from datetime import datetime
from decimal import Decimal

import numpy as np
import pytest

from pandas._libs import lib
from pandas.compat import IS64
from pandas.errors import (
    PerformanceWarning,
    SpecificationError,
)

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    Grouper,
    Index,
    MultiIndex,
    RangeIndex,
    Series,
    Timedelta,
    Timestamp,
    date_range,
    to_datetime,
)
import pandas._testing as tm
from pandas.core.arrays import BooleanArray
import pandas.core.common as com
from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
from pandas.tests.groupby import get_groupby_method_args


def test_repr():
    # GH18203
    result = repr(Grouper(key="A", level="B"))
    expected = "Grouper(key='A', level='B', axis=0, sort=False, dropna=True)"
    assert result == expected


@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"])
def test_basic(dtype):

    data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)

    index = np.arange(9)
    np.random.shuffle(index)
    data = data.reindex(index)

    grouped = data.groupby(lambda x: x // 3, group_keys=False)

    for k, v in grouped:
        assert len(v) == 3

    agged = grouped.aggregate(np.mean)
    assert agged[1] == 1

    tm.assert_series_equal(agged, grouped.agg(np.mean))  # shorthand
    tm.assert_series_equal(agged, grouped.mean())
    tm.assert_series_equal(grouped.agg(np.sum), grouped.sum())

    expected = grouped.apply(lambda x: x * x.sum())
    transformed = grouped.transform(lambda x: x * x.sum())
    assert transformed[7] == 12
    tm.assert_series_equal(transformed, expected)

    value_grouped = data.groupby(data)
    tm.assert_series_equal(
        value_grouped.aggregate(np.mean), agged, check_index_type=False
    )

    # complex agg
    agged = grouped.aggregate([np.mean, np.std])

    msg = r"nested renamer is not supported"
    with pytest.raises(SpecificationError, match=msg):
        grouped.aggregate({"one": np.mean, "two": np.std})

    group_constants = {0: 10, 1: 20, 2: 30}
    agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
    assert agged[1] == 21

    # corner cases
    msg = "Must produce aggregated value"
    # exception raised is type Exception
    with pytest.raises(Exception, match=msg):
        grouped.aggregate(lambda x: x * 2)


def test_groupby_nonobject_dtype(mframe, df_mixed_floats):
    key = mframe.index.codes[0]
    grouped = mframe.groupby(key)
    result = grouped.sum()

    expected = mframe.groupby(key.astype("O")).sum()
    tm.assert_frame_equal(result, expected)

    # GH 3911, mixed frame non-conversion
    df = df_mixed_floats.copy()
    df["value"] = range(len(df))

    def max_value(group):
        return group.loc[group["value"].idxmax()]

    applied = df.groupby("A").apply(max_value)
    result = applied.dtypes
    expected = df.dtypes
    tm.assert_series_equal(result, expected)


def test_groupby_return_type():

    # GH2893, return a reduced type
    df1 = DataFrame(
        [
            {"val1": 1, "val2": 20},
            {"val1": 1, "val2": 19},
            {"val1": 2, "val2": 27},
            {"val1": 2, "val2": 12},
        ]
    )

    def func(dataf):
        return dataf["val2"] - dataf["val2"].mean()

    with tm.assert_produces_warning(FutureWarning):
        result = df1.groupby("val1", squeeze=True).apply(func)
    assert isinstance(result, Series)

    df2 = DataFrame(
        [
            {"val1": 1, "val2": 20},
            {"val1": 1, "val2": 19},
            {"val1": 1, "val2": 27},
            {"val1": 1, "val2": 12},
        ]
    )

    def func(dataf):
        return dataf["val2"] - dataf["val2"].mean()

    with tm.assert_produces_warning(FutureWarning):
        result = df2.groupby("val1", squeeze=True).apply(func)
    assert isinstance(result, Series)

    # GH3596, return a consistent type (regression in 0.11 from 0.10.1)
    df = DataFrame([[1, 1], [1, 1]], columns=["X", "Y"])
    with tm.assert_produces_warning(FutureWarning):
        result = df.groupby("X", squeeze=False).count()
    assert isinstance(result, DataFrame)


def test_inconsistent_return_type():
    # GH5592
    # inconsistent return type
    df = DataFrame(
        {
            "A": ["Tiger", "Tiger", "Tiger", "Lamb", "Lamb", "Pony", "Pony"],
            "B": Series(np.arange(7), dtype="int64"),
            "C": date_range("20130101", periods=7),
        }
    )

    def f(grp):
        return grp.iloc[0]

    expected = df.groupby("A").first()[["B"]]
    result = df.groupby("A").apply(f)[["B"]]
    tm.assert_frame_equal(result, expected)

    def f(grp):
        if grp.name == "Tiger":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["B"]]
    e = expected.copy()
    e.loc["Tiger"] = np.nan
    tm.assert_frame_equal(result, e)

    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["B"]]
    e = expected.copy()
    e.loc["Pony"] = np.nan
    tm.assert_frame_equal(result, e)

    # 5592 revisited, with datetimes
    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["C"]]
    e = df.groupby("A").first()[["C"]]
    e.loc["Pony"] = pd.NaT
    tm.assert_frame_equal(result, e)

    # scalar outputs
    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0].loc["C"]

    result = df.groupby("A").apply(f)
    e = df.groupby("A").first()["C"].copy()
    e.loc["Pony"] = np.nan
    e.name = None
    tm.assert_series_equal(result, e)


def test_pass_args_kwargs(ts, tsframe):
    def f(x, q=None, axis=0):
        return np.percentile(x, q, axis=axis)

    g = lambda x: np.percentile(x, 80, axis=0)

    # Series
    ts_grouped = ts.groupby(lambda x: x.month)
    agg_result = ts_grouped.agg(np.percentile, 80, axis=0)
    apply_result = ts_grouped.apply(np.percentile, 80, axis=0)
    trans_result = ts_grouped.transform(np.percentile, 80, axis=0)

    agg_expected = ts_grouped.quantile(0.8)
    trans_expected = ts_grouped.transform(g)

    tm.assert_series_equal(apply_result, agg_expected)
    tm.assert_series_equal(agg_result, agg_expected)
    tm.assert_series_equal(trans_result, trans_expected)

    agg_result = ts_grouped.agg(f, q=80)
    apply_result = ts_grouped.apply(f, q=80)
    trans_result = ts_grouped.transform(f, q=80)
    tm.assert_series_equal(agg_result, agg_expected)
    tm.assert_series_equal(apply_result, agg_expected)
    tm.assert_series_equal(trans_result, trans_expected)

    # DataFrame
    for as_index in [True, False]:
        df_grouped = tsframe.groupby(lambda x: x.month, as_index=as_index)
        agg_result = df_grouped.agg(np.percentile, 80, axis=0)
        apply_result = df_grouped.apply(DataFrame.quantile, 0.8)
        expected = df_grouped.quantile(0.8)
        tm.assert_frame_equal(apply_result, expected, check_names=False)
        tm.assert_frame_equal(agg_result, expected)

        apply_result = df_grouped.apply(DataFrame.quantile, [0.4, 0.8])
        expected_seq = df_grouped.quantile([0.4, 0.8])
        tm.assert_frame_equal(apply_result, expected_seq, check_names=False)

        agg_result = df_grouped.agg(f, q=80)
        apply_result = df_grouped.apply(DataFrame.quantile, q=0.8)
        tm.assert_frame_equal(agg_result, expected)
        tm.assert_frame_equal(apply_result, expected, check_names=False)


@pytest.mark.parametrize("as_index", [True, False])
def test_pass_args_kwargs_duplicate_columns(tsframe, as_index):
    # go through _aggregate_frame with self.axis == 0 and duplicate columns
    tsframe.columns = ["A", "B", "A", "C"]
    gb = tsframe.groupby(lambda x: x.month, as_index=as_index)

    res = gb.agg(np.percentile, 80, axis=0)

    ex_data = {
        1: tsframe[tsframe.index.month == 1].quantile(0.8),
        2: tsframe[tsframe.index.month == 2].quantile(0.8),
    }
    expected = DataFrame(ex_data).T
    if not as_index:
        # TODO: try to get this more consistent?
        expected.index = Index(range(2))

    tm.assert_frame_equal(res, expected)


def test_len():
    df = tm.makeTimeDataFrame()
    grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
    assert len(grouped) == len(df)

    grouped = df.groupby([lambda x: x.year, lambda x: x.month])
    expected = len({(x.year, x.month) for x in df.index})
    assert len(grouped) == expected

    # issue 11016
    df = DataFrame({"a": [np.nan] * 3, "b": [1, 2, 3]})
    assert len(df.groupby("a")) == 0
    assert len(df.groupby("b")) == 3
    assert len(df.groupby(["a", "b"])) == 3


def test_basic_regression():
    # regression
    result = Series([1.0 * x for x in list(range(1, 10)) * 10])

    data = np.random.random(1100) * 10.0
    groupings = Series(data)

    grouped = result.groupby(groupings)
    grouped.mean()


@pytest.mark.parametrize(
    "dtype", ["float64", "float32", "int64", "int32", "int16", "int8"]
)
def test_with_na_groups(dtype):
    index = Index(np.arange(10))
    values = Series(np.ones(10), index, dtype=dtype)
    labels = Series(
        [np.nan, "foo", "bar", "bar", np.nan, np.nan, "bar", "bar", np.nan, "foo"],
        index=index,
    )

    # this SHOULD be an int
    grouped = values.groupby(labels)
    agged = grouped.agg(len)
    expected = Series([4, 2], index=["bar", "foo"])

    tm.assert_series_equal(agged, expected, check_dtype=False)

    # assert issubclass(agged.dtype.type, np.integer)

    # explicitly return a float from my function
    def f(x):
        return float(len(x))

    agged = grouped.agg(f)
    expected = Series([4.0, 2.0], index=["bar", "foo"])

    tm.assert_series_equal(agged, expected)


def test_indices_concatenation_order():

    # GH 2808

    def f1(x):
        y = x[(x.b % 2) == 1] ** 2
        if y.empty:
            multiindex = MultiIndex(levels=[[]] * 2, codes=[[]] * 2, names=["b", "c"])
            res = DataFrame(columns=["a"], index=multiindex)
            return res
        else:
            y = y.set_index(["b", "c"])
            return y

    def f2(x):
        y = x[(x.b % 2) == 1] ** 2
        if y.empty:
            return DataFrame()
        else:
            y = y.set_index(["b", "c"])
            return y

    def f3(x):
        y = x[(x.b % 2) == 1] ** 2
        if y.empty:
            multiindex = MultiIndex(
                levels=[[]] * 2, codes=[[]] * 2, names=["foo", "bar"]
            )
            res = DataFrame(columns=["a", "b"], index=multiindex)
            return res
        else:
            return y

    df = DataFrame({"a": [1, 2, 2, 2], "b": range(4), "c": range(5, 9)})

    df2 = DataFrame({"a": [3, 2, 2, 2], "b": range(4), "c": range(5, 9)})

    # correct result
    result1 = df.groupby("a").apply(f1)
    result2 = df2.groupby("a").apply(f1)
    tm.assert_frame_equal(result1, result2)

    # should fail (not the same number of levels)
    msg = "Cannot concat indices that do not have the same number of levels"
    with pytest.raises(AssertionError, match=msg):
        df.groupby("a").apply(f2)
    with pytest.raises(AssertionError, match=msg):
        df2.groupby("a").apply(f2)

