)
                # double curly brace prints one brace in format string
                return "frozenset({{{}}})".format(", ".join(map(repr, tmp)))

        thing1 = Thing(["One", "red"])
        thing2 = Thing(["Two", "blue"])
        df = DataFrame({thing1: [0, 1], thing2: [2, 3]})
        expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))

        # use custom label directly
        result = df.set_index(thing2)
        tm.assert_frame_equal(result, expected)

        # custom label wrapped in list
        result = df.set_index([thing2])
        tm.assert_frame_equal(result, expected)

        # missing key
        thing3 = Thing(["Three", "pink"])
        msg = r"frozenset\(\{'Three', 'pink'\}\)"
        with pytest.raises(KeyError, match=msg):
            # missing label directly
            df.set_index(thing3)

        with pytest.raises(KeyError, match=msg):
            # missing label in list
            df.set_index([thing3])

    def test_set_index_custom_label_type_raises(self):
        # GH 24969

        # purposefully inherit from something unhashable
        class Thing(set):
            def __init__(self, name, color):
                self.name = name
                self.color = color

            def __str__(self) -> str:
                return f"<Thing {repr(self.name)}>"

        thing1 = Thing("One", "red")
        thing2 = Thing("Two", "blue")
        df = DataFrame([[0, 2], [1, 3]], columns=[thing1, thing2])

        msg = 'The parameter "keys" may be a column key, .*'

        with pytest.raises(TypeError, match=msg):
            # use custom label directly
            df.set_index(thing2)

        with pytest.raises(TypeError, match=msg):
            # custom label wrapped in list
            df.set_index([thing2])

    def test_construction_with_categorical_index(self):
        ci = tm.makeCategoricalIndex(10)
        ci.name = "B"

        # with Categorical
        df = DataFrame({"A": np.random.randn(10), "B": ci.values})
        idf = df.set_index("B")
        tm.assert_index_equal(idf.index, ci)

        # from a CategoricalIndex
        df = DataFrame({"A": np.random.randn(10), "B": ci})
        idf = df.set_index("B")
        tm.assert_index_equal(idf.index, ci)

        # round-trip
        idf = idf.reset_index().set_index("B")
        tm.assert_index_equal(idf.index, ci)

    def test_set_index_cast_datetimeindex(self):
        df = DataFrame(
            {
                "A": [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)],
                "B": np.random.randn(1000),
            }
        )

        idf = df.set_index("A")
        assert isinstance(idf.index, DatetimeIndex)

    def test_convert_dti_to_series(self):
        # don't cast a DatetimeIndex WITH a tz, leave as object
        # GH 6032
        idx = DatetimeIndex(
            to_datetime(["2013-1-1 13:00", "2013-1-2 14:00"]), name="B"
        ).tz_localize("US/Pacific")
        df = DataFrame(np.random.randn(2, 1), columns=["A"])

        expected = Series(
            np.array(
                [
                    Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
                    Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
                ],
                dtype="object",
            ),
            name="B",
        )

        # convert index to series
        result = Series(idx)
        tm.assert_series_equal(result, expected)

        # assign to frame
        df["B"] = idx
        result = df["B"]
        tm.assert_series_equal(result, expected)

        # convert to series while keeping the timezone
        msg = "stop passing 'keep_tz'"
        with tm.assert_produces_warning(FutureWarning) as m:
            result = idx.to_series(keep_tz=True, index=[0, 1])
        tm.assert_series_equal(result, expected)
        assert msg in str(m[0].message)

        # convert to utc
        with tm.assert_produces_warning(FutureWarning) as m:
            df["B"] = idx.to_series(keep_tz=False, index=[0, 1])
        result = df["B"]
        comp = Series(DatetimeIndex(expected.values).tz_localize(None), name="B")
        tm.assert_series_equal(result, comp)
        msg = "do 'idx.tz_convert(None)' before calling"
        assert msg in str(m[0].message)

        result = idx.to_series(index=[0, 1])
        tm.assert_series_equal(result, expected)

        with tm.assert_produces_warning(FutureWarning) as m:
            result = idx.to_series(keep_tz=False, index=[0, 1])
        tm.assert_series_equal(result, expected.dt.tz_convert(None))
        msg = "do 'idx.tz_convert(None)' before calling"
        assert msg in str(m[0].message)

        # list of datetimes with a tz
        df["B"] = idx.to_pydatetime()
        result = df["B"]
        tm.assert_series_equal(result, expected)

