from itertools import product
import operator

import numpy as np
from numpy import nan
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (
    Categorical,
    CategoricalIndex,
    DataFrame,
    Series,
    date_range,
    isna,
    notna,
)
from pandas.api.types import is_scalar
from pandas.core.index import MultiIndex
from pandas.core.indexes.datetimes import Timestamp
import pandas.util.testing as tm
from pandas.util.testing import (
    assert_almost_equal,
    assert_frame_equal,
    assert_index_equal,
    assert_series_equal,
)


class TestSeriesAnalytics:
    def test_describe(self):
        s = Series([0, 1, 2, 3, 4], name="int_data")
        result = s.describe()
        expected = Series(
            [5, 2, s.std(), 0, 1, 2, 3, 4],
            name="int_data",
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_series_equal(result, expected)

        s = Series([True, True, False, False, False], name="bool_data")
        result = s.describe()
        expected = Series(
            [5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"]
        )
        tm.assert_series_equal(result, expected)

        s = Series(["a", "a", "b", "c", "d"], name="str_data")
        result = s.describe()
        expected = Series(
            [5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"]
        )
        tm.assert_series_equal(result, expected)

    def test_describe_empty_object(self):
        # https://github.com/pandas-dev/pandas/issues/27183
        s = pd.Series([None, None], dtype=object)
        result = s.describe()
        expected = pd.Series(
            [0, 0, np.nan, np.nan],
            dtype=object,
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_series_equal(result, expected)

        result = s[:0].describe()
        tm.assert_series_equal(result, expected)
        # ensure NaN, not None
        assert np.isnan(result.iloc[2])
        assert np.isnan(result.iloc[3])

    def test_describe_with_tz(self, tz_naive_fixture):
        # GH 21332
        tz = tz_naive_fixture
        name = str(tz_naive_fixture)
        start = Timestamp(2018, 1, 1)
        end = Timestamp(2018, 1, 5)
        s = Series(date_range(start, end, tz=tz), name=name)
        result = s.describe()
        expected = Series(
            [
                5,
                5,
                s.value_counts().index[0],
                1,
                start.tz_localize(tz),
                end.tz_localize(tz),
            ],
            name=name,
            index=["count", "unique", "top", "freq", "first", "last"],
        )
        tm.assert_series_equal(result, expected)

    def test_argsort(self, datetime_series):
        self._check_accum_op("argsort", datetime_series, check_dtype=False)
        argsorted = datetime_series.argsort()
        assert issubclass(argsorted.dtype.type, np.integer)

        # GH 2967 (introduced bug in 0.11-dev I think)
        s = Series([Timestamp("201301{i:02d}".format(i=i)) for i in range(1, 6)])
        assert s.dtype == "datetime64[ns]"
        shifted = s.shift(-1)
        assert shifted.dtype == "datetime64[ns]"
        assert isna(shifted[4])

        result = s.argsort()
        expected = Series(range(5), dtype="int64")
        assert_series_equal(result, expected)

        result = shifted.argsort()
        expected = Series(list(range(4)) + [-1], dtype="int64")
        assert_series_equal(result, expected)

    def test_argsort_stable(self):
        s = Series(np.random.randint(0, 100, size=10000))
        mindexer = s.argsort(kind="mergesort")
        qindexer = s.argsort()

        mexpected = np.argsort(s.values, kind="mergesort")
        qexpected = np.argsort(s.values, kind="quicksort")

        tm.assert_series_equal(mindexer, Series(mexpected), check_dtype=False)
        tm.assert_series_equal(qindexer, Series(qexpected), check_dtype=False)
        msg = (
            r"ndarray Expected type <class 'numpy\.ndarray'>,"
            r" found <class 'pandas\.core\.series\.Series'> instead"
        )
        with pytest.raises(AssertionError, match=msg):
            tm.assert_numpy_array_equal(qindexer, mindexer)

    def test_cumsum(self, datetime_series):
        self._check_accum_op("cumsum", datetime_series)

    def test_cumprod(self, datetime_series):
        self._check_accum_op("cumprod", datetime_series)

    def test_cummin(self, datetime_series):
        tm.assert_numpy_array_equal(
            datetime_series.cummin().values,
            np.minimum.accumulate(np.array(datetime_series)),
        )
        ts = datetime_series.copy()
        ts[::2] = np.NaN
        result = ts.cummin()[1::2]
        expected = np.minimum.accumulate(ts.dropna())

        tm.assert_series_equal(result, expected)

    def test_cummax(self, datetime_series):
        tm.assert_numpy_array_equal(
            datetime_series.cummax().values,
            np.maximum.accumulate(np.array(datetime_series)),
        )
        ts = datetime_series.copy()
        ts[::2] = np.NaN
        result = ts.cummax()[1::2]
        expected = np.maximum.accumulate(ts.dropna())

        tm.assert_series_equal(result, expected)

    def test_cummin_datetime64(self):
        s = pd.Series(
            pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"])
        )

        expected = pd.Series(
            pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-1"])
        )
        result = s.cummin(skipna=True)
        tm.assert_series_equal(expected, result)

        expected = pd.Series(
            pd.to_datetime(
                ["NaT", "2000-1-2", "2000-1-2", "2000-1-1", "2000-1-1", "2000-1-1"]
            )
        )
        result = s.cummin(skipna=False)
        tm.assert_series_equal(expected, result)