    # should fail (incorrect shape)
    with pytest.raises(AssertionError, match=msg):
        df.groupby("a").apply(f3)
    with pytest.raises(AssertionError, match=msg):
        df2.groupby("a").apply(f3)


def test_attr_wrapper(ts):
    grouped = ts.groupby(lambda x: x.weekday())

    result = grouped.std()
    expected = grouped.agg(lambda x: np.std(x, ddof=1))
    tm.assert_series_equal(result, expected)

    # this is pretty cool
    result = grouped.describe()
    expected = {name: gp.describe() for name, gp in grouped}
    expected = DataFrame(expected).T
    tm.assert_frame_equal(result, expected)

    # get attribute
    result = grouped.dtype
    expected = grouped.agg(lambda x: x.dtype)
    tm.assert_series_equal(result, expected)

    # make sure raises error
    msg = "'SeriesGroupBy' object has no attribute 'foo'"
    with pytest.raises(AttributeError, match=msg):
        getattr(grouped, "foo")


def test_frame_groupby(tsframe):
    grouped = tsframe.groupby(lambda x: x.weekday())

    # aggregate
    aggregated = grouped.aggregate(np.mean)
    assert len(aggregated) == 5
    assert len(aggregated.columns) == 4

    # by string
    tscopy = tsframe.copy()
    tscopy["weekday"] = [x.weekday() for x in tscopy.index]
    stragged = tscopy.groupby("weekday").aggregate(np.mean)
    tm.assert_frame_equal(stragged, aggregated, check_names=False)

    # transform
    grouped = tsframe.head(30).groupby(lambda x: x.weekday())
    transformed = grouped.transform(lambda x: x - x.mean())
    assert len(transformed) == 30
    assert len(transformed.columns) == 4

    # transform propagate
    transformed = grouped.transform(lambda x: x.mean())
    for name, group in grouped:
        mean = group.mean()
        for idx in group.index:
            tm.assert_series_equal(transformed.xs(idx), mean, check_names=False)

    # iterate
    for weekday, group in grouped:
        assert group.index[0].weekday() == weekday

    # groups / group_indices
    groups = grouped.groups
    indices = grouped.indices

    for k, v in groups.items():
        samething = tsframe.index.take(indices[k])
        assert (samething == v).all()


def test_frame_groupby_columns(tsframe):
    mapping = {"A": 0, "B": 0, "C": 1, "D": 1}
    grouped = tsframe.groupby(mapping, axis=1)

    # aggregate
    aggregated = grouped.aggregate(np.mean)
    assert len(aggregated) == len(tsframe)
    assert len(aggregated.columns) == 2

    # transform
    tf = lambda x: x - x.mean()
    groupedT = tsframe.T.groupby(mapping, axis=0)
    tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))

    # iterate
    for k, v in grouped:
        assert len(v.columns) == 2


def test_frame_set_name_single(df):
    grouped = df.groupby("A")

    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.mean()
    assert result.index.name == "A"

    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby("A", as_index=False).mean()
    assert result.index.name != "A"

    # GH#50538
    msg = "The operation <function mean.*failed"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.agg(np.mean)
    assert result.index.name == "A"
    # Ensure users can't pass numeric_only
    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
        grouped.agg(np.mean, numeric_only=True)

    result = grouped.agg({"C": np.mean, "D": np.std})
    assert result.index.name == "A"

    result = grouped["C"].mean()
    assert result.index.name == "A"
    result = grouped["C"].agg(np.mean)
    assert result.index.name == "A"
    result = grouped["C"].agg([np.mean, np.std])
    assert result.index.name == "A"

    msg = r"nested renamer is not supported"
    with pytest.raises(SpecificationError, match=msg):
        grouped["C"].agg({"foo": np.mean, "bar": np.std})


def test_multi_func(df):
    col1 = df["A"]
    col2 = df["B"]

    grouped = df.groupby([col1.get, col2.get])
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        agged = grouped.mean()
        expected = df.groupby(["A", "B"]).mean()

    # TODO groupby get drops names
    tm.assert_frame_equal(
        agged.loc[:, ["C", "D"]], expected.loc[:, ["C", "D"]], check_names=False
    )

    # some "groups" with no data
    df = DataFrame(
        {
            "v1": np.random.randn(6),
            "v2": np.random.randn(6),
            "k1": np.array(["b", "b", "b", "a", "a", "a"]),
            "k2": np.array(["1", "1", "1", "2", "2", "2"]),
        },
        index=["one", "two", "three", "four", "five", "six"],
    )
    # only verify that it works for now
    grouped = df.groupby(["k1", "k2"])
    grouped.agg(np.sum)


def test_multi_key_multiple_functions(df):
    grouped = df.groupby(["A", "B"])["C"]

    agged = grouped.agg([np.mean, np.std])
    expected = DataFrame({"mean": grouped.agg(np.mean), "std": grouped.agg(np.std)})
    tm.assert_frame_equal(agged, expected)


def test_frame_multi_key_function_list():
    data = DataFrame(
        {
            "A": [
                "foo",
                "foo",
                "foo",
                "foo",
                "bar",
                "bar",
                "bar",
                "bar",
                "foo",
                "foo",
                "foo",
            ],
            "B": [
                "one",
                "one",
                "one",
                "two",
                "one",
                "one",
                "one",
                "two",
                "two",
                "two",
                "one",
            ],
            "C": [
                "dull",
                "dull",
                "shiny",
                "dull",
                "dull",
                "shiny",
                "shiny",
                "dull",
                "shiny",
                "shiny",
                "shiny",
            ],
            "D": np.random.randn(11),
            "E": np.random.randn(11),
            "F": np.random.randn(11),
        }
    )

    grouped = data.groupby(["A", "B"])
    funcs = [np.mean, np.std]
    with tm.assert_produces_warning(
        FutureWarning, match=r"\['C'\] did not aggregate successfully"
    ):
        agged = grouped.agg(funcs)
    expected = pd.concat(
        [grouped["D"].agg(funcs), grouped["E"].agg(funcs), grouped["F"].agg(funcs)],
        keys=["D", "E", "F"],
        axis=1,
    )
    assert isinstance(agged.index, MultiIndex)
    assert isinstance(expected.index, MultiIndex)
    tm.assert_frame_equal(agged, expected)


@pytest.mark.parametrize("op", [lambda x: x.sum(), lambda x: x.mean()])
def test_groupby_multiple_columns(df, op):
    data = df
    grouped = data.groupby(["A", "B"])

    result1 = op(grouped)

    keys = []
    values = []
    for n1, gp1 in data.groupby("A"):
        for n2, gp2 in gp1.groupby("B"):
            keys.append((n1, n2))
            values.append(op(gp2.loc[:, ["C", "D"]]))

    mi = MultiIndex.from_tuples(keys, names=["A", "B"])
    expected = pd.concat(values, axis=1).T
    expected.index = mi

    # a little bit crude
    for col in ["C", "D"]:
        result_col = op(grouped[col])
        pivoted = result1[col]
        exp = expected[col]
        tm.assert_series_equal(result_col, exp)
        tm.assert_series_equal(pivoted, exp)

    # test single series works the same
    result = data["C"].groupby([data["A"], data["B"]]).mean()
    expected = data.groupby(["A", "B"]).mean()["C"]

    tm.assert_series_equal(result, expected)


def test_as_index_select_column():
    # GH 5764
    df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
    result = df.groupby("A", as_index=False)["B"].get_group(1)
    expected = Series([2, 4], name="B")
    tm.assert_series_equal(result, expected)

    result = df.groupby("A", as_index=False, group_keys=True)["B"].apply(
        lambda x: x.cumsum()
    )
    expected = Series(
        [2, 6, 6], name="B", index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 2)])
    )
    tm.assert_series_equal(result, expected)


def test_groupby_as_index_select_column_sum_empty_df():
    # GH 35246
    df = DataFrame(columns=Index(["A", "B", "C"], name="alpha"))
    left = df.groupby(by="A", as_index=False)["B"].sum(numeric_only=False)

    expected = DataFrame(columns=df.columns[:2], index=range(0))
    tm.assert_frame_equal(left, expected)


def test_groupby_as_index_agg(df):
    grouped = df.groupby("A", as_index=False)

    # single-key

    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.agg(np.mean)
        expected = grouped.mean()
    tm.assert_frame_equal(result, expected)

    result2 = grouped.agg({"C": np.mean, "D": np.sum})
    with tm.assert_produces_warning(FutureWarning, match=msg):
        expected2 = grouped.mean()
        expected2["D"] = grouped.sum()["D"]
    tm.assert_frame_equal(result2, expected2)

    grouped = df.groupby("A", as_index=True)

    msg = r"nested renamer is not supported"
    with pytest.raises(SpecificationError, match=msg):
        grouped["C"].agg({"Q": np.sum})

    # multi-key

    grouped = df.groupby(["A", "B"], as_index=False)

    result = grouped.agg(np.mean)
    expected = grouped.mean()
    tm.assert_frame_equal(result, expected)

    result2 = grouped.agg({"C": np.mean, "D": np.sum})
    expected2 = grouped.mean()
    expected2["D"] = grouped.sum()["D"]
    tm.assert_frame_equal(result2, expected2)

    expected3 = grouped["C"].sum()
    expected3 = DataFrame(expected3).rename(columns={"C": "Q"})
    result3 = grouped["C"].agg({"Q": np.sum})
    tm.assert_frame_equal(result3, expected3)

    # GH7115 & GH8112 & GH8582
    df = DataFrame(np.random.randint(0, 100, (50, 3)), columns=["jim", "joe", "jolie"])
    ts = Series(np.random.randint(5, 10, 50), name="jim")

    gr = df.groupby(ts)
    gr.nth(0)  # invokes set_selection_from_grouper internally
    tm.assert_frame_equal(gr.apply(sum), df.groupby(ts).apply(sum))

    for attr in ["mean", "max", "count", "idxmax", "cumsum", "all"]:
        gr = df.groupby(ts, as_index=False)
        left = getattr(gr, attr)()

        gr = df.groupby(ts.values, as_index=True)
        right = getattr(gr, attr)().reset_index(drop=True)

        tm.assert_frame_equal(left, right)


def test_ops_not_as_index(reduction_func):
    # GH 10355, 21090
    # Using as_index=False should not modify grouped column

    if reduction_func in ("corrwith", "nth", "ngroup"):
        pytest.skip(f"GH 5755: Test not applicable for {reduction_func}")
    warn = FutureWarning if reduction_func == "mad" else None

    df = DataFrame(np.random.randint(0, 5, size=(100, 2)), columns=["a", "b"])
    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
        expected = getattr(df.groupby("a"), reduction_func)()
    if reduction_func == "size":
        expected = expected.rename("size")
    expected = expected.reset_index()

    if reduction_func != "size":
        # 32 bit compat -> groupby preserves dtype whereas reset_index casts to int64
        expected["a"] = expected["a"].astype(df["a"].dtype)

    g = df.groupby("a", as_index=False)

    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
        result = getattr(g, reduction_func)()
    tm.assert_frame_equal(result, expected)

    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
        result = g.agg(reduction_func)
    tm.assert_frame_equal(result, expected)

    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
        result = getattr(g["b"], reduction_func)()
    tm.assert_frame_equal(result, expected)

    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
        result = g["b"].agg(reduction_func)
    tm.assert_frame_equal(result, expected)


def test_as_index_series_return_frame(df):
    grouped = df.groupby("A", as_index=False)
    grouped2 = df.groupby(["A", "B"], as_index=False)

