test_apply.py

from datetime import datetime
from io import StringIO

import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, bdate_range
import pandas._testing as tm


def test_apply_issues():
    # GH 5788

    s = """2011.05.16,00:00,1.40893
2011.05.16,01:00,1.40760
2011.05.16,02:00,1.40750
2011.05.16,03:00,1.40649
2011.05.17,02:00,1.40893
2011.05.17,03:00,1.40760
2011.05.17,04:00,1.40750
2011.05.17,05:00,1.40649
2011.05.18,02:00,1.40893
2011.05.18,03:00,1.40760
2011.05.18,04:00,1.40750
2011.05.18,05:00,1.40649"""

    df = pd.read_csv(
        StringIO(s),
        header=None,
        names=["date", "time", "value"],
        parse_dates=[["date", "time"]],
    )
    df = df.set_index("date_time")

    expected = df.groupby(df.index.date).idxmax()
    result = df.groupby(df.index.date).apply(lambda x: x.idxmax())
    tm.assert_frame_equal(result, expected)

    # GH 5789
    # don't auto coerce dates
    df = pd.read_csv(StringIO(s), header=None, names=["date", "time", "value"])
    exp_idx = pd.Index(
        ["2011.05.16", "2011.05.17", "2011.05.18"], dtype=object, name="date"
    )
    expected = Series(["00:00", "02:00", "02:00"], index=exp_idx)
    result = df.groupby("date").apply(lambda x: x["time"][x["value"].idxmax()])
    tm.assert_series_equal(result, expected)


def test_apply_trivial():
    # GH 20066
    # trivial apply: ignore input and return a constant dataframe.
    df = pd.DataFrame(
        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
        columns=["key", "data"],
    )
    expected = pd.concat([df.iloc[1:], df.iloc[1:]], axis=1, keys=["float64", "object"])
    result = df.groupby([str(x) for x in df.dtypes], axis=1).apply(
        lambda x: df.iloc[1:]
    )

    tm.assert_frame_equal(result, expected)


@pytest.mark.xfail(
    reason="GH#20066; function passed into apply "
    "returns a DataFrame with the same index "
    "as the one to create GroupBy object."
)
def test_apply_trivial_fail():
    # GH 20066
    # trivial apply fails if the constant dataframe has the same index
    # with the one used to create GroupBy object.
    df = pd.DataFrame(
        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
        columns=["key", "data"],
    )
    expected = pd.concat([df, df], axis=1, keys=["float64", "object"])
    result = df.groupby([str(x) for x in df.dtypes], axis=1).apply(lambda x: df)

    tm.assert_frame_equal(result, expected)


def test_fast_apply():
    # make sure that fast apply is correctly called
    # rather than raising any kind of error
    # otherwise the python path will be callsed
    # which slows things down
    N = 1000
    labels = np.random.randint(0, 2000, size=N)
    labels2 = np.random.randint(0, 3, size=N)
    df = DataFrame(
        {
            "key": labels,
            "key2": labels2,
            "value1": np.random.randn(N),
            "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
        }
    )

    def f(g):
        return 1

    g = df.groupby(["key", "key2"])

    grouper = g.grouper

    splitter = grouper._get_splitter(g._selected_obj, axis=g.axis)
    group_keys = grouper._get_group_keys()

    values, mutated = splitter.fast_apply(f, group_keys)

    assert not mutated


@pytest.mark.parametrize(
    "df, group_names",
    [
        (DataFrame({"a": [1, 1, 1, 2, 3], "b": ["a", "a", "a", "b", "c"]}), [1, 2, 3]),
        (DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]}), [0, 1]),
        (DataFrame({"a": [1]}), [1]),
        (DataFrame({"a": [1, 1, 1, 2, 2, 1, 1, 2], "b": range(8)}), [1, 2]),
        (DataFrame({"a": [1, 2, 3, 1, 2, 3], "two": [4, 5, 6, 7, 8, 9]}), [1, 2, 3]),
        (
            DataFrame(
                {
                    "a": list("aaabbbcccc"),
                    "B": [3, 4, 3, 6, 5, 2, 1, 9, 5, 4],
                    "C": [4, 0, 2, 2, 2, 7, 8, 6, 2, 8],
                }
            ),
            ["a", "b", "c"],
        ),
        (DataFrame([[1, 2, 3], [2, 2, 3]], columns=["a", "b", "c"]), [1, 2]),
    ],
    ids=[
        "GH2936",
        "GH7739 & GH10519",
        "GH10519",
        "GH2656",
        "GH12155",
        "GH20084",
        "GH21417",
    ],
)
def test_group_apply_once_per_group(df, group_names):
    # GH2936, GH7739, GH10519, GH2656, GH12155, GH20084, GH21417

