test_reductions.py

import builtins
import datetime as dt
from string import ascii_lowercase

import numpy as np
import pytest

from pandas._libs.tslibs import iNaT

import pandas as pd
from pandas import (
    DataFrame,
    MultiIndex,
    Series,
    Timestamp,
    date_range,
    isna,
)
import pandas._testing as tm


@pytest.mark.parametrize("agg_func", ["any", "all"])
@pytest.mark.parametrize(
    "vals",
    [
        ["foo", "bar", "baz"],
        ["foo", "", ""],
        ["", "", ""],
        [1, 2, 3],
        [1, 0, 0],
        [0, 0, 0],
        [1.0, 2.0, 3.0],
        [1.0, 0.0, 0.0],
        [0.0, 0.0, 0.0],
        [True, True, True],
        [True, False, False],
        [False, False, False],
        [np.nan, np.nan, np.nan],
    ],
)
def test_groupby_bool_aggs(skipna, agg_func, vals):
    df = DataFrame({"key": ["a"] * 3 + ["b"] * 3, "val": vals * 2})

    # Figure out expectation using Python builtin
    exp = getattr(builtins, agg_func)(vals)

    # edge case for missing data with skipna and 'any'
    if skipna and all(isna(vals)) and agg_func == "any":
        exp = False

    expected = DataFrame(
        [exp] * 2, columns=["val"], index=pd.Index(["a", "b"], name="key")
    )
    result = getattr(df.groupby("key"), agg_func)(skipna=skipna)
    tm.assert_frame_equal(result, expected)


def test_any():
    df = DataFrame(
        [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
        columns=["A", "B", "C"],
    )
    expected = DataFrame(
        [[True, True], [False, True]], columns=["B", "C"], index=[1, 3]
    )
    expected.index.name = "A"
    result = df.groupby("A").any()
    tm.assert_frame_equal(result, expected)


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

    expected = df.set_axis(np.array([0]))
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
@pytest.mark.parametrize(
    "data",
    [
        [False, False, False],
        [True, True, True],
        [pd.NA, pd.NA, pd.NA],
        [False, pd.NA, False],
        [True, pd.NA, True],
        [True, pd.NA, False],
    ],
)
def test_masked_kleene_logic(bool_agg_func, skipna, data):
    # GH#37506
    ser = Series(data, dtype="boolean")

    # The result should match aggregating on the whole series. Correctness
    # there is verified in test_reductions.py::test_any_all_boolean_kleene_logic
    expected_data = getattr(ser, bool_agg_func)(skipna=skipna)
    expected = Series(expected_data, index=np.array([0]), dtype="boolean")

    result = ser.groupby([0, 0, 0]).agg(bool_agg_func, skipna=skipna)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "dtype1,dtype2,exp_col1,exp_col2",
    [
        (
            "float",
            "Float64",
            np.array([True], dtype=bool),
            pd.array([pd.NA], dtype="boolean"),
        ),
        (
            "Int64",
            "float",
            pd.array([pd.NA], dtype="boolean"),
            np.array([True], dtype=bool),
        ),
        (
            "Int64",
            "Int64",
            pd.array([pd.NA], dtype="boolean"),
            pd.array([pd.NA], dtype="boolean"),
        ),
        (
            "Float64",
            "boolean",
            pd.array([pd.NA], dtype="boolean"),
            pd.array([pd.NA], dtype="boolean"),
        ),
    ],
)
def test_masked_mixed_types(dtype1, dtype2, exp_col1, exp_col2):
    # GH#37506
    data = [1.0, np.nan]
    df = DataFrame(
        {"col1": pd.array(data, dtype=dtype1), "col2": pd.array(data, dtype=dtype2)}
    )
    result = df.groupby([1, 1]).agg("all", skipna=False)

    expected = DataFrame({"col1": exp_col1, "col2": exp_col2}, index=np.array([1]))
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
def test_masked_bool_aggs_skipna(bool_agg_func, dtype, skipna, frame_or_series):
    # GH#40585
    obj = frame_or_series([pd.NA, 1], dtype=dtype)
    expected_res = True
    if not skipna and bool_agg_func == "all":
        expected_res = pd.NA
    expected = frame_or_series([expected_res], index=np.array([1]), dtype="boolean")

    result = obj.groupby([1, 1]).agg(bool_agg_func, skipna=skipna)
    tm.assert_equal(result, expected)