    # GH#50538
    msg = "The operation <function sum.*failed"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped["C"].agg(np.sum)
        expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
    assert isinstance(result, DataFrame)
    tm.assert_frame_equal(result, expected)
    # Ensure users can't pass numeric_only
    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
        grouped.agg(np.mean, numeric_only=True)

    result2 = grouped2["C"].agg(np.sum)
    expected2 = grouped2.agg(np.sum).loc[:, ["A", "B", "C"]]
    assert isinstance(result2, DataFrame)
    tm.assert_frame_equal(result2, expected2)

    result = grouped["C"].sum()
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        expected = grouped.sum().loc[:, ["A", "C"]]
    assert isinstance(result, DataFrame)
    tm.assert_frame_equal(result, expected)

    result2 = grouped2["C"].sum()
    expected2 = grouped2.sum().loc[:, ["A", "B", "C"]]
    assert isinstance(result2, DataFrame)
    tm.assert_frame_equal(result2, expected2)


def test_as_index_series_column_slice_raises(df):
    # GH15072
    grouped = df.groupby("A", as_index=False)
    msg = r"Column\(s\) C already selected"

    with pytest.raises(IndexError, match=msg):
        grouped["C"].__getitem__("D")


def test_groupby_as_index_cython(df):
    data = df

    # single-key
    grouped = data.groupby("A", as_index=False)
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.mean()
        expected = data.groupby(["A"]).mean()
    expected.insert(0, "A", expected.index)
    expected.index = np.arange(len(expected))
    tm.assert_frame_equal(result, expected)

    # multi-key
    grouped = data.groupby(["A", "B"], as_index=False)
    result = grouped.mean()
    expected = data.groupby(["A", "B"]).mean()

    arrays = list(zip(*expected.index.values))
    expected.insert(0, "A", arrays[0])
    expected.insert(1, "B", arrays[1])
    expected.index = np.arange(len(expected))
    tm.assert_frame_equal(result, expected)


def test_groupby_as_index_series_scalar(df):
    grouped = df.groupby(["A", "B"], as_index=False)

    # GH #421

    result = grouped["C"].agg(len)
    expected = grouped.agg(len).loc[:, ["A", "B", "C"]]
    tm.assert_frame_equal(result, expected)


def test_groupby_as_index_corner(df, ts):
    msg = "as_index=False only valid with DataFrame"
    with pytest.raises(TypeError, match=msg):
        ts.groupby(lambda x: x.weekday(), as_index=False)

    msg = "as_index=False only valid for axis=0"
    with pytest.raises(ValueError, match=msg):
        df.groupby(lambda x: x.lower(), as_index=False, axis=1)


def test_groupby_multiple_key():
    df = tm.makeTimeDataFrame()
    grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
    agged = grouped.sum()
    tm.assert_almost_equal(df.values, agged.values)

    grouped = df.T.groupby(
        [lambda x: x.year, lambda x: x.month, lambda x: x.day], axis=1
    )

    agged = grouped.agg(lambda x: x.sum())
    tm.assert_index_equal(agged.index, df.columns)
    tm.assert_almost_equal(df.T.values, agged.values)

    agged = grouped.agg(lambda x: x.sum())
    tm.assert_almost_equal(df.T.values, agged.values)


def test_groupby_multi_corner(df):
    # test that having an all-NA column doesn't mess you up
    df = df.copy()
    df["bad"] = np.nan
    agged = df.groupby(["A", "B"]).mean()

    expected = df.groupby(["A", "B"]).mean()
    expected["bad"] = np.nan

    tm.assert_frame_equal(agged, expected)


def test_omit_nuisance(df):
    grouped = df.groupby("A")
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        agged = grouped.agg(np.mean)
        exp = grouped.mean()
    tm.assert_frame_equal(agged, exp)

    df = df.loc[:, ["A", "C", "D"]]
    df["E"] = datetime.now()
    grouped = df.groupby("A")
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.agg(np.sum)
        expected = grouped.sum()
    tm.assert_frame_equal(result, expected)

    # won't work with axis = 1
    grouped = df.groupby({"A": 0, "C": 0, "D": 1, "E": 1}, axis=1)
    msg = "does not support reduction 'sum'"
    with pytest.raises(TypeError, match=msg):
        grouped.agg(lambda x: x.sum(0, numeric_only=False))


@pytest.mark.parametrize(
    "agg_function",
    ["max", "min"],
)
def test_keep_nuisance_agg(df, agg_function):
    # GH 38815
    grouped = df.groupby("A")
    result = getattr(grouped, agg_function)()
    expected = result.copy()
    expected.loc["bar", "B"] = getattr(df.loc[df["A"] == "bar", "B"], agg_function)()
    expected.loc["foo", "B"] = getattr(df.loc[df["A"] == "foo", "B"], agg_function)()
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "agg_function",
    ["sum", "mean", "prod", "std", "var", "sem", "median"],
)
@pytest.mark.parametrize("numeric_only", [lib.no_default, True, False])
def test_omit_nuisance_agg(df, agg_function, numeric_only):
    # GH 38774, GH 38815
    if numeric_only is lib.no_default or (not numeric_only and agg_function != "sum"):
        # sum doesn't drop strings
        warn = FutureWarning
    else:
        warn = None

    grouped = df.groupby("A")

    if agg_function in ("var", "std", "sem") and numeric_only is False:
        # Added numeric_only as part of GH#46560; these do not drop nuisance
        # columns when numeric_only is False
        klass = TypeError if agg_function == "var" else ValueError
        with pytest.raises(klass, match="could not convert string to float"):
            getattr(grouped, agg_function)(numeric_only=numeric_only)
    else:
        if numeric_only is lib.no_default:
            msg = (
                f"The default value of numeric_only in DataFrameGroupBy.{agg_function}"
            )
        else:
            msg = "Dropping invalid columns"
        with tm.assert_produces_warning(warn, match=msg):
            result = getattr(grouped, agg_function)(numeric_only=numeric_only)
        if (
            (numeric_only is lib.no_default or not numeric_only)
            # These methods drop non-numeric columns even when numeric_only is False
            and agg_function not in ("mean", "prod", "median")
        ):
            columns = ["A", "B", "C", "D"]
        else:
            columns = ["A", "C", "D"]
        if agg_function == "sum" and numeric_only is False:
            # sum doesn't drop nuisance string columns
            warn = None
        elif agg_function in ("sum", "std", "var", "sem") and numeric_only is not True:
            warn = FutureWarning
        else:
            warn = None
        msg = "The default value of numeric_only"
        with tm.assert_produces_warning(warn, match=msg):
            expected = getattr(df.loc[:, columns].groupby("A"), agg_function)(
                numeric_only=numeric_only
            )
        tm.assert_frame_equal(result, expected)


def test_omit_nuisance_warnings(df):
    # GH 38815
    with tm.assert_produces_warning(FutureWarning, filter_level="always"):
        grouped = df.groupby("A")
        result = grouped.skew()
        expected = df.loc[:, ["A", "C", "D"]].groupby("A").skew()
        tm.assert_frame_equal(result, expected)


def test_omit_nuisance_python_multiple(three_group):
    grouped = three_group.groupby(["A", "B"])

    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        agged = grouped.agg(np.mean)
        exp = grouped.mean()
    tm.assert_frame_equal(agged, exp)


def test_empty_groups_corner(mframe):
    # handle empty groups
    df = DataFrame(
        {
            "k1": np.array(["b", "b", "b", "a", "a", "a"]),
            "k2": np.array(["1", "1", "1", "2", "2", "2"]),
            "k3": ["foo", "bar"] * 3,
            "v1": np.random.randn(6),
            "v2": np.random.randn(6),
        }
    )

    grouped = df.groupby(["k1", "k2"])
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.agg(np.mean)
        expected = grouped.mean()
    tm.assert_frame_equal(result, expected)

    grouped = mframe[3:5].groupby(level=0)
    agged = grouped.apply(lambda x: x.mean())
    agged_A = grouped["A"].apply(np.mean)
    tm.assert_series_equal(agged["A"], agged_A)
    assert agged.index.name == "first"


def test_nonsense_func():
    df = DataFrame([0])
    msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'"
    with pytest.raises(TypeError, match=msg):
        df.groupby(lambda x: x + "foo")


def test_wrap_aggregated_output_multindex(mframe):
    df = mframe.T
    df["baz", "two"] = "peekaboo"

    keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
    # GH#50538
    msg = "The operation <function mean.*failed"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        agged = df.groupby(keys).agg(np.mean)
    assert isinstance(agged.columns, MultiIndex)
    # Ensure users can't pass numeric_only
    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
        df.groupby(keys).agg(np.mean, numeric_only=True)

    def aggfun(ser):
        if ser.name == ("foo", "one"):
            raise TypeError
        else:
            return ser.sum()

    with tm.assert_produces_warning(FutureWarning, match="Dropping invalid columns"):
        agged2 = df.groupby(keys).aggregate(aggfun)
    assert len(agged2.columns) + 1 == len(df.columns)


def test_groupby_level_apply(mframe):

    result = mframe.groupby(level=0).count()
    assert result.index.name == "first"
    result = mframe.groupby(level=1).count()
    assert result.index.name == "second"

    result = mframe["A"].groupby(level=0).count()
    assert result.index.name == "first"


def test_groupby_level_mapper(mframe):
    deleveled = mframe.reset_index()

    mapper0 = {"foo": 0, "bar": 0, "baz": 1, "qux": 1}
    mapper1 = {"one": 0, "two": 0, "three": 1}

    result0 = mframe.groupby(mapper0, level=0).sum()
    result1 = mframe.groupby(mapper1, level=1).sum()

    mapped_level0 = np.array([mapper0.get(x) for x in deleveled["first"]])
    mapped_level1 = np.array([mapper1.get(x) for x in deleveled["second"]])
    expected0 = mframe.groupby(mapped_level0).sum()
    expected1 = mframe.groupby(mapped_level1).sum()
    expected0.index.name, expected1.index.name = "first", "second"

    tm.assert_frame_equal(result0, expected0)
    tm.assert_frame_equal(result1, expected1)


def test_groupby_level_nonmulti():
    # GH 1313, GH 13901
    s = Series([1, 2, 3, 10, 4, 5, 20, 6], Index([1, 2, 3, 1, 4, 5, 2, 6], name="foo"))
    expected = Series([11, 22, 3, 4, 5, 6], Index(range(1, 7), name="foo"))

    result = s.groupby(level=0).sum()
    tm.assert_series_equal(result, expected)
    result = s.groupby(level=[0]).sum()
    tm.assert_series_equal(result, expected)
    result = s.groupby(level=-1).sum()
    tm.assert_series_equal(result, expected)
    result = s.groupby(level=[-1]).sum()
    tm.assert_series_equal(result, expected)

    msg = "level > 0 or level < -1 only valid with MultiIndex"
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=1)
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=-2)
    msg = "No group keys passed!"
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=[])
    msg = "multiple levels only valid with MultiIndex"
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=[0, 0])
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=[0, 1])
    msg = "level > 0 or level < -1 only valid with MultiIndex"
    with pytest.raises(ValueError, match=msg):
        s.groupby(level=[1])


def test_groupby_complex():
    # GH 12902
    a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1])
    expected = Series((1 + 2j, 5 + 10j))

    result = a.groupby(level=0).sum()
    tm.assert_series_equal(result, expected)

    with tm.assert_produces_warning(FutureWarning):
        result = a.sum(level=0)
    tm.assert_series_equal(result, expected)


def test_groupby_complex_numbers():
    # GH 17927
    df = DataFrame(
        [
            {"a": 1, "b": 1 + 1j},
            {"a": 1, "b": 1 + 2j},
            {"a": 4, "b": 1},
        ]
    )
    expected = DataFrame(
        np.array([1, 1, 1], dtype=np.int64),
        index=Index([(1 + 1j), (1 + 2j), (1 + 0j)], name="b"),
        columns=Index(["a"], dtype="object"),
    )
    result = df.groupby("b", sort=False).count()
    tm.assert_frame_equal(result, expected)