    # This test should ensure that a function is only evaluated
    # once per group. Previously the function has been evaluated twice
    # on the first group to check if the Cython index slider is safe to use
    # This test ensures that the side effect (append to list) is only triggered
    # once per group

    names = []
    # cannot parameterize over the functions since they need external
    # `names` to detect side effects

    def f_copy(group):
        # this takes the fast apply path
        names.append(group.name)
        return group.copy()

    def f_nocopy(group):
        # this takes the slow apply path
        names.append(group.name)
        return group

    def f_scalar(group):
        # GH7739, GH2656
        names.append(group.name)
        return 0

    def f_none(group):
        # GH10519, GH12155, GH21417
        names.append(group.name)
        return None

    def f_constant_df(group):
        # GH2936, GH20084
        names.append(group.name)
        return DataFrame({"a": [1], "b": [1]})

    for func in [f_copy, f_nocopy, f_scalar, f_none, f_constant_df]:
        del names[:]

        df.groupby("a").apply(func)
        assert names == group_names


def test_apply_with_mixed_dtype():
    # GH3480, apply with mixed dtype on axis=1 breaks in 0.11
    df = DataFrame(
        {
            "foo1": np.random.randn(6),
            "foo2": ["one", "two", "two", "three", "one", "two"],
        }
    )
    result = df.apply(lambda x: x, axis=1).dtypes
    expected = df.dtypes
    tm.assert_series_equal(result, expected)

    # GH 3610 incorrect dtype conversion with as_index=False
    df = DataFrame({"c1": [1, 2, 6, 6, 8]})
    df["c2"] = df.c1 / 2.0
    result1 = df.groupby("c2").mean().reset_index().c2
    result2 = df.groupby("c2", as_index=False).mean().c2
    tm.assert_series_equal(result1, result2)


def test_groupby_as_index_apply(df):
    # GH #4648 and #3417
    df = DataFrame(
        {
            "item_id": ["b", "b", "a", "c", "a", "b"],
            "user_id": [1, 2, 1, 1, 3, 1],
            "time": range(6),
        }
    )

    g_as = df.groupby("user_id", as_index=True)
    g_not_as = df.groupby("user_id", as_index=False)

    res_as = g_as.head(2).index
    res_not_as = g_not_as.head(2).index
    exp = Index([0, 1, 2, 4])
    tm.assert_index_equal(res_as, exp)
    tm.assert_index_equal(res_not_as, exp)

    res_as_apply = g_as.apply(lambda x: x.head(2)).index
    res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index

    # apply doesn't maintain the original ordering
    # changed in GH5610 as the as_index=False returns a MI here
    exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (2, 4)])
    tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
    exp_as_apply = MultiIndex.from_tuples(tp, names=["user_id", None])

    tm.assert_index_equal(res_as_apply, exp_as_apply)
    tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)

    ind = Index(list("abcde"))
    df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
    res = df.groupby(0, as_index=False).apply(lambda x: x).index
    tm.assert_index_equal(res, ind)


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

    def desc(group):
        result = group.describe()
        result.index.name = "stat"
        return result

    def desc2(group):
        result = group.describe()
        result.index.name = "stat"
        result = result[: len(group)]
        # weirdo
        return result

    def desc3(group):
        result = group.describe()