@pytest.mark.parametrize(
    "bool_agg_func,data,expected_res",
    [
        ("any", [pd.NA, np.nan], False),
        ("any", [pd.NA, 1, np.nan], True),
        ("all", [pd.NA, pd.NaT], True),
        ("all", [pd.NA, False, pd.NaT], False),
    ],
)
def test_object_type_missing_vals(bool_agg_func, data, expected_res, frame_or_series):
    # GH#37501
    obj = frame_or_series(data, dtype=object)
    result = obj.groupby([1] * len(data)).agg(bool_agg_func)
    expected = frame_or_series([expected_res], index=np.array([1]), dtype="bool")
    tm.assert_equal(result, expected)


@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
def test_object_NA_raises_with_skipna_false(bool_agg_func):
    # GH#37501
    ser = Series([pd.NA], dtype=object)
    with pytest.raises(TypeError, match="boolean value of NA is ambiguous"):
        ser.groupby([1]).agg(bool_agg_func, skipna=False)


@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
def test_empty(frame_or_series, bool_agg_func):
    # GH 45231
    kwargs = {"columns": ["a"]} if frame_or_series is DataFrame else {"name": "a"}
    obj = frame_or_series(**kwargs, dtype=object)
    result = getattr(obj.groupby(obj.index), bool_agg_func)()
    expected = frame_or_series(**kwargs, dtype=bool)
    tm.assert_equal(result, expected)


@pytest.mark.parametrize(
    "func, values",
    [
        ("idxmin", {"c_int": [0, 2], "c_float": [1, 3], "c_date": [1, 2]}),
        ("idxmax", {"c_int": [1, 3], "c_float": [0, 2], "c_date": [0, 3]}),
    ],
)
@pytest.mark.parametrize("numeric_only", [True, False])
def test_idxmin_idxmax_returns_int_types(func, values, numeric_only):
    # GH 25444
    df = DataFrame(
        {
            "name": ["A", "A", "B", "B"],
            "c_int": [1, 2, 3, 4],
            "c_float": [4.02, 3.03, 2.04, 1.05],
            "c_date": ["2019", "2018", "2016", "2017"],
        }
    )
    df["c_date"] = pd.to_datetime(df["c_date"])
    df["c_date_tz"] = df["c_date"].dt.tz_localize("US/Pacific")
    df["c_timedelta"] = df["c_date"] - df["c_date"].iloc[0]
    df["c_period"] = df["c_date"].dt.to_period("W")
    df["c_Integer"] = df["c_int"].astype("Int64")
    df["c_Floating"] = df["c_float"].astype("Float64")

    result = getattr(df.groupby("name"), func)(numeric_only=numeric_only)

    expected = DataFrame(values, index=pd.Index(["A", "B"], name="name"))
    if numeric_only:
        expected = expected.drop(columns=["c_date"])
    else:
        expected["c_date_tz"] = expected["c_date"]
        expected["c_timedelta"] = expected["c_date"]
        expected["c_period"] = expected["c_date"]
    expected["c_Integer"] = expected["c_int"]
    expected["c_Floating"] = expected["c_float"]

    tm.assert_frame_equal(result, expected)


def test_idxmin_idxmax_axis1():
    df = DataFrame(
        np.random.default_rng(2).standard_normal((10, 4)), columns=["A", "B", "C", "D"]
    )
    df["A"] = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4]

    gb = df.groupby("A")

    warn_msg = "DataFrameGroupBy.idxmax with axis=1 is deprecated"
    with tm.assert_produces_warning(FutureWarning, match=warn_msg):
        res = gb.idxmax(axis=1)

    alt = df.iloc[:, 1:].idxmax(axis=1)
    indexer = res.index.get_level_values(1)

    tm.assert_series_equal(alt[indexer], res.droplevel("A"))

    df["E"] = date_range("2016-01-01", periods=10)
    gb2 = df.groupby("A")