        # GH 6785
        # set the index manually
        import pytz

        df = DataFrame([{"ts": datetime(2014, 4, 1, tzinfo=pytz.utc), "foo": 1}])
        expected = df.set_index("ts")
        df.index = df["ts"]
        df.pop("ts")
        tm.assert_frame_equal(df, expected)

    def test_reset_index_tz(self, tz_aware_fixture):
        # GH 3950
        # reset_index with single level
        tz = tz_aware_fixture
        idx = date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx")
        df = DataFrame({"a": range(5), "b": ["A", "B", "C", "D", "E"]}, index=idx)

        expected = DataFrame(
            {
                "idx": [
                    datetime(2011, 1, 1),
                    datetime(2011, 1, 2),
                    datetime(2011, 1, 3),
                    datetime(2011, 1, 4),
                    datetime(2011, 1, 5),
                ],
                "a": range(5),
                "b": ["A", "B", "C", "D", "E"],
            },
            columns=["idx", "a", "b"],
        )
        expected["idx"] = expected["idx"].apply(lambda d: Timestamp(d, tz=tz))
        tm.assert_frame_equal(df.reset_index(), expected)

    def test_set_index_timezone(self):
        # GH 12358
        # tz-aware Series should retain the tz
        idx = to_datetime(["2014-01-01 10:10:10"], utc=True).tz_convert("Europe/Rome")
        df = DataFrame({"A": idx})
        assert df.set_index(idx).index[0].hour == 11
        assert DatetimeIndex(Series(df.A))[0].hour == 11
        assert df.set_index(df.A).index[0].hour == 11

    def test_set_index_dst(self):
        di = date_range("2006-10-29 00:00:00", periods=3, freq="H", tz="US/Pacific")

        df = DataFrame(data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=di).reset_index()
        # single level
        res = df.set_index("index")
        exp = DataFrame(
            data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=Index(di, name="index")
        )
        tm.assert_frame_equal(res, exp)

        # GH 12920
        res = df.set_index(["index", "a"])
        exp_index = MultiIndex.from_arrays([di, [0, 1, 2]], names=["index", "a"])
        exp = DataFrame({"b": [3, 4, 5]}, index=exp_index)
        tm.assert_frame_equal(res, exp)

    def test_reset_index_with_intervals(self):
        idx = IntervalIndex.from_breaks(np.arange(11), name="x")
        original = DataFrame({"x": idx, "y": np.arange(10)})[["x", "y"]]

        result = original.set_index("x")
        expected = DataFrame({"y": np.arange(10)}, index=idx)
        tm.assert_frame_equal(result, expected)

        result2 = result.reset_index()
        tm.assert_frame_equal(result2, original)

    def test_set_index_multiindexcolumns(self):
        columns = MultiIndex.from_tuples([("foo", 1), ("foo", 2), ("bar", 1)])
        df = DataFrame(np.random.randn(3, 3), columns=columns)
        result = df.set_index(df.columns[0])
        expected = df.iloc[:, 1:]
        expected.index = df.iloc[:, 0].values
        expected.index.names = [df.columns[0]]
        tm.assert_frame_equal(result, expected)

    def test_set_index_empty_column(self):
        # GH 1971
        df = DataFrame(
            [
                {"a": 1, "p": 0},
                {"a": 2, "m": 10},
                {"a": 3, "m": 11, "p": 20},
                {"a": 4, "m": 12, "p": 21},
            ],
            columns=("a", "m", "p", "x"),
        )

        result = df.set_index(["a", "x"])
        expected = df[["m", "p"]]
        expected.index = MultiIndex.from_arrays([df["a"], df["x"]], names=["a", "x"])
        tm.assert_frame_equal(result, expected)

    def test_set_columns(self, float_string_frame):
        cols = Index(np.arange(len(float_string_frame.columns)))
        float_string_frame.columns = cols
        with pytest.raises(ValueError, match="Length mismatch"):
            float_string_frame.columns = cols[::2]

    def test_dti_set_index_reindex(self):
        # GH 6631
        df = DataFrame(np.random.random(6))
        idx1 = date_range("2011/01/01", periods=6, freq="M", tz="US/Eastern")
        idx2 = date_range("2013", periods=6, freq="A", tz="Asia/Tokyo")

        df = df.set_index(idx1)
        tm.assert_index_equal(df.index, idx1)
        df = df.reindex(idx2)
        tm.assert_index_equal(df.index, idx2)

        # GH 11314
        # with tz
        index = date_range(
            datetime(2015, 10, 1), datetime(2015, 10, 1, 23), freq="H", tz="US/Eastern"
        )
        df = DataFrame(np.random.randn(24, 1), columns=["a"], index=index)
        new_index = date_range(
            datetime(2015, 10, 2), datetime(2015, 10, 2, 23), freq="H", tz="US/Eastern"
        )

        result = df.set_index(new_index)
        assert result.index.freq == index.freq

    # Renaming

    def test_rename(self, float_frame):
        mapping = {"A": "a", "B": "b", "C": "c", "D": "d"}

        renamed = float_frame.rename(columns=mapping)
        renamed2 = float_frame.rename(columns=str.lower)

        tm.assert_frame_equal(renamed, renamed2)
        tm.assert_frame_equal(
            renamed2.rename(columns=str.upper), float_frame, check_names=False
        )

        # index
        data = {"A": {"foo": 0, "bar": 1}}

        # gets sorted alphabetical
        df = DataFrame(data)
        renamed = df.rename(index={"foo": "bar", "bar": "foo"})
        tm.assert_index_equal(renamed.index, Index(["foo", "bar"]))

        renamed = df.rename(index=str.upper)
        tm.assert_index_equal(renamed.index, Index(["BAR", "FOO"]))

        # have to pass something
        with pytest.raises(TypeError, match="must pass an index to rename"):
            float_frame.rename()