        # names are different
        result.index.name = f"stat_{len(group):d}"

        result = result[: len(group)]
        # weirdo
        return result

    result = grouped.apply(desc)
    assert result.index.names == ("A", "B", "stat")

    result2 = grouped.apply(desc2)
    assert result2.index.names == ("A", "B", "stat")

    result3 = grouped.apply(desc3)
    assert result3.index.names == ("A", "B", None)


def test_apply_series_to_frame():
    def f(piece):
        with np.errstate(invalid="ignore"):
            logged = np.log(piece)
        return DataFrame(
            {"value": piece, "demeaned": piece - piece.mean(), "logged": logged}
        )

    dr = bdate_range("1/1/2000", periods=100)
    ts = Series(np.random.randn(100), index=dr)

    grouped = ts.groupby(lambda x: x.month)
    result = grouped.apply(f)

    assert isinstance(result, DataFrame)
    tm.assert_index_equal(result.index, ts.index)


def test_apply_series_yield_constant(df):
    result = df.groupby(["A", "B"])["C"].apply(len)
    assert result.index.names[:2] == ("A", "B")


def test_apply_frame_yield_constant(df):
    # GH13568
    result = df.groupby(["A", "B"]).apply(len)
    assert isinstance(result, Series)
    assert result.name is None

    result = df.groupby(["A", "B"])[["C", "D"]].apply(len)
    assert isinstance(result, Series)
    assert result.name is None


def test_apply_frame_to_series(df):
    grouped = df.groupby(["A", "B"])
    result = grouped.apply(len)
    expected = grouped.count()["C"]
    tm.assert_index_equal(result.index, expected.index)
    tm.assert_numpy_array_equal(result.values, expected.values)


def test_apply_frame_concat_series():
    def trans(group):
        return group.groupby("B")["C"].sum().sort_values()[:2]

    def trans2(group):
        grouped = group.groupby(df.reindex(group.index)["B"])
        return grouped.sum().sort_values()[:2]

    df = DataFrame(
        {
            "A": np.random.randint(0, 5, 1000),
            "B": np.random.randint(0, 5, 1000),
            "C": np.random.randn(1000),
        }
    )

    result = df.groupby("A").apply(trans)
    exp = df.groupby("A")["C"].apply(trans2)
    tm.assert_series_equal(result, exp, check_names=False)
    assert result.name == "C"


def test_apply_transform(ts):
    grouped = ts.groupby(lambda x: x.month)
    result = grouped.apply(lambda x: x * 2)
    expected = grouped.transform(lambda x: x * 2)
    tm.assert_series_equal(result, expected)


def test_apply_multikey_corner(tsframe):
    grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])

    def f(group):
        return group.sort_values("A")[-5:]

    result = grouped.apply(f)
    for key, group in grouped:
        tm.assert_frame_equal(result.loc[key], f(group))


def test_apply_chunk_view():
    # Low level tinkering could be unsafe, make sure not
    df = DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})

    result = df.groupby("key", group_keys=False).apply(lambda x: x[:2])
    expected = df.take([0, 1, 3, 4, 6, 7])
    tm.assert_frame_equal(result, expected)


def test_apply_no_name_column_conflict():
    df = DataFrame(
        {
            "name": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
            "name2": [0, 0, 0, 1, 1, 1, 0, 0, 1, 1],
            "value": range(9, -1, -1),
        }
    )

    # it works! #2605
    grouped = df.groupby(["name", "name2"])
    grouped.apply(lambda x: x.sort_values("value", inplace=True))


def test_apply_typecast_fail():
    df = DataFrame(
        {
            "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
            "c": np.tile(["a", "b", "c"], 2),
            "v": np.arange(1.0, 7.0),
        }
    )

    def f(group):
        v = group["v"]
        group["v2"] = (v - v.min()) / (v.max() - v.min())
        return group

    result = df.groupby("d").apply(f)

    expected = df.copy()
    expected["v2"] = np.tile([0.0, 0.5, 1], 2)

    tm.assert_frame_equal(result, expected)


def test_apply_multiindex_fail():
    index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]])
    df = DataFrame(
        {
            "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
            "c": np.tile(["a", "b", "c"], 2),
            "v": np.arange(1.0, 7.0),
        },
        index=index,
    )

    def f(group):
        v = group["v"]
        group["v2"] = (v - v.min()) / (v.max() - v.min())
        return group