    msg = "'>' not supported between instances of 'Timestamp' and 'float'"
    with pytest.raises(TypeError, match=msg):
        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
            gb2.idxmax(axis=1)


def test_groupby_mean_no_overflow():
    # Regression test for (#22487)
    df = DataFrame(
        {
            "user": ["A", "A", "A", "A", "A"],
            "connections": [4970, 4749, 4719, 4704, 18446744073699999744],
        }
    )
    assert df.groupby("user")["connections"].mean()["A"] == 3689348814740003840


def test_mean_on_timedelta():
    # GH 17382
    df = DataFrame({"time": pd.to_timedelta(range(10)), "cat": ["A", "B"] * 5})
    result = df.groupby("cat")["time"].mean()
    expected = Series(
        pd.to_timedelta([4, 5]), name="time", index=pd.Index(["A", "B"], name="cat")
    )
    tm.assert_series_equal(result, expected)


def test_cython_median():
    arr = np.random.default_rng(2).standard_normal(1000)
    arr[::2] = np.nan
    df = DataFrame(arr)

    labels = np.random.default_rng(2).integers(0, 50, size=1000).astype(float)
    labels[::17] = np.nan

    result = df.groupby(labels).median()
    msg = "using DataFrameGroupBy.median"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        exp = df.groupby(labels).agg(np.nanmedian)
    tm.assert_frame_equal(result, exp)

    df = DataFrame(np.random.default_rng(2).standard_normal((1000, 5)))
    msg = "using DataFrameGroupBy.median"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        rs = df.groupby(labels).agg(np.median)
    xp = df.groupby(labels).median()
    tm.assert_frame_equal(rs, xp)


def test_median_empty_bins(observed):
    df = DataFrame(np.random.default_rng(2).integers(0, 44, 500))

    grps = range(0, 55, 5)
    bins = pd.cut(df[0], grps)

    result = df.groupby(bins, observed=observed).median()
    expected = df.groupby(bins, observed=observed).agg(lambda x: x.median())
    tm.assert_frame_equal(result, expected)


def test_max_min_non_numeric():
    # #2700
    aa = DataFrame({"nn": [11, 11, 22, 22], "ii": [1, 2, 3, 4], "ss": 4 * ["mama"]})

    result = aa.groupby("nn").max()
    assert "ss" in result

    result = aa.groupby("nn").max(numeric_only=False)
    assert "ss" in result

    result = aa.groupby("nn").min()
    assert "ss" in result

    result = aa.groupby("nn").min(numeric_only=False)
    assert "ss" in result


def test_max_min_object_multiple_columns(using_array_manager):
    # GH#41111 case where the aggregation is valid for some columns but not
    # others; we split object blocks column-wise, consistent with
    # DataFrame._reduce

    df = DataFrame(
        {
            "A": [1, 1, 2, 2, 3],
            "B": [1, "foo", 2, "bar", False],
            "C": ["a", "b", "c", "d", "e"],
        }
    )
    df._consolidate_inplace()  # should already be consolidate, but double-check
    if not using_array_manager:
        assert len(df._mgr.blocks) == 2

    gb = df.groupby("A")

    result = gb[["C"]].max()
    # "max" is valid for column "C" but not for "B"
    ei = pd.Index([1, 2, 3], name="A")
    expected = DataFrame({"C": ["b", "d", "e"]}, index=ei)
    tm.assert_frame_equal(result, expected)

    result = gb[["C"]].min()
    # "min" is valid for column "C" but not for "B"
    ei = pd.Index([1, 2, 3], name="A")
    expected = DataFrame({"C": ["a", "c", "e"]}, index=ei)
    tm.assert_frame_equal(result, expected)


def test_min_date_with_nans():
    # GH26321
    dates = pd.to_datetime(
        Series(["2019-05-09", "2019-05-09", "2019-05-09"]), format="%Y-%m-%d"
    ).dt.date
    df = DataFrame({"a": [np.nan, "1", np.nan], "b": [0, 1, 1], "c": dates})

    result = df.groupby("b", as_index=False)["c"].min()["c"]
    expected = pd.to_datetime(
        Series(["2019-05-09", "2019-05-09"], name="c"), format="%Y-%m-%d"
    ).dt.date
    tm.assert_series_equal(result, expected)