        # partial columns
        renamed = float_frame.rename(columns={"C": "foo", "D": "bar"})
        tm.assert_index_equal(renamed.columns, Index(["A", "B", "foo", "bar"]))

        # other axis
        renamed = float_frame.T.rename(index={"C": "foo", "D": "bar"})
        tm.assert_index_equal(renamed.index, Index(["A", "B", "foo", "bar"]))

        # index with name
        index = Index(["foo", "bar"], name="name")
        renamer = DataFrame(data, index=index)
        renamed = renamer.rename(index={"foo": "bar", "bar": "foo"})
        tm.assert_index_equal(renamed.index, Index(["bar", "foo"], name="name"))
        assert renamed.index.name == renamer.index.name

    @pytest.mark.parametrize(
        "args,kwargs",
        [
            ((ChainMap({"A": "a"}, {"B": "b"}),), dict(axis="columns")),
            ((), dict(columns=ChainMap({"A": "a"}, {"B": "b"}))),
        ],
    )
    def test_rename_chainmap(self, args, kwargs):
        # see gh-23859
        colAData = range(1, 11)
        colBdata = np.random.randn(10)

        df = DataFrame({"A": colAData, "B": colBdata})
        result = df.rename(*args, **kwargs)

        expected = DataFrame({"a": colAData, "b": colBdata})
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_inplace(self, float_frame):
        # GH 15704
        expected = float_frame.rename_axis("foo")
        result = float_frame.copy()
        no_return = result.rename_axis("foo", inplace=True)

        assert no_return is None
        tm.assert_frame_equal(result, expected)

        expected = float_frame.rename_axis("bar", axis=1)
        result = float_frame.copy()
        no_return = result.rename_axis("bar", axis=1, inplace=True)

        assert no_return is None
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_raises(self):
        # https://github.com/pandas-dev/pandas/issues/17833
        df = DataFrame({"A": [1, 2], "B": [1, 2]})
        with pytest.raises(ValueError, match="Use `.rename`"):
            df.rename_axis(id, axis=0)

        with pytest.raises(ValueError, match="Use `.rename`"):
            df.rename_axis({0: 10, 1: 20}, axis=0)

        with pytest.raises(ValueError, match="Use `.rename`"):
            df.rename_axis(id, axis=1)

        with pytest.raises(ValueError, match="Use `.rename`"):
            df["A"].rename_axis(id)

    def test_rename_axis_mapper(self):
        # GH 19978
        mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])
        df = DataFrame(
            {"x": list(range(len(mi))), "y": [i * 10 for i in range(len(mi))]}, index=mi
        )

        # Test for rename of the Index object of columns
        result = df.rename_axis("cols", axis=1)
        tm.assert_index_equal(result.columns, Index(["x", "y"], name="cols"))

        # Test for rename of the Index object of columns using dict
        result = result.rename_axis(columns={"cols": "new"}, axis=1)
        tm.assert_index_equal(result.columns, Index(["x", "y"], name="new"))

        # Test for renaming index using dict
        result = df.rename_axis(index={"ll": "foo"})
        assert result.index.names == ["foo", "nn"]

        # Test for renaming index using a function
        result = df.rename_axis(index=str.upper, axis=0)
        assert result.index.names == ["LL", "NN"]

        # Test for renaming index providing complete list
        result = df.rename_axis(index=["foo", "goo"])
        assert result.index.names == ["foo", "goo"]

        # Test for changing index and columns at same time
        sdf = df.reset_index().set_index("nn").drop(columns=["ll", "y"])
        result = sdf.rename_axis(index="foo", columns="meh")
        assert result.index.name == "foo"
        assert result.columns.name == "meh"

        # Test different error cases
        with pytest.raises(TypeError, match="Must pass"):
            df.rename_axis(index="wrong")

        with pytest.raises(ValueError, match="Length of names"):
            df.rename_axis(index=["wrong"])

        with pytest.raises(TypeError, match="bogus"):
            df.rename_axis(bogus=None)

    @pytest.mark.parametrize(
        "kwargs, rename_index, rename_columns",
        [
            ({"mapper": None, "axis": 0}, True, False),
            ({"mapper": None, "axis": 1}, False, True),
            ({"index": None}, True, False),
            ({"columns": None}, False, True),
            ({"index": None, "columns": None}, True, True),
            ({}, False, False),
        ],
    )
    def test_rename_axis_none(self, kwargs, rename_index, rename_columns):
        # GH 25034
        index = Index(list("abc"), name="foo")
        columns = Index(["col1", "col2"], name="bar")
        data = np.arange(6).reshape(3, 2)
        df = DataFrame(data, index, columns)

        result = df.rename_axis(**kwargs)
        expected_index = index.rename(None) if rename_index else index
        expected_columns = columns.rename(None) if rename_columns else columns
        expected = DataFrame(data, expected_index, expected_columns)
        tm.assert_frame_equal(result, expected)

    def test_rename_multiindex(self):

        tuples_index = [("foo1", "bar1"), ("foo2", "bar2")]
        tuples_columns = [("fizz1", "buzz1"), ("fizz2", "buzz2")]
        index = MultiIndex.from_tuples(tuples_index, names=["foo", "bar"])
        columns = MultiIndex.from_tuples(tuples_columns, names=["fizz", "buzz"])
        df = DataFrame([(0, 0), (1, 1)], index=index, columns=columns)