    result = df.groupby("d").apply(f)

    expected = df.copy()
    expected["v2"] = np.tile([0.0, 0.5, 1], 2)

    tm.assert_frame_equal(result, expected)


def test_apply_corner(tsframe):
    result = tsframe.groupby(lambda x: x.year).apply(lambda x: x * 2)
    expected = tsframe * 2
    tm.assert_frame_equal(result, expected)


def test_apply_without_copy():
    # GH 5545
    # returning a non-copy in an applied function fails

    data = DataFrame(
        {
            "id_field": [100, 100, 200, 300],
            "category": ["a", "b", "c", "c"],
            "value": [1, 2, 3, 4],
        }
    )

    def filt1(x):
        if x.shape[0] == 1:
            return x.copy()
        else:
            return x[x.category == "c"]

    def filt2(x):
        if x.shape[0] == 1:
            return x
        else:
            return x[x.category == "c"]

    expected = data.groupby("id_field").apply(filt1)
    result = data.groupby("id_field").apply(filt2)
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("test_series", [True, False])
def test_apply_with_duplicated_non_sorted_axis(test_series):
    # GH 30667
    df = pd.DataFrame(
        [["x", "p"], ["x", "p"], ["x", "o"]], columns=["X", "Y"], index=[1, 2, 2]
    )
    if test_series:
        ser = df.set_index("Y")["X"]
        result = ser.groupby(level=0).apply(lambda x: x)

        # not expecting the order to remain the same for duplicated axis
        result = result.sort_index()
        expected = ser.sort_index()
        tm.assert_series_equal(result, expected)
    else:
        result = df.groupby("Y").apply(lambda x: x)

        # not expecting the order to remain the same for duplicated axis
        result = result.sort_values("Y")
        expected = df.sort_values("Y")
        tm.assert_frame_equal(result, expected)


def test_apply_corner_cases():
    # #535, can't use sliding iterator

    N = 1000
    labels = np.random.randint(0, 100, size=N)
    df = DataFrame(
        {
            "key": labels,
            "value1": np.random.randn(N),
            "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
        }
    )

    grouped = df.groupby("key")

    def f(g):
        g["value3"] = g["value1"] * 2
        return g

    result = grouped.apply(f)
    assert "value3" in result


def test_apply_numeric_coercion_when_datetime():
    # In the past, group-by/apply operations have been over-eager
    # in converting dtypes to numeric, in the presence of datetime
    # columns.  Various GH issues were filed, the reproductions
    # for which are here.

    # GH 15670
    df = pd.DataFrame(
        {"Number": [1, 2], "Date": ["2017-03-02"] * 2, "Str": ["foo", "inf"]}
    )
    expected = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
    df.Date = pd.to_datetime(df.Date)
    result = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
    tm.assert_series_equal(result["Str"], expected["Str"])

    # GH 15421
    df = pd.DataFrame(
        {"A": [10, 20, 30], "B": ["foo", "3", "4"], "T": [pd.Timestamp("12:31:22")] * 3}
    )

    def get_B(g):
        return g.iloc[0][["B"]]

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

    # GH 14423
    def predictions(tool):
        out = pd.Series(index=["p1", "p2", "useTime"], dtype=object)
        if "step1" in list(tool.State):
            out["p1"] = str(tool[tool.State == "step1"].Machine.values[0])
        if "step2" in list(tool.State):
            out["p2"] = str(tool[tool.State == "step2"].Machine.values[0])
            out["useTime"] = str(tool[tool.State == "step2"].oTime.values[0])
        return out

    df1 = pd.DataFrame(
        {
            "Key": ["B", "B", "A", "A"],
            "State": ["step1", "step2", "step1", "step2"],
            "oTime": ["", "2016-09-19 05:24:33", "", "2016-09-19 23:59:04"],
            "Machine": ["23", "36L", "36R", "36R"],
        }
    )
    df2 = df1.copy()
    df2.oTime = pd.to_datetime(df2.oTime)
    expected = df1.groupby("Key").apply(predictions).p1
    result = df2.groupby("Key").apply(predictions).p1
    tm.assert_series_equal(expected, result)