    result = df.groupby("b")["c"].min()
    expected.index.name = "b"
    tm.assert_series_equal(result, expected)


def test_max_inat():
    # GH#40767 dont interpret iNaT as NaN
    ser = Series([1, iNaT])
    key = np.array([1, 1], dtype=np.int64)
    gb = ser.groupby(key)

    result = gb.max(min_count=2)
    expected = Series({1: 1}, dtype=np.int64)
    tm.assert_series_equal(result, expected, check_exact=True)

    result = gb.min(min_count=2)
    expected = Series({1: iNaT}, dtype=np.int64)
    tm.assert_series_equal(result, expected, check_exact=True)

    # not enough entries -> gets masked to NaN
    result = gb.min(min_count=3)
    expected = Series({1: np.nan})
    tm.assert_series_equal(result, expected, check_exact=True)


def test_max_inat_not_all_na():
    # GH#40767 dont interpret iNaT as NaN

    # make sure we dont round iNaT+1 to iNaT
    ser = Series([1, iNaT, 2, iNaT + 1])
    gb = ser.groupby([1, 2, 3, 3])
    result = gb.min(min_count=2)

    # Note: in converting to float64, the iNaT + 1 maps to iNaT, i.e. is lossy
    expected = Series({1: np.nan, 2: np.nan, 3: iNaT + 1})
    expected.index = expected.index.astype(int)
    tm.assert_series_equal(result, expected, check_exact=True)


@pytest.mark.parametrize("func", ["min", "max"])
def test_groupby_aggregate_period_column(func):
    # GH 31471
    groups = [1, 2]
    periods = pd.period_range("2020", periods=2, freq="Y")
    df = DataFrame({"a": groups, "b": periods})

    result = getattr(df.groupby("a")["b"], func)()
    idx = pd.Index([1, 2], name="a")
    expected = Series(periods, index=idx, name="b")

    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("func", ["min", "max"])
def test_groupby_aggregate_period_frame(func):
    # GH 31471
    groups = [1, 2]
    periods = pd.period_range("2020", periods=2, freq="Y")
    df = DataFrame({"a": groups, "b": periods})

    result = getattr(df.groupby("a"), func)()
    idx = pd.Index([1, 2], name="a")
    expected = DataFrame({"b": periods}, index=idx)

    tm.assert_frame_equal(result, expected)


def test_aggregate_numeric_object_dtype():
    # https://github.com/pandas-dev/pandas/issues/39329
    # simplified case: multiple object columns where one is all-NaN
    # -> gets split as the all-NaN is inferred as float
    df = DataFrame(
        {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": [np.nan] * 4},
    ).astype(object)
    result = df.groupby("key").min()
    expected = (
        DataFrame(
            {"key": ["A", "B"], "col1": ["a", "c"], "col2": [np.nan, np.nan]},
        )
        .set_index("key")
        .astype(object)
    )
    tm.assert_frame_equal(result, expected)

    # same but with numbers
    df = DataFrame(
        {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": range(4)},
    ).astype(object)
    result = df.groupby("key").min()
    expected = (
        DataFrame({"key": ["A", "B"], "col1": ["a", "c"], "col2": [0, 2]})
        .set_index("key")
        .astype(object)
    )
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("func", ["min", "max"])
def test_aggregate_categorical_lost_index(func: str):
    # GH: 28641 groupby drops index, when grouping over categorical column with min/max
    ds = Series(["b"], dtype="category").cat.as_ordered()
    df = DataFrame({"A": [1997], "B": ds})
    result = df.groupby("A").agg({"B": func})
    expected = DataFrame({"B": ["b"]}, index=pd.Index([1997], name="A"))