    # Sorted by the magnitude of the complex numbers
    expected.index = Index([(1 + 0j), (1 + 1j), (1 + 2j)], name="b")
    result = df.groupby("b", sort=True).count()
    tm.assert_frame_equal(result, expected)


def test_groupby_series_indexed_differently():
    s1 = Series(
        [5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7],
        index=Index(["a", "b", "c", "d", "e", "f", "g"]),
    )
    s2 = Series(
        [1.0, 1.0, 4.0, 5.0, 5.0, 7.0], index=Index(["a", "b", "d", "f", "g", "h"])
    )

    grouped = s1.groupby(s2)
    agged = grouped.mean()
    exp = s1.groupby(s2.reindex(s1.index).get).mean()
    tm.assert_series_equal(agged, exp)


def test_groupby_with_hier_columns():
    tuples = list(
        zip(
            *[
                ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
                ["one", "two", "one", "two", "one", "two", "one", "two"],
            ]
        )
    )
    index = MultiIndex.from_tuples(tuples)
    columns = MultiIndex.from_tuples(
        [("A", "cat"), ("B", "dog"), ("B", "cat"), ("A", "dog")]
    )
    df = DataFrame(np.random.randn(8, 4), index=index, columns=columns)

    result = df.groupby(level=0).mean()
    tm.assert_index_equal(result.columns, columns)

    result = df.groupby(level=0, axis=1).mean()
    tm.assert_index_equal(result.index, df.index)

    result = df.groupby(level=0).agg(np.mean)
    tm.assert_index_equal(result.columns, columns)

    result = df.groupby(level=0).apply(lambda x: x.mean())
    tm.assert_index_equal(result.columns, columns)

    result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
    tm.assert_index_equal(result.columns, Index(["A", "B"]))
    tm.assert_index_equal(result.index, df.index)

    # add a nuisance column
    sorted_columns, _ = columns.sortlevel(0)
    df["A", "foo"] = "bar"
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby(level=0).mean()
    tm.assert_index_equal(result.columns, df.columns[:-1])


def test_grouping_ndarray(df):
    grouped = df.groupby(df["A"].values)

    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = grouped.sum()
        expected = df.groupby("A").sum()
    tm.assert_frame_equal(
        result, expected, check_names=False
    )  # Note: no names when grouping by value


def test_groupby_wrong_multi_labels():

    index = Index([0, 1, 2, 3, 4], name="index")
    data = DataFrame(
        {
            "foo": ["foo1", "foo1", "foo2", "foo1", "foo3"],
            "bar": ["bar1", "bar2", "bar2", "bar1", "bar1"],
            "baz": ["baz1", "baz1", "baz1", "baz2", "baz2"],
            "spam": ["spam2", "spam3", "spam2", "spam1", "spam1"],
            "data": [20, 30, 40, 50, 60],
        },
        index=index,
    )

    grouped = data.groupby(["foo", "bar", "baz", "spam"])

    result = grouped.agg(np.mean)
    expected = grouped.mean()
    tm.assert_frame_equal(result, expected)


def test_groupby_series_with_name(df):
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby(df["A"]).mean()
        result2 = df.groupby(df["A"], as_index=False).mean()
    assert result.index.name == "A"
    assert "A" in result2

    result = df.groupby([df["A"], df["B"]]).mean()
    result2 = df.groupby([df["A"], df["B"]], as_index=False).mean()
    assert result.index.names == ("A", "B")
    assert "A" in result2
    assert "B" in result2


def test_seriesgroupby_name_attr(df):
    # GH 6265
    result = df.groupby("A")["C"]
    assert result.count().name == "C"
    assert result.mean().name == "C"

    testFunc = lambda x: np.sum(x) * 2
    assert result.agg(testFunc).name == "C"


def test_consistency_name():
    # GH 12363

    df = DataFrame(
        {
            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
            "C": np.random.randn(8) + 1.0,
            "D": np.arange(8),
        }
    )

    expected = df.groupby(["A"]).B.count()
    result = df.B.groupby(df.A).count()
    tm.assert_series_equal(result, expected)


def test_groupby_name_propagation(df):
    # GH 6124
    def summarize(df, name=None):
        return Series({"count": 1, "mean": 2, "omissions": 3}, name=name)

    def summarize_random_name(df):
        # Provide a different name for each Series.  In this case, groupby
        # should not attempt to propagate the Series name since they are
        # inconsistent.
        return Series({"count": 1, "mean": 2, "omissions": 3}, name=df.iloc[0]["A"])

    metrics = df.groupby("A").apply(summarize)
    assert metrics.columns.name is None
    metrics = df.groupby("A").apply(summarize, "metrics")
    assert metrics.columns.name == "metrics"
    metrics = df.groupby("A").apply(summarize_random_name)
    assert metrics.columns.name is None


def test_groupby_nonstring_columns():
    df = DataFrame([np.arange(10) for x in range(10)])
    grouped = df.groupby(0)
    result = grouped.mean()
    expected = df.groupby(df[0]).mean()
    tm.assert_frame_equal(result, expected)


def test_groupby_mixed_type_columns():
    # GH 13432, unorderable types in py3
    df = DataFrame([[0, 1, 2]], columns=["A", "B", 0])
    expected = DataFrame([[1, 2]], columns=["B", 0], index=Index([0], name="A"))

    result = df.groupby("A").first()
    tm.assert_frame_equal(result, expected)

    result = df.groupby("A").sum()
    tm.assert_frame_equal(result, expected)


# TODO: Ensure warning isn't emitted in the first place
@pytest.mark.filterwarnings("ignore:Mean of:RuntimeWarning")
def test_cython_grouper_series_bug_noncontig():
    arr = np.empty((100, 100))
    arr.fill(np.nan)
    obj = Series(arr[:, 0])
    inds = np.tile(range(10), 10)

    result = obj.groupby(inds).agg(Series.median)
    assert result.isna().all()


def test_series_grouper_noncontig_index():
    index = Index(tm.rands_array(10, 100))

    values = Series(np.random.randn(50), index=index[::2])
    labels = np.random.randint(0, 5, 50)

    # it works!
    grouped = values.groupby(labels)

    # accessing the index elements causes segfault
    f = lambda x: len(set(map(id, x.index)))
    grouped.agg(f)


def test_convert_objects_leave_decimal_alone():

    s = Series(range(5))
    labels = np.array(["a", "b", "c", "d", "e"], dtype="O")

    def convert_fast(x):
        return Decimal(str(x.mean()))

    def convert_force_pure(x):
        # base will be length 0
        assert len(x.values.base) > 0
        return Decimal(str(x.mean()))

    grouped = s.groupby(labels)

    result = grouped.agg(convert_fast)
    assert result.dtype == np.object_
    assert isinstance(result[0], Decimal)

    result = grouped.agg(convert_force_pure)
    assert result.dtype == np.object_
    assert isinstance(result[0], Decimal)


def test_groupby_dtype_inference_empty():
    # GH 6733
    df = DataFrame({"x": [], "range": np.arange(0, dtype="int64")})
    assert df["x"].dtype == np.float64

    result = df.groupby("x").first()
    exp_index = Index([], name="x", dtype=np.float64)
    expected = DataFrame({"range": Series([], index=exp_index, dtype="int64")})
    tm.assert_frame_equal(result, expected, by_blocks=True)


def test_groupby_unit64_float_conversion():
    #  GH: 30859 groupby converts unit64 to floats sometimes
    df = DataFrame({"first": [1], "second": [1], "value": [16148277970000000000]})
    result = df.groupby(["first", "second"])["value"].max()
    expected = Series(
        [16148277970000000000],
        MultiIndex.from_product([[1], [1]], names=["first", "second"]),
        name="value",
    )
    tm.assert_series_equal(result, expected)


def test_groupby_list_infer_array_like(df):
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby(list(df["A"])).mean()
        expected = df.groupby(df["A"]).mean()
    tm.assert_frame_equal(result, expected, check_names=False)

    with pytest.raises(KeyError, match=r"^'foo'$"):
        df.groupby(list(df["A"][:-1]))

    # pathological case of ambiguity
    df = DataFrame({"foo": [0, 1], "bar": [3, 4], "val": np.random.randn(2)})

    result = df.groupby(["foo", "bar"]).mean()
    expected = df.groupby([df["foo"], df["bar"]]).mean()[["val"]]


def test_groupby_keys_same_size_as_index():
    # GH 11185
    freq = "s"
    index = date_range(
        start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq
    )
    df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index)
    result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean()
    expected = df.set_index([df.index, "metric"]).astype(float)

    tm.assert_frame_equal(result, expected)


def test_groupby_one_row():
    # GH 11741
    msg = r"^'Z'$"
    df1 = DataFrame(np.random.randn(1, 4), columns=list("ABCD"))
    with pytest.raises(KeyError, match=msg):
        df1.groupby("Z")
    df2 = DataFrame(np.random.randn(2, 4), columns=list("ABCD"))
    with pytest.raises(KeyError, match=msg):
        df2.groupby("Z")


def test_groupby_nat_exclude():
    # GH 6992
    df = DataFrame(
        {
            "values": np.random.randn(8),
            "dt": [
                np.nan,
                Timestamp("2013-01-01"),
                np.nan,
                Timestamp("2013-02-01"),
                np.nan,
                Timestamp("2013-02-01"),
                np.nan,
                Timestamp("2013-01-01"),
            ],
            "str": [np.nan, "a", np.nan, "a", np.nan, "a", np.nan, "b"],
        }
    )
    grouped = df.groupby("dt")

    expected = [Index([1, 7]), Index([3, 5])]
    keys = sorted(grouped.groups.keys())
    assert len(keys) == 2
    for k, e in zip(keys, expected):
        # grouped.groups keys are np.datetime64 with system tz
        # not to be affected by tz, only compare values
        tm.assert_index_equal(grouped.groups[k], e)