        #
        # without specifying level -> across all levels

        renamed = df.rename(
            index={"foo1": "foo3", "bar2": "bar3"},
            columns={"fizz1": "fizz3", "buzz2": "buzz3"},
        )
        new_index = MultiIndex.from_tuples(
            [("foo3", "bar1"), ("foo2", "bar3")], names=["foo", "bar"]
        )
        new_columns = MultiIndex.from_tuples(
            [("fizz3", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]
        )
        tm.assert_index_equal(renamed.index, new_index)
        tm.assert_index_equal(renamed.columns, new_columns)
        assert renamed.index.names == df.index.names
        assert renamed.columns.names == df.columns.names

        #
        # with specifying a level (GH13766)

        # dict
        new_columns = MultiIndex.from_tuples(
            [("fizz3", "buzz1"), ("fizz2", "buzz2")], names=["fizz", "buzz"]
        )
        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=0)
        tm.assert_index_equal(renamed.columns, new_columns)
        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="fizz")
        tm.assert_index_equal(renamed.columns, new_columns)

        new_columns = MultiIndex.from_tuples(
            [("fizz1", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]
        )
        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=1)
        tm.assert_index_equal(renamed.columns, new_columns)
        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="buzz")
        tm.assert_index_equal(renamed.columns, new_columns)

        # function
        func = str.upper
        new_columns = MultiIndex.from_tuples(
            [("FIZZ1", "buzz1"), ("FIZZ2", "buzz2")], names=["fizz", "buzz"]
        )
        renamed = df.rename(columns=func, level=0)
        tm.assert_index_equal(renamed.columns, new_columns)
        renamed = df.rename(columns=func, level="fizz")
        tm.assert_index_equal(renamed.columns, new_columns)

        new_columns = MultiIndex.from_tuples(
            [("fizz1", "BUZZ1"), ("fizz2", "BUZZ2")], names=["fizz", "buzz"]
        )
        renamed = df.rename(columns=func, level=1)
        tm.assert_index_equal(renamed.columns, new_columns)
        renamed = df.rename(columns=func, level="buzz")
        tm.assert_index_equal(renamed.columns, new_columns)

        # index
        new_index = MultiIndex.from_tuples(
            [("foo3", "bar1"), ("foo2", "bar2")], names=["foo", "bar"]
        )
        renamed = df.rename(index={"foo1": "foo3", "bar2": "bar3"}, level=0)
        tm.assert_index_equal(renamed.index, new_index)

    def test_rename_nocopy(self, float_frame):
        renamed = float_frame.rename(columns={"C": "foo"}, copy=False)
        renamed["foo"] = 1.0
        assert (float_frame["C"] == 1.0).all()

    def test_rename_inplace(self, float_frame):
        float_frame.rename(columns={"C": "foo"})
        assert "C" in float_frame
        assert "foo" not in float_frame

        c_id = id(float_frame["C"])
        float_frame = float_frame.copy()
        float_frame.rename(columns={"C": "foo"}, inplace=True)

        assert "C" not in float_frame
        assert "foo" in float_frame
        assert id(float_frame["foo"]) != c_id

    def test_rename_bug(self):
        # GH 5344
        # rename set ref_locs, and set_index was not resetting
        df = DataFrame({0: ["foo", "bar"], 1: ["bah", "bas"], 2: [1, 2]})
        df = df.rename(columns={0: "a"})
        df = df.rename(columns={1: "b"})
        df = df.set_index(["a", "b"])
        df.columns = ["2001-01-01"]
        expected = DataFrame(
            [[1], [2]],
            index=MultiIndex.from_tuples(
                [("foo", "bah"), ("bar", "bas")], names=["a", "b"]
            ),
            columns=["2001-01-01"],
        )
        tm.assert_frame_equal(df, expected)

    def test_rename_bug2(self):
        # GH 19497
        # rename was changing Index to MultiIndex if Index contained tuples

        df = DataFrame(data=np.arange(3), index=[(0, 0), (1, 1), (2, 2)], columns=["a"])
        df = df.rename({(1, 1): (5, 4)}, axis="index")
        expected = DataFrame(
            data=np.arange(3), index=[(0, 0), (5, 4), (2, 2)], columns=["a"]
        )
        tm.assert_frame_equal(df, expected)

    def test_rename_errors_raises(self):
        df = DataFrame(columns=["A", "B", "C", "D"])
        with pytest.raises(KeyError, match="'E'] not found in axis"):
            df.rename(columns={"A": "a", "E": "e"}, errors="raise")

    @pytest.mark.parametrize(
        "mapper, errors, expected_columns",
        [
            ({"A": "a", "E": "e"}, "ignore", ["a", "B", "C", "D"]),
            ({"A": "a"}, "raise", ["a", "B", "C", "D"]),
            (str.lower, "raise", ["a", "b", "c", "d"]),
        ],
    )
    def test_rename_errors(self, mapper, errors, expected_columns):
        # GH 13473
        # rename now works with errors parameter
        df = DataFrame(columns=["A", "B", "C", "D"])
        result = df.rename(columns=mapper, errors=errors)
        expected = DataFrame(columns=expected_columns)
        tm.assert_frame_equal(result, expected)

    def test_reorder_levels(self):
        index = MultiIndex(
            levels=[["bar"], ["one", "two", "three"], [0, 1]],
            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
            names=["L0", "L1", "L2"],
        )
        df = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=index)

