from datetime import datetime
from decimal import Decimal
from io import StringIO

import numpy as np
import pytest

from pandas.errors import PerformanceWarning

import pandas as pd
from pandas import (
    DataFrame,
    Grouper,
    Index,
    MultiIndex,
    Series,
    Timestamp,
    date_range,
    read_csv,
)
import pandas._testing as tm
from pandas.core.base import SpecificationError
import pandas.core.common as com


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

    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 = Series(
        [np.dtype("object")] * 2 + [np.dtype("float64")] * 2 + [np.dtype("int64")],
        index=["A", "B", "C", "D", "value"],
    )
    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
    df_grouped = tsframe.groupby(lambda x: x.month)
    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)

    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)


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, 2], index=["bar", "foo"])

    tm.assert_series_equal(agged, expected, check_dtype=False)
    assert issubclass(agged.dtype.type, np.dtype(dtype).type)


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")

    result = grouped.mean()
    assert result.index.name == "A"

    result = df.groupby("A", as_index=False).mean()
    assert result.index.name != "A"

    result = grouped.agg(np.mean)
    assert result.index.name == "A"

    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])
    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]
    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)["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=["A", "B", "C"])
    left = df.groupby(by="A", as_index=False)["B"].sum()
    assert type(left) is DataFrame
    assert left.to_dict() == {"A": {}, "B": {}}


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

    # single-key

    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)

    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",):
        pytest.skip("Test not applicable")

    if reduction_func in ("nth", "ngroup"):
        pytest.skip("Skip until behavior is determined (GH #5755)")

    df = DataFrame(np.random.randint(0, 5, size=(100, 2)), columns=["a", "b"])
    expected = getattr(df.groupby("a"), reduction_func)()
    if reduction_func == "size":
        expected = expected.rename("size")
    expected = expected.reset_index()

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

    result = getattr(g, reduction_func)()
    tm.assert_frame_equal(result, expected)

    result = g.agg(reduction_func)
    tm.assert_frame_equal(result, expected)

    result = getattr(g["b"], reduction_func)()
    tm.assert_frame_equal(result, expected)

    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)

    result = grouped["C"].agg(np.sum)
    expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
    assert isinstance(result, DataFrame)
    tm.assert_frame_equal(result, expected)

    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()
    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)
    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):
    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")

    result = grouped.mean()
    expected = df.loc[:, ["A", "C", "D"]].groupby("A").mean()
    tm.assert_frame_equal(result, expected)

    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")
    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 = "reduction operation 'sum' not allowed for this dtype"
    with pytest.raises(TypeError, match=msg):
        grouped.agg(lambda x: x.sum(0, numeric_only=False))


def test_omit_nuisance_sem(df):
    # GH 38774 - sem should work with nuisance columns
    grouped = df.groupby("A")
    result = grouped.sem()
    expected = df.loc[:, ["A", "C", "D"]].groupby("A").sem()
    tm.assert_frame_equal(result, expected)


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

    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"])
    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])]
    agged = df.groupby(keys).agg(np.mean)
    assert isinstance(agged.columns, MultiIndex)

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

    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)

    result = a.sum(level=0)
    tm.assert_series_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"
    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)

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

    data = read_csv(StringIO(data), index_col=0)

    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):
    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):
    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 = pd.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"])

    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"]
    tmp = d.groupby(["group"]).mean()
    res_values = np.array([[0, 1], [0, 1]], dtype=np.int64)
    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").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)

    # 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
    result = df.groupby(df["A"].values).sum()
    assert result.index.name is None

    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).apply(DataFrame.drop_duplicates)
            expected = df
            tm.assert_frame_equal(expected, result)

            result = (
                df.sort_index()
                .groupby(level=level, sort=sort)
                .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": pd.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"
        )


def test_empty_dataframe_groupby():
    # GH8093
    df = DataFrame(columns=["A", "B", "C"])

    result = df.groupby("A").sum()
    expected = DataFrame(columns=["B", "C"], dtype=np.float64)
    expected.index.name = "A"

    tm.assert_frame_equal(result, expected)


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=["foo"],
        index=pd.date_range("2018-01-01", periods=2, freq="D"),
    )

    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"),
            )
        ),
        index=pd.date_range("2018-01-01", periods=2, freq="D"),
    )

    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.parametrize("bool_agg_func", ["any", "all"])
def test_bool_aggs_dup_column_labels(bool_agg_func):
    # 21668
    df = DataFrame([[True, True]], columns=["a", "a"])
    grp_by = df.groupby([0])
    result = getattr(grp_by, bool_agg_func)()

    expected = df
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "idx", [Index(["a", "a"]), MultiIndex.from_tuples((("a", "a"), ("a", "a")))]
)
@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("Not applicable")

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

    args = []
    if groupby_func in {"fillna", "nth"}:
        args.append(0)
    elif groupby_func == "corrwith":
        args.append(df)
    elif groupby_func == "tshift":
        df.index = [Timestamp("today")]
        args.extend([1, "D"])

    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
    df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})

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

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

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

    tm.assert_frame_equal(result, expected)


def test_groupby_list_level():
    # GH 9790
    expected = DataFrame(np.arange(0, 9).reshape(3, 3))
    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], ...}"),
    ],
)
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)