    # ordered categorical dtype should be preserved
    expected["B"] = expected["B"].astype(ds.dtype)

    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Float64", "Float32", "boolean"])
def test_groupby_min_max_nullable(dtype):
    if dtype == "Int64":
        # GH#41743 avoid precision loss
        ts = 1618556707013635762
    elif dtype == "boolean":
        ts = 0
    else:
        ts = 4.0

    df = DataFrame({"id": [2, 2], "ts": [ts, ts + 1]})
    df["ts"] = df["ts"].astype(dtype)

    gb = df.groupby("id")

    result = gb.min()
    expected = df.iloc[:1].set_index("id")
    tm.assert_frame_equal(result, expected)

    res_max = gb.max()
    expected_max = df.iloc[1:].set_index("id")
    tm.assert_frame_equal(res_max, expected_max)

    result2 = gb.min(min_count=3)
    expected2 = DataFrame({"ts": [pd.NA]}, index=expected.index, dtype=dtype)
    tm.assert_frame_equal(result2, expected2)

    res_max2 = gb.max(min_count=3)
    tm.assert_frame_equal(res_max2, expected2)

    # Case with NA values
    df2 = DataFrame({"id": [2, 2, 2], "ts": [ts, pd.NA, ts + 1]})
    df2["ts"] = df2["ts"].astype(dtype)
    gb2 = df2.groupby("id")

    result3 = gb2.min()
    tm.assert_frame_equal(result3, expected)

    res_max3 = gb2.max()
    tm.assert_frame_equal(res_max3, expected_max)

    result4 = gb2.min(min_count=100)
    tm.assert_frame_equal(result4, expected2)

    res_max4 = gb2.max(min_count=100)
    tm.assert_frame_equal(res_max4, expected2)


def test_min_max_nullable_uint64_empty_group():
    # don't raise NotImplementedError from libgroupby
    cat = pd.Categorical([0] * 10, categories=[0, 1])
    df = DataFrame({"A": cat, "B": pd.array(np.arange(10, dtype=np.uint64))})
    gb = df.groupby("A", observed=False)

    res = gb.min()

    idx = pd.CategoricalIndex([0, 1], dtype=cat.dtype, name="A")
    expected = DataFrame({"B": pd.array([0, pd.NA], dtype="UInt64")}, index=idx)
    tm.assert_frame_equal(res, expected)

    res = gb.max()
    expected.iloc[0, 0] = 9
    tm.assert_frame_equal(res, expected)


@pytest.mark.parametrize("func", ["first", "last", "min", "max"])
def test_groupby_min_max_categorical(func):
    # GH: 52151
    df = DataFrame(
        {
            "col1": pd.Categorical(["A"], categories=list("AB"), ordered=True),
            "col2": pd.Categorical([1], categories=[1, 2], ordered=True),
            "value": 0.1,
        }
    )
    result = getattr(df.groupby("col1", observed=False), func)()

    idx = pd.CategoricalIndex(data=["A", "B"], name="col1", ordered=True)
    expected = DataFrame(
        {
            "col2": pd.Categorical([1, None], categories=[1, 2], ordered=True),
            "value": [0.1, None],
        },
        index=idx,
    )
    tm.assert_frame_equal(result, expected)


def test_max_nan_bug():
    df = DataFrame(
        {
            "Unnamed: 0": ["-04-23", "-05-06", "-05-07"],
            "Date": [
                "2013-04-23 00:00:00",
                "2013-05-06 00:00:00",
                "2013-05-07 00:00:00",
            ],
            "app": Series([np.nan, np.nan, "OE"]),
            "File": ["log080001.log", "log.log", "xlsx"],
        }
    )
    gb = df.groupby("Date")
    r = gb[["File"]].max()
    e = gb["File"].max().to_frame()
    tm.assert_frame_equal(r, e)
    assert not r["File"].isna().any()


@pytest.mark.slow
@pytest.mark.parametrize("sort", [False, True])
@pytest.mark.parametrize("dropna", [False, True])
@pytest.mark.parametrize("as_index", [True, False])
@pytest.mark.parametrize("with_nan", [True, False])
@pytest.mark.parametrize("keys", [["joe"], ["joe", "jim"]])
def test_series_groupby_nunique(sort, dropna, as_index, with_nan, keys):
    n = 100
    m = 10
    days = date_range("2015-08-23", periods=10)
    df = DataFrame(
        {
            "jim": np.random.default_rng(2).choice(list(ascii_lowercase), n),
            "joe": np.random.default_rng(2).choice(days, n),
            "julie": np.random.default_rng(2).integers(0, m, n),
        }
    )
    if with_nan:
        df = df.astype({"julie": float})  # Explicit cast to avoid implicit cast below
        df.loc[1::17, "jim"] = None
        df.loc[3::37, "joe"] = None
        df.loc[7::19, "julie"] = None
        df.loc[8::19, "julie"] = None
        df.loc[9::19, "julie"] = None
    original_df = df.copy()
    gr = df.groupby(keys, as_index=as_index, sort=sort)
    left = gr["julie"].nunique(dropna=dropna)