        # no change, position
        result = df.reorder_levels([0, 1, 2])
        tm.assert_frame_equal(df, result)

        # no change, labels
        result = df.reorder_levels(["L0", "L1", "L2"])
        tm.assert_frame_equal(df, result)

        # rotate, position
        result = df.reorder_levels([1, 2, 0])
        e_idx = MultiIndex(
            levels=[["one", "two", "three"], [0, 1], ["bar"]],
            codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0]],
            names=["L1", "L2", "L0"],
        )
        expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)
        tm.assert_frame_equal(result, expected)

        result = df.reorder_levels([0, 0, 0])
        e_idx = MultiIndex(
            levels=[["bar"], ["bar"], ["bar"]],
            codes=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]],
            names=["L0", "L0", "L0"],
        )
        expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)
        tm.assert_frame_equal(result, expected)

        result = df.reorder_levels(["L0", "L0", "L0"])
        tm.assert_frame_equal(result, expected)

    def test_reset_index(self, float_frame):
        stacked = float_frame.stack()[::2]
        stacked = DataFrame({"foo": stacked, "bar": stacked})

        names = ["first", "second"]
        stacked.index.names = names
        deleveled = stacked.reset_index()
        for i, (lev, level_codes) in enumerate(
            zip(stacked.index.levels, stacked.index.codes)
        ):
            values = lev.take(level_codes)
            name = names[i]
            tm.assert_index_equal(values, Index(deleveled[name]))

        stacked.index.names = [None, None]
        deleveled2 = stacked.reset_index()
        tm.assert_series_equal(
            deleveled["first"], deleveled2["level_0"], check_names=False
        )
        tm.assert_series_equal(
            deleveled["second"], deleveled2["level_1"], check_names=False
        )

        # default name assigned
        rdf = float_frame.reset_index()
        exp = Series(float_frame.index.values, name="index")
        tm.assert_series_equal(rdf["index"], exp)

        # default name assigned, corner case
        df = float_frame.copy()
        df["index"] = "foo"
        rdf = df.reset_index()
        exp = Series(float_frame.index.values, name="level_0")
        tm.assert_series_equal(rdf["level_0"], exp)

        # but this is ok
        float_frame.index.name = "index"
        deleveled = float_frame.reset_index()
        tm.assert_series_equal(deleveled["index"], Series(float_frame.index))
        tm.assert_index_equal(deleveled.index, Index(np.arange(len(deleveled))))

        # preserve column names
        float_frame.columns.name = "columns"
        resetted = float_frame.reset_index()
        assert resetted.columns.name == "columns"

        # only remove certain columns
        df = float_frame.reset_index().set_index(["index", "A", "B"])
        rs = df.reset_index(["A", "B"])

        # TODO should reset_index check_names ?
        tm.assert_frame_equal(rs, float_frame, check_names=False)

        rs = df.reset_index(["index", "A", "B"])
        tm.assert_frame_equal(rs, float_frame.reset_index(), check_names=False)

        rs = df.reset_index(["index", "A", "B"])
        tm.assert_frame_equal(rs, float_frame.reset_index(), check_names=False)

        rs = df.reset_index("A")
        xp = float_frame.reset_index().set_index(["index", "B"])
        tm.assert_frame_equal(rs, xp, check_names=False)

        # test resetting in place
        df = float_frame.copy()
        resetted = float_frame.reset_index()
        df.reset_index(inplace=True)
        tm.assert_frame_equal(df, resetted, check_names=False)

        df = float_frame.reset_index().set_index(["index", "A", "B"])
        rs = df.reset_index("A", drop=True)
        xp = float_frame.copy()
        del xp["A"]
        xp = xp.set_index(["B"], append=True)
        tm.assert_frame_equal(rs, xp, check_names=False)

    def test_reset_index_name(self):
        df = DataFrame(
            [[1, 2, 3, 4], [5, 6, 7, 8]],
            columns=["A", "B", "C", "D"],
            index=Index(range(2), name="x"),
        )
        assert df.reset_index().index.name is None
        assert df.reset_index(drop=True).index.name is None
        df.reset_index(inplace=True)
        assert df.index.name is None

    def test_reset_index_level(self):
        df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "C", "D"])

        for levels in ["A", "B"], [0, 1]:
            # With MultiIndex
            result = df.set_index(["A", "B"]).reset_index(level=levels[0])
            tm.assert_frame_equal(result, df.set_index("B"))

            result = df.set_index(["A", "B"]).reset_index(level=levels[:1])
            tm.assert_frame_equal(result, df.set_index("B"))

            result = df.set_index(["A", "B"]).reset_index(level=levels)
            tm.assert_frame_equal(result, df)

            result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True)
            tm.assert_frame_equal(result, df[["C", "D"]])