    # confirm obj is not filtered
    tm.assert_frame_equal(grouped.grouper.groupings[0].obj, df)
    assert grouped.ngroups == 2

    expected = {
        Timestamp("2013-01-01 00:00:00"): np.array([1, 7], dtype=np.intp),
        Timestamp("2013-02-01 00:00:00"): np.array([3, 5], dtype=np.intp),
    }

    for k in grouped.indices:
        tm.assert_numpy_array_equal(grouped.indices[k], expected[k])

    tm.assert_frame_equal(grouped.get_group(Timestamp("2013-01-01")), df.iloc[[1, 7]])
    tm.assert_frame_equal(grouped.get_group(Timestamp("2013-02-01")), df.iloc[[3, 5]])

    with pytest.raises(KeyError, match=r"^NaT$"):
        grouped.get_group(pd.NaT)

    nan_df = DataFrame(
        {"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]}
    )
    assert nan_df["nan"].dtype == "float64"
    assert nan_df["nat"].dtype == "datetime64[ns]"

    for key in ["nan", "nat"]:
        grouped = nan_df.groupby(key)
        assert grouped.groups == {}
        assert grouped.ngroups == 0
        assert grouped.indices == {}
        with pytest.raises(KeyError, match=r"^nan$"):
            grouped.get_group(np.nan)
        with pytest.raises(KeyError, match=r"^NaT$"):
            grouped.get_group(pd.NaT)


def test_groupby_two_group_keys_all_nan():
    # GH #36842: Grouping over two group keys shouldn't raise an error
    df = DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]})
    result = df.groupby(["a", "b"]).indices
    assert result == {}


def test_groupby_2d_malformed():
    d = DataFrame(index=range(2))
    d["group"] = ["g1", "g2"]
    d["zeros"] = [0, 0]
    d["ones"] = [1, 1]
    d["label"] = ["l1", "l2"]
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        tmp = d.groupby(["group"]).mean()
    res_values = np.array([[0.0, 1.0], [0.0, 1.0]])
    tm.assert_index_equal(tmp.columns, Index(["zeros", "ones"]))
    tm.assert_numpy_array_equal(tmp.values, res_values)


def test_int32_overflow():
    B = np.concatenate((np.arange(10000), np.arange(10000), np.arange(5000)))
    A = np.arange(25000)
    df = DataFrame({"A": A, "B": B, "C": A, "D": B, "E": np.random.randn(25000)})

    left = df.groupby(["A", "B", "C", "D"]).sum()
    right = df.groupby(["D", "C", "B", "A"]).sum()
    assert len(left) == len(right)


def test_groupby_sort_multi():
    df = DataFrame(
        {
            "a": ["foo", "bar", "baz"],
            "b": [3, 2, 1],
            "c": [0, 1, 2],
            "d": np.random.randn(3),
        }
    )

    tups = [tuple(row) for row in df[["a", "b", "c"]].values]
    tups = com.asarray_tuplesafe(tups)
    result = df.groupby(["a", "b", "c"], sort=True).sum()
    tm.assert_numpy_array_equal(result.index.values, tups[[1, 2, 0]])

    tups = [tuple(row) for row in df[["c", "a", "b"]].values]
    tups = com.asarray_tuplesafe(tups)
    result = df.groupby(["c", "a", "b"], sort=True).sum()
    tm.assert_numpy_array_equal(result.index.values, tups)

    tups = [tuple(x) for x in df[["b", "c", "a"]].values]
    tups = com.asarray_tuplesafe(tups)
    result = df.groupby(["b", "c", "a"], sort=True).sum()
    tm.assert_numpy_array_equal(result.index.values, tups[[2, 1, 0]])

    df = DataFrame(
        {"a": [0, 1, 2, 0, 1, 2], "b": [0, 0, 0, 1, 1, 1], "d": np.random.randn(6)}
    )
    grouped = df.groupby(["a", "b"])["d"]
    result = grouped.sum()

    def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
        tups = [tuple(row) for row in df[keys].values]
        tups = com.asarray_tuplesafe(tups)
        expected = f(df.groupby(tups)[field])
        for k, v in expected.items():
            assert result[k] == v

    _check_groupby(df, result, ["a", "b"], "d")


def test_dont_clobber_name_column():
    df = DataFrame(
        {"key": ["a", "a", "a", "b", "b", "b"], "name": ["foo", "bar", "baz"] * 2}
    )

    result = df.groupby("key", group_keys=False).apply(lambda x: x)
    tm.assert_frame_equal(result, df)


def test_skip_group_keys():

    tsf = tm.makeTimeDataFrame()

    grouped = tsf.groupby(lambda x: x.month, group_keys=False)
    result = grouped.apply(lambda x: x.sort_values(by="A")[:3])

    pieces = [group.sort_values(by="A")[:3] for key, group in grouped]

    expected = pd.concat(pieces)
    tm.assert_frame_equal(result, expected)

    grouped = tsf["A"].groupby(lambda x: x.month, group_keys=False)
    result = grouped.apply(lambda x: x.sort_values()[:3])

    pieces = [group.sort_values()[:3] for key, group in grouped]

    expected = pd.concat(pieces)
    tm.assert_series_equal(result, expected)


def test_no_nonsense_name(float_frame):
    # GH #995
    s = float_frame["C"].copy()
    s.name = None

    result = s.groupby(float_frame["A"]).agg(np.sum)
    assert result.name is None


def test_multifunc_sum_bug():
    # GH #1065
    x = DataFrame(np.arange(9).reshape(3, 3))
    x["test"] = 0
    x["fl"] = [1.3, 1.5, 1.6]

    grouped = x.groupby("test")
    result = grouped.agg({"fl": "sum", 2: "size"})
    assert result["fl"].dtype == np.float64


def test_handle_dict_return_value(df):
    def f(group):
        return {"max": group.max(), "min": group.min()}

    def g(group):
        return Series({"max": group.max(), "min": group.min()})

    result = df.groupby("A")["C"].apply(f)
    expected = df.groupby("A")["C"].apply(g)

    assert isinstance(result, Series)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("grouper", ["A", ["A", "B"]])
def test_set_group_name(df, grouper):
    def f(group):
        assert group.name is not None
        return group

    def freduce(group):
        assert group.name is not None
        return group.sum()

    def foo(x):
        return freduce(x)

    grouped = df.groupby(grouper, group_keys=False)

    # make sure all these work
    grouped.apply(f)
    grouped.aggregate(freduce)
    grouped.aggregate({"C": freduce, "D": freduce})
    grouped.transform(f)

    grouped["C"].apply(f)
    grouped["C"].aggregate(freduce)
    grouped["C"].aggregate([freduce, foo])
    grouped["C"].transform(f)


def test_group_name_available_in_inference_pass():
    # gh-15062
    df = DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": np.arange(6)})

    names = []

    def f(group):
        names.append(group.name)
        return group.copy()

    df.groupby("a", sort=False, group_keys=False).apply(f)

    expected_names = [0, 1, 2]
    assert names == expected_names


def test_no_dummy_key_names(df):
    # see gh-1291
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby(df["A"].values).sum()
    assert result.index.name is None

    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.groupby([df["A"].values, df["B"].values]).sum()
    assert result.index.names == (None, None)


def test_groupby_sort_multiindex_series():
    # series multiindex groupby sort argument was not being passed through
    # _compress_group_index
    # GH 9444
    index = MultiIndex(
        levels=[[1, 2], [1, 2]],
        codes=[[0, 0, 0, 0, 1, 1], [1, 1, 0, 0, 0, 0]],
        names=["a", "b"],
    )
    mseries = Series([0, 1, 2, 3, 4, 5], index=index)
    index = MultiIndex(
        levels=[[1, 2], [1, 2]], codes=[[0, 0, 1], [1, 0, 0]], names=["a", "b"]
    )
    mseries_result = Series([0, 2, 4], index=index)

    result = mseries.groupby(level=["a", "b"], sort=False).first()
    tm.assert_series_equal(result, mseries_result)
    result = mseries.groupby(level=["a", "b"], sort=True).first()
    tm.assert_series_equal(result, mseries_result.sort_index())


def test_groupby_reindex_inside_function():

    periods = 1000
    ind = date_range(start="2012/1/1", freq="5min", periods=periods)
    df = DataFrame({"high": np.arange(periods), "low": np.arange(periods)}, index=ind)

    def agg_before(func, fix=False):
        """
        Run an aggregate func on the subset of data.
        """

        def _func(data):
            d = data.loc[data.index.map(lambda x: x.hour < 11)].dropna()
            if fix:
                data[data.index[0]]
            if len(d) == 0:
                return None
            return func(d)

        return _func

    grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
    closure_bad = grouped.agg({"high": agg_before(np.max)})
    closure_good = grouped.agg({"high": agg_before(np.max, True)})

    tm.assert_frame_equal(closure_bad, closure_good)


def test_groupby_multiindex_missing_pair():
    # GH9049
    df = DataFrame(
        {
            "group1": ["a", "a", "a", "b"],
            "group2": ["c", "c", "d", "c"],
            "value": [1, 1, 1, 5],
        }
    )
    df = df.set_index(["group1", "group2"])
    df_grouped = df.groupby(level=["group1", "group2"], sort=True)

    res = df_grouped.agg("sum")
    idx = MultiIndex.from_tuples(
        [("a", "c"), ("a", "d"), ("b", "c")], names=["group1", "group2"]
    )
    exp = DataFrame([[2], [1], [5]], index=idx, columns=["value"])

    tm.assert_frame_equal(res, exp)


def test_groupby_multiindex_not_lexsorted():
    # GH 11640

    # define the lexsorted version
    lexsorted_mi = MultiIndex.from_tuples(
        [("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
    )
    lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
    assert lexsorted_df.columns._is_lexsorted()

    # define the non-lexsorted version
    not_lexsorted_df = DataFrame(
        columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
    )
    not_lexsorted_df = not_lexsorted_df.pivot_table(
        index="a", columns=["b", "c"], values="d"
    )
    not_lexsorted_df = not_lexsorted_df.reset_index()
    assert not not_lexsorted_df.columns._is_lexsorted()

    # compare the results
    tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)

    expected = lexsorted_df.groupby("a").mean()
    with tm.assert_produces_warning(PerformanceWarning):
        result = not_lexsorted_df.groupby("a").mean()
    tm.assert_frame_equal(expected, result)

    # a transforming function should work regardless of sort
    # GH 14776
    df = DataFrame(
        {"x": ["a", "a", "b", "a"], "y": [1, 1, 2, 2], "z": [1, 2, 3, 4]}
    ).set_index(["x", "y"])
    assert not df.index._is_lexsorted()

    for level in [0, 1, [0, 1]]:
        for sort in [False, True]:
            result = df.groupby(level=level, sort=sort, group_keys=False).apply(
                DataFrame.drop_duplicates
            )
            expected = df
            tm.assert_frame_equal(expected, result)

            result = (
                df.sort_index()
                .groupby(level=level, sort=sort, group_keys=False)
                .apply(DataFrame.drop_duplicates)
            )
            expected = df.sort_index()
            tm.assert_frame_equal(expected, result)


def test_index_label_overlaps_location():
    # checking we don't have any label/location confusion in the
    # wake of GH5375
    df = DataFrame(list("ABCDE"), index=[2, 0, 2, 1, 1])
    g = df.groupby(list("ababb"))
    actual = g.filter(lambda x: len(x) > 2)
    expected = df.iloc[[1, 3, 4]]
    tm.assert_frame_equal(actual, expected)

    ser = df[0]
    g = ser.groupby(list("ababb"))
    actual = g.filter(lambda x: len(x) > 2)
    expected = ser.take([1, 3, 4])
    tm.assert_series_equal(actual, expected)

    #  and again, with a generic Index of floats
    df.index = df.index.astype(float)
    g = df.groupby(list("ababb"))
    actual = g.filter(lambda x: len(x) > 2)
    expected = df.iloc[[1, 3, 4]]
    tm.assert_frame_equal(actual, expected)

    ser = df[0]
    g = ser.groupby(list("ababb"))
    actual = g.filter(lambda x: len(x) > 2)
    expected = ser.take([1, 3, 4])
    tm.assert_series_equal(actual, expected)


def test_transform_doesnt_clobber_ints():
    # GH 7972
    n = 6
    x = np.arange(n)
    df = DataFrame({"a": x // 2, "b": 2.0 * x, "c": 3.0 * x})
    df2 = DataFrame({"a": x // 2 * 1.0, "b": 2.0 * x, "c": 3.0 * x})

    gb = df.groupby("a")
    result = gb.transform("mean")

    gb2 = df2.groupby("a")
    expected = gb2.transform("mean")
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "sort_column",
    ["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]],
)
@pytest.mark.parametrize(
    "group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]]
)
def test_groupby_preserves_sort(sort_column, group_column):
    # Test to ensure that groupby always preserves sort order of original
    # object. Issue #8588 and #9651

    df = DataFrame(
        {
            "int_groups": [3, 1, 0, 1, 0, 3, 3, 3],
            "string_groups": ["z", "a", "z", "a", "a", "g", "g", "g"],
            "ints": [8, 7, 4, 5, 2, 9, 1, 1],
            "floats": [2.3, 5.3, 6.2, -2.4, 2.2, 1.1, 1.1, 5],
            "strings": ["z", "d", "a", "e", "word", "word2", "42", "47"],
        }
    )