    gr = df.groupby(keys, as_index=as_index, sort=sort)
    right = gr["julie"].apply(Series.nunique, dropna=dropna)
    if not as_index:
        right = right.reset_index(drop=True)

    if as_index:
        tm.assert_series_equal(left, right, check_names=False)
    else:
        tm.assert_frame_equal(left, right, check_names=False)
    tm.assert_frame_equal(df, original_df)


def test_nunique():
    df = DataFrame({"A": list("abbacc"), "B": list("abxacc"), "C": list("abbacx")})

    expected = DataFrame({"A": list("abc"), "B": [1, 2, 1], "C": [1, 1, 2]})
    result = df.groupby("A", as_index=False).nunique()
    tm.assert_frame_equal(result, expected)

    # as_index
    expected.index = list("abc")
    expected.index.name = "A"
    expected = expected.drop(columns="A")
    result = df.groupby("A").nunique()
    tm.assert_frame_equal(result, expected)

    # with na
    result = df.replace({"x": None}).groupby("A").nunique(dropna=False)
    tm.assert_frame_equal(result, expected)

    # dropna
    expected = DataFrame({"B": [1] * 3, "C": [1] * 3}, index=list("abc"))
    expected.index.name = "A"
    result = df.replace({"x": None}).groupby("A").nunique()
    tm.assert_frame_equal(result, expected)


def test_nunique_with_object():
    # GH 11077
    data = DataFrame(
        [
            [100, 1, "Alice"],
            [200, 2, "Bob"],
            [300, 3, "Charlie"],
            [-400, 4, "Dan"],
            [500, 5, "Edith"],
        ],
        columns=["amount", "id", "name"],
    )

    result = data.groupby(["id", "amount"])["name"].nunique()
    index = MultiIndex.from_arrays([data.id, data.amount])
    expected = Series([1] * 5, name="name", index=index)
    tm.assert_series_equal(result, expected)


def test_nunique_with_empty_series():
    # GH 12553
    data = Series(name="name", dtype=object)
    result = data.groupby(level=0).nunique()
    expected = Series(name="name", dtype="int64")
    tm.assert_series_equal(result, expected)


def test_nunique_with_timegrouper():
    # GH 13453
    test = DataFrame(
        {
            "time": [
                Timestamp("2016-06-28 09:35:35"),
                Timestamp("2016-06-28 16:09:30"),
                Timestamp("2016-06-28 16:46:28"),
            ],
            "data": ["1", "2", "3"],
        }
    ).set_index("time")
    result = test.groupby(pd.Grouper(freq="h"))["data"].nunique()
    expected = test.groupby(pd.Grouper(freq="h"))["data"].apply(Series.nunique)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "key, data, dropna, expected",
    [
        (
            ["x", "x", "x"],
            [Timestamp("2019-01-01"), pd.NaT, Timestamp("2019-01-01")],
            True,
            Series([1], index=pd.Index(["x"], name="key"), name="data"),
        ),
        (
            ["x", "x", "x"],
            [dt.date(2019, 1, 1), pd.NaT, dt.date(2019, 1, 1)],
            True,
            Series([1], index=pd.Index(["x"], name="key"), name="data"),
        ),
        (
            ["x", "x", "x", "y", "y"],
            [
                dt.date(2019, 1, 1),
                pd.NaT,
                dt.date(2019, 1, 1),
                pd.NaT,
                dt.date(2019, 1, 1),
            ],
            False,
            Series([2, 2], index=pd.Index(["x", "y"], name="key"), name="data"),
        ),
        (
            ["x", "x", "x", "x", "y"],
            [
                dt.date(2019, 1, 1),
                pd.NaT,
                dt.date(2019, 1, 1),
                pd.NaT,
                dt.date(2019, 1, 1),
            ],
            False,
            Series([2, 1], index=pd.Index(["x", "y"], name="key"), name="data"),
        ),
    ],
)
def test_nunique_with_NaT(key, data, dropna, expected):
    # GH 27951
    df = DataFrame({"key": key, "data": data})
    result = df.groupby(["key"])["data"].nunique(dropna=dropna)
    tm.assert_series_equal(result, expected)