            # With single-level Index (GH 16263)
            result = df.set_index("A").reset_index(level=levels[0])
            tm.assert_frame_equal(result, df)

            result = df.set_index("A").reset_index(level=levels[:1])
            tm.assert_frame_equal(result, df)

            result = df.set_index(["A"]).reset_index(level=levels[0], drop=True)
            tm.assert_frame_equal(result, df[["B", "C", "D"]])

        # Missing levels - for both MultiIndex and single-level Index:
        for idx_lev in ["A", "B"], ["A"]:
            with pytest.raises(KeyError, match=r"(L|l)evel \(?E\)?"):
                df.set_index(idx_lev).reset_index(level=["A", "E"])
            with pytest.raises(IndexError, match="Too many levels"):
                df.set_index(idx_lev).reset_index(level=[0, 1, 2])

    def test_reset_index_right_dtype(self):
        time = np.arange(0.0, 10, np.sqrt(2) / 2)
        s1 = Series(
            (9.81 * time ** 2) / 2, index=Index(time, name="time"), name="speed"
        )
        df = DataFrame(s1)

        resetted = s1.reset_index()
        assert resetted["time"].dtype == np.float64

        resetted = df.reset_index()
        assert resetted["time"].dtype == np.float64

    def test_reset_index_multiindex_col(self):
        vals = np.random.randn(3, 3).astype(object)
        idx = ["x", "y", "z"]
        full = np.hstack(([[x] for x in idx], vals))
        df = DataFrame(
            vals,
            Index(idx, name="a"),
            columns=[["b", "b", "c"], ["mean", "median", "mean"]],
        )
        rs = df.reset_index()
        xp = DataFrame(
            full, columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]]
        )
        tm.assert_frame_equal(rs, xp)

        rs = df.reset_index(col_fill=None)
        xp = DataFrame(
            full, columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]]
        )
        tm.assert_frame_equal(rs, xp)

        rs = df.reset_index(col_level=1, col_fill="blah")
        xp = DataFrame(
            full, columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]]
        )
        tm.assert_frame_equal(rs, xp)

        df = DataFrame(
            vals,
            MultiIndex.from_arrays([[0, 1, 2], ["x", "y", "z"]], names=["d", "a"]),
            columns=[["b", "b", "c"], ["mean", "median", "mean"]],
        )
        rs = df.reset_index("a")
        xp = DataFrame(
            full,
            Index([0, 1, 2], name="d"),
            columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]],
        )
        tm.assert_frame_equal(rs, xp)

        rs = df.reset_index("a", col_fill=None)
        xp = DataFrame(
            full,
            Index(range(3), name="d"),
            columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]],
        )
        tm.assert_frame_equal(rs, xp)

        rs = df.reset_index("a", col_fill="blah", col_level=1)
        xp = DataFrame(
            full,
            Index(range(3), name="d"),
            columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]],
        )
        tm.assert_frame_equal(rs, xp)

    def test_reset_index_multiindex_nan(self):
        # GH6322, testing reset_index on MultiIndexes
        # when we have a nan or all nan
        df = DataFrame(
            {"A": ["a", "b", "c"], "B": [0, 1, np.nan], "C": np.random.rand(3)}
        )
        rs = df.set_index(["A", "B"]).reset_index()
        tm.assert_frame_equal(rs, df)

        df = DataFrame(
            {"A": [np.nan, "b", "c"], "B": [0, 1, 2], "C": np.random.rand(3)}
        )
        rs = df.set_index(["A", "B"]).reset_index()
        tm.assert_frame_equal(rs, df)

        df = DataFrame({"A": ["a", "b", "c"], "B": [0, 1, 2], "C": [np.nan, 1.1, 2.2]})
        rs = df.set_index(["A", "B"]).reset_index()
        tm.assert_frame_equal(rs, df)

        df = DataFrame(
            {
                "A": ["a", "b", "c"],
                "B": [np.nan, np.nan, np.nan],
                "C": np.random.rand(3),
            }
        )
        rs = df.set_index(["A", "B"]).reset_index()
        tm.assert_frame_equal(rs, df)

    def test_reset_index_with_datetimeindex_cols(self):
        # GH5818
        #
        df = DataFrame(
            [[1, 2], [3, 4]],
            columns=date_range("1/1/2013", "1/2/2013"),
            index=["A", "B"],
        )

        result = df.reset_index()
        expected = DataFrame(
            [["A", 1, 2], ["B", 3, 4]],
            columns=["index", datetime(2013, 1, 1), datetime(2013, 1, 2)],
        )
        tm.assert_frame_equal(result, expected)

    def test_reset_index_range(self):
        # GH 12071
        df = DataFrame([[0, 0], [1, 1]], columns=["A", "B"], index=RangeIndex(stop=2))
        result = df.reset_index()
        assert isinstance(result.index, RangeIndex)
        expected = DataFrame(
            [[0, 0, 0], [1, 1, 1]],
            columns=["index", "A", "B"],
            index=RangeIndex(stop=2),
        )
        tm.assert_frame_equal(result, expected)

    def test_set_index_names(self):
        df = tm.makeDataFrame()
        df.index.name = "name"

        assert df.set_index(df.index).index.names == ["name"]

        mi = MultiIndex.from_arrays(df[["A", "B"]].T.values, names=["A", "B"])
        mi2 = MultiIndex.from_arrays(
            df[["A", "B", "A", "B"]].T.values, names=["A", "B", "C", "D"]
        )

        df = df.set_index(["A", "B"])

        assert df.set_index(df.index).index.names == ["A", "B"]