    # Try sorting on different types and with different group types

    df = df.sort_values(by=sort_column)
    g = df.groupby(group_column)

    def test_sort(x):
        tm.assert_frame_equal(x, x.sort_values(by=sort_column))

    g.apply(test_sort)


def test_pivot_table_values_key_error():
    # This test is designed to replicate the error in issue #14938
    df = DataFrame(
        {
            "eventDate": date_range(datetime.today(), periods=20, freq="M").tolist(),
            "thename": range(0, 20),
        }
    )

    df["year"] = df.set_index("eventDate").index.year
    df["month"] = df.set_index("eventDate").index.month

    with pytest.raises(KeyError, match="'badname'"):
        df.reset_index().pivot_table(
            index="year", columns="month", values="badname", aggfunc="count"
        )


@pytest.mark.parametrize("columns", ["C", ["C"]])
@pytest.mark.parametrize("keys", [["A"], ["A", "B"]])
@pytest.mark.parametrize(
    "values",
    [
        [True],
        [0],
        [0.0],
        ["a"],
        Categorical([0]),
        [to_datetime(0)],
        date_range(0, 1, 1, tz="US/Eastern"),
        pd.array([0], dtype="Int64"),
        pd.array([0], dtype="Float64"),
        pd.array([False], dtype="boolean"),
    ],
    ids=[
        "bool",
        "int",
        "float",
        "str",
        "cat",
        "dt64",
        "dt64tz",
        "Int64",
        "Float64",
        "boolean",
    ],
)
@pytest.mark.parametrize("method", ["attr", "agg", "apply"])
@pytest.mark.parametrize(
    "op", ["idxmax", "idxmin", "mad", "min", "max", "sum", "prod", "skew"]
)
@pytest.mark.filterwarnings("ignore:Dropping invalid columns:FutureWarning")
@pytest.mark.filterwarnings("ignore:.*Select only valid:FutureWarning")
def test_empty_groupby(columns, keys, values, method, op, request, using_array_manager):
    # GH8093 & GH26411
    override_dtype = None

    if (
        isinstance(values, Categorical)
        and not isinstance(columns, list)
        and op in ["sum", "prod", "skew", "mad"]
    ):
        # handled below GH#41291

        if using_array_manager and op == "mad":
            right_msg = "Cannot interpret 'CategoricalDtype.* as a data type"
            msg = "Regex pattern \"'Categorical' does not implement.*" + right_msg
            mark = pytest.mark.xfail(raises=AssertionError, match=msg)
            request.node.add_marker(mark)

    elif (
        isinstance(values, Categorical)
        and len(keys) == 1
        and op in ["idxmax", "idxmin"]
    ):
        mark = pytest.mark.xfail(
            raises=ValueError, match="attempt to get arg(min|max) of an empty sequence"
        )
        request.node.add_marker(mark)
    elif (
        isinstance(values, Categorical)
        and len(keys) == 1
        and not isinstance(columns, list)
    ):
        mark = pytest.mark.xfail(
            raises=TypeError, match="'Categorical' does not implement"
        )
        request.node.add_marker(mark)
    elif isinstance(values, Categorical) and len(keys) == 1 and op in ["sum", "prod"]:
        mark = pytest.mark.xfail(
            raises=AssertionError, match="(DataFrame|Series) are different"
        )
        request.node.add_marker(mark)
    elif (
        isinstance(values, Categorical)
        and len(keys) == 2
        and op in ["min", "max", "sum"]
    ):
        mark = pytest.mark.xfail(
            raises=AssertionError, match="(DataFrame|Series) are different"
        )
        request.node.add_marker(mark)

    elif (
        op == "mad"
        and not isinstance(columns, list)
        and isinstance(values, pd.DatetimeIndex)
        and values.tz is not None
        and using_array_manager
    ):
        mark = pytest.mark.xfail(
            raises=TypeError,
            match=r"Cannot interpret 'datetime64\[ns, US/Eastern\]' as a data type",
        )
        request.node.add_marker(mark)

    elif isinstance(values, BooleanArray) and op in ["sum", "prod"]:
        # We expect to get Int64 back for these
        override_dtype = "Int64"

    if isinstance(values[0], bool) and op in ("prod", "sum"):
        # sum/product of bools is an integer
        override_dtype = "int64"

    df = DataFrame({"A": values, "B": values, "C": values}, columns=list("ABC"))

    if hasattr(values, "dtype"):
        # check that we did the construction right
        assert (df.dtypes == values.dtype).all()

    df = df.iloc[:0]

    gb = df.groupby(keys, group_keys=False)[columns]

    def get_result():
        warn = FutureWarning if op == "mad" else None
        with tm.assert_produces_warning(
            warn, match="The 'mad' method is deprecated", raise_on_extra_warnings=False
        ):
            if method == "attr":
                return getattr(gb, op)()
            else:
                return getattr(gb, method)(op)

    if columns == "C":
        # i.e. SeriesGroupBy
        if op in ["prod", "sum", "skew"]:
            # ops that require more than just ordered-ness
            if df.dtypes[0].kind == "M":
                # GH#41291
                # datetime64 -> prod and sum are invalid
                if op == "skew":
                    msg = "does not support reduction 'skew'"
                else:
                    msg = "datetime64 type does not support"
                with pytest.raises(TypeError, match=msg):
                    get_result()

                return
        if op in ["prod", "sum", "skew", "mad"]:
            if isinstance(values, Categorical):
                # GH#41291
                if op == "mad":
                    # mad calls mean, which Categorical doesn't implement
                    msg = "does not support reduction 'mean'"
                elif op == "skew":
                    msg = f"does not support reduction '{op}'"
                else:
                    msg = "category type does not support"
                with pytest.raises(TypeError, match=msg):
                    get_result()

                return
    else:
        # ie. DataFrameGroupBy
        if op in ["prod", "sum"]:
            # ops that require more than just ordered-ness
            if df.dtypes[0].kind == "M":
                # GH#41291
                # datetime64 -> prod and sum are invalid
                result = get_result()

                # with numeric_only=True, these are dropped, and we get
                # an empty DataFrame back
                expected = df.set_index(keys)[[]]
                tm.assert_equal(result, expected)
                return

            elif isinstance(values, Categorical):
                # GH#41291
                # Categorical doesn't implement sum or prod
                result = get_result()

                # with numeric_only=True, these are dropped, and we get
                # an empty DataFrame back
                expected = df.set_index(keys)[[]]
                if len(keys) != 1 and op == "prod":
                    # TODO: why just prod and not sum?
                    # Categorical is special without 'observed=True'
                    lev = Categorical([0], dtype=values.dtype)
                    mi = MultiIndex.from_product([lev, lev], names=["A", "B"])
                    expected = DataFrame([], columns=[], index=mi)

                tm.assert_equal(result, expected)
                return

            elif df.dtypes[0] == object:
                # FIXME: the test is actually wrong here, xref #41341
                result = get_result()
                # In this case we have list-of-list, will raise TypeError,
                # and subsequently be dropped as nuisance columns
                expected = df.set_index(keys)[[]]
                tm.assert_equal(result, expected)
                return

        if (
            op in ["mad", "min", "max", "skew"]
            and isinstance(values, Categorical)
            and len(keys) == 1
        ):
            # Categorical doesn't implement, so with numeric_only=True
            #  these are dropped and we get an empty DataFrame back
            result = get_result()
            expected = df.set_index(keys)[[]]

            # with numeric_only=True, these are dropped, and we get
            # an empty DataFrame back
            if len(keys) != 1:
                # Categorical is special without 'observed=True'
                lev = Categorical([0], dtype=values.dtype)
                mi = MultiIndex.from_product([lev, lev], names=keys)
                expected = DataFrame([], columns=[], index=mi)
            else:
                # all columns are dropped, but we end up with one row
                # Categorical is special without 'observed=True'
                lev = Categorical([0], dtype=values.dtype)
                ci = Index(lev, name=keys[0])
                expected = DataFrame([], columns=[], index=ci)
            # expected = df.set_index(keys)[columns]

            tm.assert_equal(result, expected)
            return

    result = get_result()
    expected = df.set_index(keys)[columns]
    if override_dtype is not None:
        expected = expected.astype(override_dtype)
    if len(keys) == 1:
        expected.index.name = keys[0]
    tm.assert_equal(result, expected)


def test_empty_groupby_apply_nonunique_columns():
    # GH#44417
    df = DataFrame(np.random.randn(0, 4))
    df[3] = df[3].astype(np.int64)
    df.columns = [0, 1, 2, 0]
    gb = df.groupby(df[1], group_keys=False)
    res = gb.apply(lambda x: x)
    assert (res.dtypes == df.dtypes).all()


def test_tuple_as_grouping():
    # https://github.com/pandas-dev/pandas/issues/18314
    df = DataFrame(
        {
            ("a", "b"): [1, 1, 1, 1],
            "a": [2, 2, 2, 2],
            "b": [2, 2, 2, 2],
            "c": [1, 1, 1, 1],
        }
    )

    with pytest.raises(KeyError, match=r"('a', 'b')"):
        df[["a", "b", "c"]].groupby(("a", "b"))

    result = df.groupby(("a", "b"))["c"].sum()
    expected = Series([4], name="c", index=Index([1], name=("a", "b")))
    tm.assert_series_equal(result, expected)


def test_tuple_correct_keyerror():
    # https://github.com/pandas-dev/pandas/issues/18798
    df = DataFrame(1, index=range(3), columns=MultiIndex.from_product([[1, 2], [3, 4]]))
    with pytest.raises(KeyError, match=r"^\(7, 8\)$"):
        df.groupby((7, 8)).mean()


def test_groupby_agg_ohlc_non_first():
    # GH 21716
    df = DataFrame(
        [[1], [1]],
        columns=Index(["foo"], name="mycols"),
        index=date_range("2018-01-01", periods=2, freq="D", name="dti"),
    )

    expected = DataFrame(
        [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]],
        columns=MultiIndex.from_tuples(
            (
                ("foo", "sum", "foo"),
                ("foo", "ohlc", "open"),
                ("foo", "ohlc", "high"),
                ("foo", "ohlc", "low"),
                ("foo", "ohlc", "close"),
            ),
            names=["mycols", None, None],
        ),
        index=date_range("2018-01-01", periods=2, freq="D", name="dti"),
    )

    result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"])

    tm.assert_frame_equal(result, expected)


def test_groupby_multiindex_nat():
    # GH 9236
    values = [
        (pd.NaT, "a"),
        (datetime(2012, 1, 2), "a"),
        (datetime(2012, 1, 2), "b"),
        (datetime(2012, 1, 3), "a"),
    ]
    mi = MultiIndex.from_tuples(values, names=["date", None])
    ser = Series([3, 2, 2.5, 4], index=mi)

    result = ser.groupby(level=1).mean()
    expected = Series([3.0, 2.5], index=["a", "b"])
    tm.assert_series_equal(result, expected)


def test_groupby_empty_list_raises():
    # GH 5289
    values = zip(range(10), range(10))
    df = DataFrame(values, columns=["apple", "b"])
    msg = "Grouper and axis must be same length"
    with pytest.raises(ValueError, match=msg):
        df.groupby([[]])


def test_groupby_multiindex_series_keys_len_equal_group_axis():
    # GH 25704
    index_array = [["x", "x"], ["a", "b"], ["k", "k"]]
    index_names = ["first", "second", "third"]
    ri = MultiIndex.from_arrays(index_array, names=index_names)
    s = Series(data=[1, 2], index=ri)
    result = s.groupby(["first", "third"]).sum()

    index_array = [["x"], ["k"]]
    index_names = ["first", "third"]
    ei = MultiIndex.from_arrays(index_array, names=index_names)
    expected = Series([3], index=ei)

    tm.assert_series_equal(result, expected)


def test_groupby_groups_in_BaseGrouper():
    # GH 26326
    # Test if DataFrame grouped with a pandas.Grouper has correct groups
    mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"])
    df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
    result = df.groupby([Grouper(level="alpha"), "beta"])
    expected = df.groupby(["alpha", "beta"])
    assert result.groups == expected.groups

    result = df.groupby(["beta", Grouper(level="alpha")])
    expected = df.groupby(["beta", "alpha"])
    assert result.groups == expected.groups