def test_nunique_preserves_column_level_names():
    # GH 23222
    test = DataFrame([1, 2, 2], columns=pd.Index(["A"], name="level_0"))
    result = test.groupby([0, 0, 0]).nunique()
    expected = DataFrame([2], index=np.array([0]), columns=test.columns)
    tm.assert_frame_equal(result, expected)


def test_nunique_transform_with_datetime():
    # GH 35109 - transform with nunique on datetimes results in integers
    df = DataFrame(date_range("2008-12-31", "2009-01-02"), columns=["date"])
    result = df.groupby([0, 0, 1])["date"].transform("nunique")
    expected = Series([2, 2, 1], name="date")
    tm.assert_series_equal(result, expected)


def test_empty_categorical(observed):
    # GH#21334
    cat = Series([1]).astype("category")
    ser = cat[:0]
    gb = ser.groupby(ser, observed=observed)
    result = gb.nunique()
    if observed:
        expected = Series([], index=cat[:0], dtype="int64")
    else:
        expected = Series([0], index=cat, dtype="int64")
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("min_count", [0, 10])
def test_groupby_sum_mincount_boolean(min_count):
    b = True
    a = False
    na = np.nan
    dfg = pd.array([b, b, na, na, a, a, b], dtype="boolean")

    df = DataFrame({"A": [1, 1, 2, 2, 3, 3, 1], "B": dfg})
    result = df.groupby("A").sum(min_count=min_count)
    if min_count == 0:
        expected = DataFrame(
            {"B": pd.array([3, 0, 0], dtype="Int64")},
            index=pd.Index([1, 2, 3], name="A"),
        )
        tm.assert_frame_equal(result, expected)
    else:
        expected = DataFrame(
            {"B": pd.array([pd.NA] * 3, dtype="Int64")},
            index=pd.Index([1, 2, 3], name="A"),
        )
        tm.assert_frame_equal(result, expected)


def test_groupby_sum_below_mincount_nullable_integer():
    # https://github.com/pandas-dev/pandas/issues/32861
    df = DataFrame({"a": [0, 1, 2], "b": [0, 1, 2], "c": [0, 1, 2]}, dtype="Int64")
    grouped = df.groupby("a")
    idx = pd.Index([0, 1, 2], name="a", dtype="Int64")

    result = grouped["b"].sum(min_count=2)
    expected = Series([pd.NA] * 3, dtype="Int64", index=idx, name="b")
    tm.assert_series_equal(result, expected)

    result = grouped.sum(min_count=2)
    expected = DataFrame({"b": [pd.NA] * 3, "c": [pd.NA] * 3}, dtype="Int64", index=idx)
    tm.assert_frame_equal(result, expected)


def test_groupby_sum_timedelta_with_nat():
    # GH#42659
    df = DataFrame(
        {
            "a": [1, 1, 2, 2],
            "b": [pd.Timedelta("1d"), pd.Timedelta("2d"), pd.Timedelta("3d"), pd.NaT],
        }
    )
    td3 = pd.Timedelta(days=3)

    gb = df.groupby("a")

    res = gb.sum()
    expected = DataFrame({"b": [td3, td3]}, index=pd.Index([1, 2], name="a"))
    tm.assert_frame_equal(res, expected)

    res = gb["b"].sum()
    tm.assert_series_equal(res, expected["b"])

    res = gb["b"].sum(min_count=2)
    expected = Series([td3, pd.NaT], dtype="m8[ns]", name="b", index=expected.index)
    tm.assert_series_equal(res, expected)