        # Check that set_index isn't converting a MultiIndex into an Index
        assert isinstance(df.set_index(df.index).index, MultiIndex)

        # Check actual equality
        tm.assert_index_equal(df.set_index(df.index).index, mi)

        idx2 = df.index.rename(["C", "D"])

        # Check that [MultiIndex, MultiIndex] yields a MultiIndex rather
        # than a pair of tuples
        assert isinstance(df.set_index([df.index, idx2]).index, MultiIndex)

        # Check equality
        tm.assert_index_equal(df.set_index([df.index, idx2]).index, mi2)

    def test_rename_objects(self, float_string_frame):
        renamed = float_string_frame.rename(columns=str.upper)

        assert "FOO" in renamed
        assert "foo" not in renamed

    def test_rename_axis_style(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["X", "Y"])
        expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])

        result = df.rename(str.lower, axis=1)
        tm.assert_frame_equal(result, expected)

        result = df.rename(str.lower, axis="columns")
        tm.assert_frame_equal(result, expected)

        result = df.rename({"A": "a", "B": "b"}, axis=1)
        tm.assert_frame_equal(result, expected)

        result = df.rename({"A": "a", "B": "b"}, axis="columns")
        tm.assert_frame_equal(result, expected)

        # Index
        expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])
        result = df.rename(str.lower, axis=0)
        tm.assert_frame_equal(result, expected)

        result = df.rename(str.lower, axis="index")
        tm.assert_frame_equal(result, expected)

        result = df.rename({"X": "x", "Y": "y"}, axis=0)
        tm.assert_frame_equal(result, expected)

        result = df.rename({"X": "x", "Y": "y"}, axis="index")
        tm.assert_frame_equal(result, expected)

        result = df.rename(mapper=str.lower, axis="index")
        tm.assert_frame_equal(result, expected)

    def test_rename_mapper_multi(self):
        df = DataFrame({"A": ["a", "b"], "B": ["c", "d"], "C": [1, 2]}).set_index(
            ["A", "B"]
        )
        result = df.rename(str.upper)
        expected = df.rename(index=str.upper)
        tm.assert_frame_equal(result, expected)

    def test_rename_positional_named(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])
        result = df.rename(index=str.lower, columns=str.upper)
        expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_style_raises(self):
        # see gh-12392
        df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["0", "1"])

        # Named target and axis
        over_spec_msg = "Cannot specify both 'axis' and any of 'index' or 'columns'"
        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(index=str.lower, axis=1)

        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(index=str.lower, axis="columns")

        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(columns=str.lower, axis="columns")

        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(index=str.lower, axis=0)

        # Multiple targets and axis
        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(str.lower, index=str.lower, axis="columns")

        # Too many targets
        over_spec_msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'"
        with pytest.raises(TypeError, match=over_spec_msg):
            df.rename(str.lower, index=str.lower, columns=str.lower)

        # Duplicates
        with pytest.raises(TypeError, match="multiple values"):
            df.rename(id, mapper=id)

    def test_reindex_api_equivalence(self):
        # equivalence of the labels/axis and index/columns API's
        df = DataFrame(
            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
            index=["a", "b", "c"],
            columns=["d", "e", "f"],
        )

        res1 = df.reindex(["b", "a"])
        res2 = df.reindex(index=["b", "a"])
        res3 = df.reindex(labels=["b", "a"])
        res4 = df.reindex(labels=["b", "a"], axis=0)
        res5 = df.reindex(["b", "a"], axis=0)
        for res in [res2, res3, res4, res5]:
            tm.assert_frame_equal(res1, res)

        res1 = df.reindex(columns=["e", "d"])
        res2 = df.reindex(["e", "d"], axis=1)
        res3 = df.reindex(labels=["e", "d"], axis=1)
        for res in [res2, res3]:
            tm.assert_frame_equal(res1, res)

        res1 = df.reindex(index=["b", "a"], columns=["e", "d"])
        res2 = df.reindex(columns=["e", "d"], index=["b", "a"])
        res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1)
        for res in [res2, res3]:
            tm.assert_frame_equal(res1, res)

    def test_rename_positional_raises(self):
        # GH 29136
        df = DataFrame(columns=["A", "B"])
        msg = r"rename\(\) takes from 1 to 2 positional arguments"

        with pytest.raises(TypeError, match=msg):
            df.rename(None, str.lower)

    def test_rename_no_mappings_raises(self):
        # GH 29136
        df = DataFrame([[1]])
        msg = "must pass an index to rename"
        with pytest.raises(TypeError, match=msg):
            df.rename()

        with pytest.raises(TypeError, match=msg):
            df.rename(None, index=None)

        with pytest.raises(TypeError, match=msg):
            df.rename(None, columns=None)