@pytest.mark.parametrize("group_name", ["x", ["x"]])
def test_groupby_axis_1(group_name):
    # GH 27614
    df = DataFrame(
        np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20]
    )
    df.index.name = "y"
    df.columns.name = "x"

    results = df.groupby(group_name, axis=1).sum()
    expected = df.T.groupby(group_name).sum().T
    tm.assert_frame_equal(results, expected)

    # test on MI column
    iterables = [["bar", "baz", "foo"], ["one", "two"]]
    mi = MultiIndex.from_product(iterables=iterables, names=["x", "x1"])
    df = DataFrame(np.arange(18).reshape(3, 6), index=[0, 1, 0], columns=mi)
    results = df.groupby(group_name, axis=1).sum()
    expected = df.T.groupby(group_name).sum().T
    tm.assert_frame_equal(results, expected)


@pytest.mark.parametrize(
    "op, expected",
    [
        (
            "shift",
            {
                "time": [
                    None,
                    None,
                    Timestamp("2019-01-01 12:00:00"),
                    Timestamp("2019-01-01 12:30:00"),
                    None,
                    None,
                ]
            },
        ),
        (
            "bfill",
            {
                "time": [
                    Timestamp("2019-01-01 12:00:00"),
                    Timestamp("2019-01-01 12:30:00"),
                    Timestamp("2019-01-01 14:00:00"),
                    Timestamp("2019-01-01 14:30:00"),
                    Timestamp("2019-01-01 14:00:00"),
                    Timestamp("2019-01-01 14:30:00"),
                ]
            },
        ),
        (
            "ffill",
            {
                "time": [
                    Timestamp("2019-01-01 12:00:00"),
                    Timestamp("2019-01-01 12:30:00"),
                    Timestamp("2019-01-01 12:00:00"),
                    Timestamp("2019-01-01 12:30:00"),
                    Timestamp("2019-01-01 14:00:00"),
                    Timestamp("2019-01-01 14:30:00"),
                ]
            },
        ),
    ],
)
def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
    # GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
    tz = tz_naive_fixture
    data = {
        "id": ["A", "B", "A", "B", "A", "B"],
        "time": [
            Timestamp("2019-01-01 12:00:00"),
            Timestamp("2019-01-01 12:30:00"),
            None,
            None,
            Timestamp("2019-01-01 14:00:00"),
            Timestamp("2019-01-01 14:30:00"),
        ],
    }
    df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))

    grouped = df.groupby("id")
    result = getattr(grouped, op)()
    expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
    tm.assert_frame_equal(result, expected)


def test_groupby_only_none_group():
    # see GH21624
    # this was crashing with "ValueError: Length of passed values is 1, index implies 0"
    df = DataFrame({"g": [None], "x": 1})
    actual = df.groupby("g")["x"].transform("sum")
    expected = Series([np.nan], name="x")

    tm.assert_series_equal(actual, expected)


def test_groupby_duplicate_index():
    # GH#29189 the groupby call here used to raise
    ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
    gb = ser.groupby(level=0)

    result = gb.mean()
    expected = Series([2, 5.5, 8], index=[2.0, 4.0, 5.0])
    tm.assert_series_equal(result, expected)


@pytest.mark.filterwarnings("ignore:.*is deprecated.*:FutureWarning")
def test_group_on_empty_multiindex(transformation_func, request):
    # GH 47787
    # With one row, those are transforms so the schema should be the same
    if transformation_func == "tshift":
        mark = pytest.mark.xfail(raises=NotImplementedError)
        request.node.add_marker(mark)
    df = DataFrame(
        data=[[1, Timestamp("today"), 3, 4]],
        columns=["col_1", "col_2", "col_3", "col_4"],
    )
    df["col_3"] = df["col_3"].astype(int)
    df["col_4"] = df["col_4"].astype(int)
    df = df.set_index(["col_1", "col_2"])
    if transformation_func == "fillna":
        args = ("ffill",)
    elif transformation_func == "tshift":
        args = (1, "D")
    else:
        args = ()
    result = df.iloc[:0].groupby(["col_1"]).transform(transformation_func, *args)
    expected = df.groupby(["col_1"]).transform(transformation_func, *args).iloc[:0]
    if transformation_func in ("diff", "shift"):
        expected = expected.astype(int)
    tm.assert_equal(result, expected)

    result = (
        df["col_3"].iloc[:0].groupby(["col_1"]).transform(transformation_func, *args)
    )
    expected = (
        df["col_3"].groupby(["col_1"]).transform(transformation_func, *args).iloc[:0]
    )
    if transformation_func in ("diff", "shift"):
        expected = expected.astype(int)
    tm.assert_equal(result, expected)


@pytest.mark.parametrize(
    "idx",
    [
        Index(["a", "a"], name="foo"),
        MultiIndex.from_tuples((("a", "a"), ("a", "a")), names=["foo", "bar"]),
    ],
)
@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
def test_dup_labels_output_shape(groupby_func, idx):
    if groupby_func in {"size", "ngroup", "cumcount"}:
        pytest.skip(f"Not applicable for {groupby_func}")
    # TODO(2.0) Remove after pad/backfill deprecation enforced
    groupby_func = maybe_normalize_deprecated_kernels(groupby_func)
    warn = FutureWarning if groupby_func in ("mad", "tshift") else None

    df = DataFrame([[1, 1]], columns=idx)
    grp_by = df.groupby([0])

    if groupby_func == "tshift":
        df.index = [Timestamp("today")]
        # args.extend([1, "D"])
    args = get_groupby_method_args(groupby_func, df)

    with tm.assert_produces_warning(warn, match="is deprecated"):
        result = getattr(grp_by, groupby_func)(*args)

    assert result.shape == (1, 2)
    tm.assert_index_equal(result.columns, idx)


def test_groupby_crash_on_nunique(axis):
    # Fix following 30253
    dti = date_range("2016-01-01", periods=2, name="foo")
    df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})
    df.columns.names = ("bar", "baz")
    df.index = dti

    axis_number = df._get_axis_number(axis)
    if not axis_number:
        df = df.T

    gb = df.groupby(axis=axis_number, level=0)
    result = gb.nunique()

    expected = DataFrame({"A": [1, 2], "D": [1, 1]}, index=dti)
    expected.columns.name = "bar"
    if not axis_number:
        expected = expected.T

    tm.assert_frame_equal(result, expected)

    if axis_number == 0:
        # same thing, but empty columns
        gb2 = df[[]].groupby(axis=axis_number, level=0)
        exp = expected[[]]
    else:
        # same thing, but empty rows
        gb2 = df.loc[[]].groupby(axis=axis_number, level=0)
        # default for empty when we can't infer a dtype is float64
        exp = expected.loc[[]].astype(np.float64)

    res = gb2.nunique()
    tm.assert_frame_equal(res, exp)


def test_groupby_list_level():
    # GH 9790
    expected = DataFrame(np.arange(0, 9).reshape(3, 3), dtype=float)
    result = expected.groupby(level=[0]).mean()
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "max_seq_items, expected",
    [
        (5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"),
        (4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"),
        (1, "{0: [0], ...}"),
    ],
)
def test_groups_repr_truncates(max_seq_items, expected):
    # GH 1135
    df = DataFrame(np.random.randn(5, 1))
    df["a"] = df.index

    with pd.option_context("display.max_seq_items", max_seq_items):
        result = df.groupby("a").groups.__repr__()
        assert result == expected

        result = df.groupby(np.array(df.a)).groups.__repr__()
        assert result == expected


def test_group_on_two_row_multiindex_returns_one_tuple_key():
    # GH 18451
    df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}])
    df = df.set_index(["a", "b"])

    grp = df.groupby(["a", "b"])
    result = grp.indices
    expected = {(1, 2): np.array([0, 1], dtype=np.int64)}

    assert len(result) == 1
    key = (1, 2)
    assert (result[key] == expected[key]).all()


@pytest.mark.parametrize(
    "klass, attr, value",
    [
        (DataFrame, "level", "a"),
        (DataFrame, "as_index", False),
        (DataFrame, "sort", False),
        (DataFrame, "group_keys", False),
        (DataFrame, "squeeze", True),
        (DataFrame, "observed", True),
        (DataFrame, "dropna", False),
        pytest.param(
            Series,
            "axis",
            1,
            marks=pytest.mark.xfail(
                reason="GH 35443: Attribute currently not passed on to series"
            ),
        ),
        (Series, "level", "a"),
        (Series, "as_index", False),
        (Series, "sort", False),
        (Series, "group_keys", False),
        (Series, "squeeze", True),
        (Series, "observed", True),
        (Series, "dropna", False),
    ],
)
@pytest.mark.filterwarnings(
    "ignore:The `squeeze` parameter is deprecated:FutureWarning"
)
def test_subsetting_columns_keeps_attrs(klass, attr, value):
    # GH 9959 - When subsetting columns, don't drop attributes
    df = DataFrame({"a": [1], "b": [2], "c": [3]})
    if attr != "axis":
        df = df.set_index("a")

    expected = df.groupby("a", **{attr: value})
    result = expected[["b"]] if klass is DataFrame else expected["b"]
    assert getattr(result, attr) == getattr(expected, attr)


def test_subsetting_columns_axis_1():
    # GH 37725
    g = DataFrame({"A": [1], "B": [2], "C": [3]}).groupby([0, 0, 1], axis=1)
    match = "Cannot subset columns when using axis=1"
    with pytest.raises(ValueError, match=match):
        g[["A", "B"]].sum()


@pytest.mark.parametrize("func", ["sum", "any", "shift"])
def test_groupby_column_index_name_lost(func):
    # GH: 29764 groupby loses index sometimes
    expected = Index(["a"], name="idx")
    df = DataFrame([[1]], columns=expected)
    df_grouped = df.groupby([1])
    result = getattr(df_grouped, func)().columns
    tm.assert_index_equal(result, expected)


def test_groupby_duplicate_columns():
    # GH: 31735
    df = DataFrame(
        {"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]}
    ).astype(object)
    df.columns = ["A", "B", "B"]
    result = df.groupby([0, 0, 0, 0]).min()
    expected = DataFrame([["e", "a", 1]], columns=["A", "B", "B"])
    tm.assert_frame_equal(result, expected)


def test_groupby_series_with_tuple_name():
    # GH 37755
    ser = Series([1, 2, 3, 4], index=[1, 1, 2, 2], name=("a", "a"))
    ser.index.name = ("b", "b")
    result = ser.groupby(level=0).last()
    expected = Series([2, 4], index=[1, 2], name=("a", "a"))
    expected.index.name = ("b", "b")
    tm.assert_series_equal(result, expected)