def test_apply_aggregating_timedelta_and_datetime():
    # Regression test for GH 15562
    # The following groupby caused ValueErrors and IndexErrors pre 0.20.0

    df = pd.DataFrame(
        {
            "clientid": ["A", "B", "C"],
            "datetime": [np.datetime64("2017-02-01 00:00:00")] * 3,
        }
    )
    df["time_delta_zero"] = df.datetime - df.datetime
    result = df.groupby("clientid").apply(
        lambda ddf: pd.Series(
            dict(clientid_age=ddf.time_delta_zero.min(), date=ddf.datetime.min())
        )
    )
    expected = pd.DataFrame(
        {
            "clientid": ["A", "B", "C"],
            "clientid_age": [np.timedelta64(0, "D")] * 3,
            "date": [np.datetime64("2017-02-01 00:00:00")] * 3,
        }
    ).set_index("clientid")

    tm.assert_frame_equal(result, expected)


def test_time_field_bug():
    # Test a fix for the following error related to GH issue 11324 When
    # non-key fields in a group-by dataframe contained time-based fields
    # that were not returned by the apply function, an exception would be
    # raised.

    df = pd.DataFrame({"a": 1, "b": [datetime.now() for nn in range(10)]})

    def func_with_no_date(batch):
        return pd.Series({"c": 2})

    def func_with_date(batch):
        return pd.Series({"b": datetime(2015, 1, 1), "c": 2})

    dfg_no_conversion = df.groupby(by=["a"]).apply(func_with_no_date)
    dfg_no_conversion_expected = pd.DataFrame({"c": 2}, index=[1])
    dfg_no_conversion_expected.index.name = "a"

    dfg_conversion = df.groupby(by=["a"]).apply(func_with_date)
    dfg_conversion_expected = pd.DataFrame(
        {"b": datetime(2015, 1, 1), "c": 2}, index=[1]
    )
    dfg_conversion_expected.index.name = "a"

    tm.assert_frame_equal(dfg_no_conversion, dfg_no_conversion_expected)
    tm.assert_frame_equal(dfg_conversion, dfg_conversion_expected)


def test_gb_apply_list_of_unequal_len_arrays():

    # GH1738
    df = DataFrame(
        {
            "group1": ["a", "a", "a", "b", "b", "b", "a", "a", "a", "b", "b", "b"],
            "group2": ["c", "c", "d", "d", "d", "e", "c", "c", "d", "d", "d", "e"],
            "weight": [1.1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 1, 2],
            "value": [7.1, 8, 9, 10, 11, 12, 8, 7, 6, 5, 4, 3],
        }
    )
    df = df.set_index(["group1", "group2"])
    df_grouped = df.groupby(level=["group1", "group2"], sort=True)

    def noddy(value, weight):
        out = np.array(value * weight).repeat(3)
        return out

    # the kernel function returns arrays of unequal length
    # pandas sniffs the first one, sees it's an array and not
    # a list, and assumed the rest are of equal length
    # and so tries a vstack

    # don't die
    df_grouped.apply(lambda x: noddy(x.value, x.weight))


def test_groupby_apply_all_none():
    # Tests to make sure no errors if apply function returns all None
    # values. Issue 9684.
    test_df = DataFrame({"groups": [0, 0, 1, 1], "random_vars": [8, 7, 4, 5]})

    def test_func(x):
        pass

    result = test_df.groupby("groups").apply(test_func)
    expected = DataFrame()
    tm.assert_frame_equal(result, expected)


def test_groupby_apply_none_first():
    # GH 12824. Tests if apply returns None first.
    test_df1 = DataFrame({"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]})
    test_df2 = DataFrame({"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]})

    def test_func(x):
        if x.shape[0] < 2:
            return None
        return x.iloc[[0, -1]]

    result1 = test_df1.groupby("groups").apply(test_func)
    result2 = test_df2.groupby("groups").apply(test_func)
    index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], names=["groups", None])
    index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], names=["groups", None])
    expected1 = DataFrame({"groups": [1, 1], "vars": [0, 2]}, index=index1)
    expected2 = DataFrame({"groups": [2, 2], "vars": [1, 3]}, index=index2)
    tm.assert_frame_equal(result1, expected1)
    tm.assert_frame_equal(result2, expected2)