        with pytest.raises(TypeError, match=msg):
            df.rename(None, columns=None, index=None)

    def test_rename_mapper_and_positional_arguments_raises(self):
        # GH 29136
        df = DataFrame([[1]])
        msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'"
        with pytest.raises(TypeError, match=msg):
            df.rename({}, index={})

        with pytest.raises(TypeError, match=msg):
            df.rename({}, columns={})

        with pytest.raises(TypeError, match=msg):
            df.rename({}, columns={}, index={})

    def test_assign_columns(self, float_frame):
        float_frame["hi"] = "there"

        df = float_frame.copy()
        df.columns = ["foo", "bar", "baz", "quux", "foo2"]
        tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False)
        tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False)

    def test_set_index_preserve_categorical_dtype(self):
        # GH13743, GH13854
        df = DataFrame(
            {
                "A": [1, 2, 1, 1, 2],
                "B": [10, 16, 22, 28, 34],
                "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),
                "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),
            }
        )
        for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]:
            result = df.set_index(cols).reset_index()
            result = result.reindex(columns=df.columns)
            tm.assert_frame_equal(result, df)

    def test_rename_signature(self):
        sig = inspect.signature(DataFrame.rename)
        parameters = set(sig.parameters)
        assert parameters == {
            "self",
            "mapper",
            "index",
            "columns",
            "axis",
            "inplace",
            "copy",
            "level",
            "errors",
        }

    def test_reindex_signature(self):
        sig = inspect.signature(DataFrame.reindex)
        parameters = set(sig.parameters)
        assert parameters == {
            "self",
            "labels",
            "index",
            "columns",
            "axis",
            "limit",
            "copy",
            "level",
            "method",
            "fill_value",
            "tolerance",
        }

    def test_droplevel(self):
        # GH20342
        df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
        df = df.set_index([0, 1]).rename_axis(["a", "b"])
        df.columns = MultiIndex.from_tuples(
            [("c", "e"), ("d", "f")], names=["level_1", "level_2"]
        )

        # test that dropping of a level in index works
        expected = df.reset_index("a", drop=True)
        result = df.droplevel("a", axis="index")
        tm.assert_frame_equal(result, expected)

        # test that dropping of a level in columns works
        expected = df.copy()
        expected.columns = Index(["c", "d"], name="level_1")
        result = df.droplevel("level_2", axis="columns")
        tm.assert_frame_equal(result, expected)


class TestIntervalIndex:
    def test_setitem(self):

        df = DataFrame({"A": range(10)})
        s = cut(df.A, 5)
        assert isinstance(s.cat.categories, IntervalIndex)

        # B & D end up as Categoricals
        # the remainer are converted to in-line objects
        # contining an IntervalIndex.values
        df["B"] = s
        df["C"] = np.array(s)
        df["D"] = s.values
        df["E"] = np.array(s.values)

        assert is_categorical_dtype(df["B"])
        assert is_interval_dtype(df["B"].cat.categories)
        assert is_categorical_dtype(df["D"])
        assert is_interval_dtype(df["D"].cat.categories)

        assert is_object_dtype(df["C"])
        assert is_object_dtype(df["E"])

        # they compare equal as Index
        # when converted to numpy objects
        c = lambda x: Index(np.array(x))
        tm.assert_index_equal(c(df.B), c(df.B), check_names=False)
        tm.assert_index_equal(c(df.B), c(df.C), check_names=False)
        tm.assert_index_equal(c(df.B), c(df.D), check_names=False)
        tm.assert_index_equal(c(df.B), c(df.D), check_names=False)

        # B & D are the same Series
        tm.assert_series_equal(df["B"], df["B"], check_names=False)
        tm.assert_series_equal(df["B"], df["D"], check_names=False)

        # C & E are the same Series
        tm.assert_series_equal(df["C"], df["C"], check_names=False)
        tm.assert_series_equal(df["C"], df["E"], check_names=False)

    def test_set_reset_index(self):

        df = DataFrame({"A": range(10)})
        s = cut(df.A, 5)
        df["B"] = s
        df = df.set_index("B")

        df = df.reset_index()

    def test_set_axis_inplace(self):
        # GH14636
        df = DataFrame(
            {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2], "C": [4.4, 5.5, 6.6]},
            index=[2010, 2011, 2012],
        )

        expected = {0: df.copy(), 1: df.copy()}
        expected[0].index = list("abc")
        expected[1].columns = list("abc")
        expected["index"] = expected[0]
        expected["columns"] = expected[1]

        for axis in expected:
            result = df.copy()
            result.set_axis(list("abc"), axis=axis, inplace=True)
            tm.assert_frame_equal(result, expected[axis])

            # inplace=False
            result = df.set_axis(list("abc"), axis=axis)
            tm.assert_frame_equal(expected[axis], result)

        # omitting the "axis" parameter
        with tm.assert_produces_warning(None):
            result = df.set_axis(list("abc"))
        tm.assert_frame_equal(result, expected[0])

        # wrong values for the "axis" parameter
        for axis in 3, "foo":
            with pytest.raises(ValueError, match="No axis named"):
                df.set_axis(list("abc"), axis=axis)