@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
@pytest.mark.parametrize(
    "func, values", [("sum", [97.0, 98.0]), ("mean", [24.25, 24.5])]
)
def test_groupby_numerical_stability_sum_mean(func, values):
    # GH#38778
    data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
    df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
    result = getattr(df.groupby("group"), func)()
    expected = DataFrame({"a": values, "b": values}, index=Index([1, 2], name="group"))
    tm.assert_frame_equal(result, expected)


@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
def test_groupby_numerical_stability_cumsum():
    # GH#38934
    data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
    df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
    result = df.groupby("group").cumsum()
    exp_data = (
        [1e16] * 2 + [1e16 + 96, 1e16 + 98] + [5e15 + 97, 5e15 + 98] + [97.0, 98.0]
    )
    expected = DataFrame({"a": exp_data, "b": exp_data})
    tm.assert_frame_equal(result, expected, check_exact=True)


def test_groupby_cumsum_skipna_false():
    # GH#46216 don't propagate np.nan above the diagonal
    arr = np.random.randn(5, 5)
    df = DataFrame(arr)
    for i in range(5):
        df.iloc[i, i] = np.nan

    df["A"] = 1
    gb = df.groupby("A")

    res = gb.cumsum(skipna=False)

    expected = df[[0, 1, 2, 3, 4]].cumsum(skipna=False)
    tm.assert_frame_equal(res, expected)


def test_groupby_cumsum_timedelta64():
    # GH#46216 don't ignore is_datetimelike in libgroupby.group_cumsum
    dti = date_range("2016-01-01", periods=5)
    ser = Series(dti) - dti[0]
    ser[2] = pd.NaT

    df = DataFrame({"A": 1, "B": ser})
    gb = df.groupby("A")

    res = gb.cumsum(numeric_only=False, skipna=True)
    exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, ser[4], ser[4] * 2]})
    tm.assert_frame_equal(res, exp)

    res = gb.cumsum(numeric_only=False, skipna=False)
    exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, pd.NaT, pd.NaT]})
    tm.assert_frame_equal(res, exp)


def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex):
    dups = rand_series_with_duplicate_datetimeindex
    result = dups.groupby(level=0).mean()
    expected = dups.groupby(dups.index).mean()
    tm.assert_series_equal(result, expected)


def test_groupby_all_nan_groups_drop():
    # GH 15036
    s = Series([1, 2, 3], [np.nan, np.nan, np.nan])
    result = s.groupby(s.index).sum()
    expected = Series([], index=Index([], dtype=np.float64), dtype=np.int64)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("numeric_only", [True, False])
def test_groupby_empty_multi_column(as_index, numeric_only):
    # GH 15106 & GH 41998
    df = DataFrame(data=[], columns=["A", "B", "C"])
    gb = df.groupby(["A", "B"], as_index=as_index)
    result = gb.sum(numeric_only=numeric_only)
    if as_index:
        index = MultiIndex([[], []], [[], []], names=["A", "B"])
        columns = ["C"] if not numeric_only else []
    else:
        index = RangeIndex(0)
        columns = ["A", "B", "C"] if not numeric_only else ["A", "B"]
    expected = DataFrame([], columns=columns, index=index)
    tm.assert_frame_equal(result, expected)


def test_groupby_aggregation_non_numeric_dtype():
    # GH #43108
    df = DataFrame(
        [["M", [1]], ["M", [1]], ["W", [10]], ["W", [20]]], columns=["MW", "v"]
    )

    expected = DataFrame(
        {
            "v": [[1, 1], [10, 20]],
        },
        index=Index(["M", "W"], dtype="object", name="MW"),
    )

    gb = df.groupby(by=["MW"])
    result = gb.sum()
    tm.assert_frame_equal(result, expected)


def test_groupby_aggregation_multi_non_numeric_dtype():
    # GH #42395
    df = DataFrame(
        {
            "x": [1, 0, 1, 1, 0],
            "y": [Timedelta(i, "days") for i in range(1, 6)],
            "z": [Timedelta(i * 10, "days") for i in range(1, 6)],
        }
    )

    expected = DataFrame(
        {
            "y": [Timedelta(i, "days") for i in range(7, 9)],
            "z": [Timedelta(i * 10, "days") for i in range(7, 9)],
        },
        index=Index([0, 1], dtype="int64", name="x"),
    )

    gb = df.groupby(by=["x"])
    result = gb.sum()
    tm.assert_frame_equal(result, expected)


def test_groupby_aggregation_numeric_with_non_numeric_dtype():
    # GH #43108
    df = DataFrame(
        {
            "x": [1, 0, 1, 1, 0],
            "y": [Timedelta(i, "days") for i in range(1, 6)],
            "z": list(range(1, 6)),
        }
    )

    expected = DataFrame(
        {"z": [7, 8]},
        index=Index([0, 1], dtype="int64", name="x"),
    )

    gb = df.groupby(by=["x"])
    msg = "The default value of numeric_only"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = gb.sum()
    tm.assert_frame_equal(result, expected)


def test_groupby_filtered_df_std():
    # GH 16174
    dicts = [
        {"filter_col": False, "groupby_col": True, "bool_col": True, "float_col": 10.5},
        {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 20.5},
        {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 30.5},
    ]
    df = DataFrame(dicts)

    df_filter = df[df["filter_col"] == True]  # noqa:E712
    dfgb = df_filter.groupby("groupby_col")
    result = dfgb.std()
    expected = DataFrame(
        [[0.0, 0.0, 7.071068]],
        columns=["filter_col", "bool_col", "float_col"],
        index=Index([True], name="groupby_col"),
    )
    tm.assert_frame_equal(result, expected)


def test_datetime_categorical_multikey_groupby_indices():
    # GH 26859
    df = DataFrame(
        {
            "a": Series(list("abc")),
            "b": Series(
                to_datetime(["2018-01-01", "2018-02-01", "2018-03-01"]),
                dtype="category",
            ),
            "c": Categorical.from_codes([-1, 0, 1], categories=[0, 1]),
        }
    )
    result = df.groupby(["a", "b"]).indices
    expected = {
        ("a", Timestamp("2018-01-01 00:00:00")): np.array([0]),
        ("b", Timestamp("2018-02-01 00:00:00")): np.array([1]),
        ("c", Timestamp("2018-03-01 00:00:00")): np.array([2]),
    }
    assert result == expected


def test_rolling_wrong_param_min_period():
    # GH34037
    name_l = ["Alice"] * 5 + ["Bob"] * 5
    val_l = [np.nan, np.nan, 1, 2, 3] + [np.nan, 1, 2, 3, 4]
    test_df = DataFrame([name_l, val_l]).T
    test_df.columns = ["name", "val"]

    result_error_msg = r"__init__\(\) got an unexpected keyword argument 'min_period'"
    with pytest.raises(TypeError, match=result_error_msg):
        test_df.groupby("name")["val"].rolling(window=2, min_period=1).sum()


def test_pad_backfill_deprecation():
    # GH 33396
    s = Series([1, 2, 3])
    with tm.assert_produces_warning(FutureWarning, match="backfill"):
        s.groupby(level=0).backfill()
    with tm.assert_produces_warning(FutureWarning, match="pad"):
        s.groupby(level=0).pad()


def test_by_column_values_with_same_starting_value():
    # GH29635
    df = DataFrame(
        {
            "Name": ["Thomas", "Thomas", "Thomas John"],
            "Credit": [1200, 1300, 900],
            "Mood": ["sad", "happy", "happy"],
        }
    )
    aggregate_details = {"Mood": Series.mode, "Credit": "sum"}

    result = df.groupby(["Name"]).agg(aggregate_details)
    expected_result = DataFrame(
        {
            "Mood": [["happy", "sad"], "happy"],
            "Credit": [2500, 900],
            "Name": ["Thomas", "Thomas John"],
        }
    ).set_index("Name")

    tm.assert_frame_equal(result, expected_result)


def test_groupby_none_in_first_mi_level():
    # GH#47348
    arr = [[None, 1, 0, 1], [2, 3, 2, 3]]
    ser = Series(1, index=MultiIndex.from_arrays(arr, names=["a", "b"]))
    result = ser.groupby(level=[0, 1]).sum()
    expected = Series(
        [1, 2], MultiIndex.from_tuples([(0.0, 2), (1.0, 3)], names=["a", "b"])
    )
    tm.assert_series_equal(result, expected)


def test_groupby_none_column_name():
    # GH#47348
    df = DataFrame({None: [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [4, 5, 6, 7]})
    result = df.groupby(by=[None]).sum()
    expected = DataFrame({"b": [2, 5], "c": [9, 13]}, index=Index([1, 2], name=None))
    tm.assert_frame_equal(result, expected)


def test_single_element_list_grouping():
    # GH 42795
    df = DataFrame(
        {"a": [np.nan, 1], "b": [np.nan, 5], "c": [np.nan, 2]}, index=["x", "y"]
    )
    msg = (
        "In a future version of pandas, a length 1 "
        "tuple will be returned when iterating over "
        "a groupby with a grouper equal to a list of "
        "length 1. Don't supply a list with a single grouper "
        "to avoid this warning."
    )
    with tm.assert_produces_warning(FutureWarning, match=msg):
        values, _ = next(iter(df.groupby(["a"])))


@pytest.mark.parametrize("func", ["sum", "cumsum", "prod"])
def test_groupby_avoid_casting_to_float(func):
    # GH#37493
    val = 922337203685477580
    df = DataFrame({"a": 1, "b": [val]})
    result = getattr(df.groupby("a"), func)() - val
    expected = DataFrame({"b": [0]}, index=Index([1], name="a"))
    if func == "cumsum":
        expected = expected.reset_index(drop=True)
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("func, val", [("sum", 3), ("prod", 2)])
def test_groupby_sum_support_mask(any_numeric_ea_dtype, func, val):
    # GH#37493
    df = DataFrame({"a": 1, "b": [1, 2, pd.NA]}, dtype=any_numeric_ea_dtype)
    result = getattr(df.groupby("a"), func)()
    expected = DataFrame(
        {"b": [val]},
        index=Index([1], name="a", dtype=any_numeric_ea_dtype),
        dtype=any_numeric_ea_dtype,
    )
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("val, dtype", [(111, "int"), (222, "uint")])
def test_groupby_overflow(val, dtype):
    # GH#37493
    df = DataFrame({"a": 1, "b": [val, val]}, dtype=f"{dtype}8")
    result = df.groupby("a").sum()
    expected = DataFrame(
        {"b": [val * 2]},
        index=Index([1], name="a", dtype=f"{dtype}64"),
        dtype=f"{dtype}64",
    )
    tm.assert_frame_equal(result, expected)

    result = df.groupby("a").cumsum()
    expected = DataFrame({"b": [val, val * 2]}, dtype=f"{dtype}64")
    tm.assert_frame_equal(result, expected)

    result = df.groupby("a").prod()
    expected = DataFrame(
        {"b": [val * val]},
        index=Index([1], name="a", dtype=f"{dtype}64"),
        dtype=f"{dtype}64",
    )
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("skipna, val", [(True, 3), (False, pd.NA)])
def test_groupby_cumsum_mask(any_numeric_ea_dtype, skipna, val):
    # GH#37493
    df = DataFrame({"a": 1, "b": [1, pd.NA, 2]}, dtype=any_numeric_ea_dtype)
    result = df.groupby("a").cumsum(skipna=skipna)
    expected = DataFrame(
        {"b": [1, pd.NA, val]},
        dtype=any_numeric_ea_dtype,
    )
    tm.assert_frame_equal(result, expected)