def test_groupby_apply_return_empty_chunk():
    # GH 22221: apply filter which returns some empty groups
    df = pd.DataFrame(dict(value=[0, 1], group=["filled", "empty"]))
    groups = df.groupby("group")
    result = groups.apply(lambda group: group[group.value != 1]["value"])
    expected = pd.Series(
        [0],
        name="value",
        index=MultiIndex.from_product(
            [["empty", "filled"], [0]], names=["group", None]
        ).drop("empty"),
    )
    tm.assert_series_equal(result, expected)


def test_apply_with_mixed_types():
    # gh-20949
    df = pd.DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]})
    g = df.groupby("A")

    result = g.transform(lambda x: x / x.sum())
    expected = pd.DataFrame({"B": [1 / 3.0, 2 / 3.0, 1], "C": [0.4, 0.6, 1.0]})
    tm.assert_frame_equal(result, expected)

    result = g.apply(lambda x: x / x.sum())
    tm.assert_frame_equal(result, expected)


def test_func_returns_object():
    # GH 28652
    df = DataFrame({"a": [1, 2]}, index=pd.Int64Index([1, 2]))
    result = df.groupby("a").apply(lambda g: g.index)
    expected = Series(
        [pd.Int64Index([1]), pd.Int64Index([2])], index=pd.Int64Index([1, 2], name="a")
    )

    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "group_column_dtlike",
    [datetime.today(), datetime.today().date(), datetime.today().time()],
)
def test_apply_datetime_issue(group_column_dtlike):
    # GH-28247
    # groupby-apply throws an error if one of the columns in the DataFrame
    #   is a datetime object and the column labels are different from
    #   standard int values in range(len(num_columns))

    df = pd.DataFrame({"a": ["foo"], "b": [group_column_dtlike]})
    result = df.groupby("a").apply(lambda x: pd.Series(["spam"], index=[42]))

    expected = pd.DataFrame(
        ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
    )
    tm.assert_frame_equal(result, expected)


def test_apply_series_return_dataframe_groups():
    # GH 10078
    tdf = DataFrame(
        {
            "day": {
                0: pd.Timestamp("2015-02-24 00:00:00"),
                1: pd.Timestamp("2015-02-24 00:00:00"),
                2: pd.Timestamp("2015-02-24 00:00:00"),
                3: pd.Timestamp("2015-02-24 00:00:00"),
                4: pd.Timestamp("2015-02-24 00:00:00"),
            },
            "userAgent": {
                0: "some UA string",
                1: "some UA string",
                2: "some UA string",
                3: "another UA string",
                4: "some UA string",
            },
            "userId": {
                0: "17661101",
                1: "17661101",
                2: "17661101",
                3: "17661101",
                4: "17661101",
            },
        }
    )

    def most_common_values(df):
        return Series({c: s.value_counts().index[0] for c, s in df.iteritems()})

    result = tdf.groupby("day").apply(most_common_values)["userId"]
    expected = pd.Series(
        ["17661101"], index=pd.DatetimeIndex(["2015-02-24"], name="day"), name="userId"
    )
    tm.assert_series_equal(result, expected)


def test_groupby_apply_datetime_result_dtypes():
    # GH 14849
    data = pd.DataFrame.from_records(
        [
            (pd.Timestamp(2016, 1, 1), "red", "dark", 1, "8"),
            (pd.Timestamp(2015, 1, 1), "green", "stormy", 2, "9"),
            (pd.Timestamp(2014, 1, 1), "blue", "bright", 3, "10"),
            (pd.Timestamp(2013, 1, 1), "blue", "calm", 4, "potato"),
        ],
        columns=["observation", "color", "mood", "intensity", "score"],
    )
    result = data.groupby("color").apply(lambda g: g.iloc[0]).dtypes
    expected = Series(
        [np.dtype("datetime64[ns]"), np.object, np.object, np.int64, np.object],
        index=["observation", "color", "mood", "intensity", "score"],
    )
    tm.assert_series_equal(result, expected)