    def test_cummax_datetime64(self):
        s = pd.Series(
            pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"])
        )

        expected = pd.Series(
            pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-2", "NaT", "2000-1-3"])
        )
        result = s.cummax(skipna=True)
        tm.assert_series_equal(expected, result)

        expected = pd.Series(
            pd.to_datetime(
                ["NaT", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-2", "2000-1-3"]
            )
        )
        result = s.cummax(skipna=False)
        tm.assert_series_equal(expected, result)

    def test_cummin_timedelta64(self):
        s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))

        expected = pd.Series(
            pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "1 min"])
        )
        result = s.cummin(skipna=True)
        tm.assert_series_equal(expected, result)

        expected = pd.Series(
            pd.to_timedelta(["NaT", "2 min", "2 min", "1 min", "1 min", "1 min"])
        )
        result = s.cummin(skipna=False)
        tm.assert_series_equal(expected, result)

    def test_cummax_timedelta64(self):
        s = pd.Series(pd.to_timedelta(["NaT", "2 min", "NaT", "1 min", "NaT", "3 min"]))

        expected = pd.Series(
            pd.to_timedelta(["NaT", "2 min", "NaT", "2 min", "NaT", "3 min"])
        )
        result = s.cummax(skipna=True)
        tm.assert_series_equal(expected, result)

        expected = pd.Series(
            pd.to_timedelta(["NaT", "2 min", "2 min", "2 min", "2 min", "3 min"])
        )
        result = s.cummax(skipna=False)
        tm.assert_series_equal(expected, result)

    def test_npdiff(self):
        pytest.skip("skipping due to Series no longer being an ndarray")

        # no longer works as the return type of np.diff is now nd.array
        s = Series(np.arange(5))

        r = np.diff(s)
        assert_series_equal(Series([nan, 0, 0, 0, nan]), r)

    def _check_accum_op(self, name, datetime_series_, check_dtype=True):
        func = getattr(np, name)
        tm.assert_numpy_array_equal(
            func(datetime_series_).values,
            func(np.array(datetime_series_)),
            check_dtype=check_dtype,
        )

        # with missing values
        ts = datetime_series_.copy()
        ts[::2] = np.NaN

        result = func(ts)[1::2]
        expected = func(np.array(ts.dropna()))

        tm.assert_numpy_array_equal(result.values, expected, check_dtype=False)

    def test_compress(self):
        cond = [True, False, True, False, False]
        s = Series([1, -1, 5, 8, 7], index=list("abcde"), name="foo")
        expected = Series(s.values.compress(cond), index=list("ac"), name="foo")
        with tm.assert_produces_warning(FutureWarning):
            result = s.compress(cond)
        tm.assert_series_equal(result, expected)

    def test_numpy_compress(self):
        cond = [True, False, True, False, False]
        s = Series([1, -1, 5, 8, 7], index=list("abcde"), name="foo")
        expected = Series(s.values.compress(cond), index=list("ac"), name="foo")
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            tm.assert_series_equal(np.compress(cond, s), expected)

        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            msg = "the 'axis' parameter is not supported"
            with pytest.raises(ValueError, match=msg):
                np.compress(cond, s, axis=1)

            msg = "the 'out' parameter is not supported"
            with pytest.raises(ValueError, match=msg):
                np.compress(cond, s, out=s)

    def test_round(self, datetime_series):
        datetime_series.index.name = "index_name"
        result = datetime_series.round(2)
        expected = Series(
            np.round(datetime_series.values, 2), index=datetime_series.index, name="ts"
        )
        assert_series_equal(result, expected)
        assert result.name == datetime_series.name

    def test_numpy_round(self):
        # See gh-12600
        s = Series([1.53, 1.36, 0.06])
        out = np.round(s, decimals=0)
        expected = Series([2.0, 1.0, 0.0])
        assert_series_equal(out, expected)

        msg = "the 'out' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.round(s, decimals=0, out=s)

    def test_numpy_round_nan(self):
        # See gh-14197
        s = Series([1.53, np.nan, 0.06])
        with tm.assert_produces_warning(None):
            result = s.round()
        expected = Series([2.0, np.nan, 0.0])
        assert_series_equal(result, expected)

    def test_built_in_round(self):
        s = Series([1.123, 2.123, 3.123], index=range(3))
        result = round(s)
        expected_rounded0 = Series([1.0, 2.0, 3.0], index=range(3))
        tm.assert_series_equal(result, expected_rounded0)

        decimals = 2
        expected_rounded = Series([1.12, 2.12, 3.12], index=range(3))
        result = round(s, decimals)
        tm.assert_series_equal(result, expected_rounded)

    def test_prod_numpy16_bug(self):
        s = Series([1.0, 1.0, 1.0], index=range(3))
        result = s.prod()

        assert not isinstance(result, Series)

    @td.skip_if_no_scipy
    def test_corr(self, datetime_series):
        import scipy.stats as stats

        # full overlap
        tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)

        # partial overlap
        tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1)

        assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12))

        ts1 = datetime_series[:15].reindex(datetime_series.index)
        ts2 = datetime_series[5:].reindex(datetime_series.index)
        assert isna(ts1.corr(ts2, min_periods=12))

        # No overlap
        assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))

        # all NA
        cp = datetime_series[:10].copy()
        cp[:] = np.nan
        assert isna(cp.corr(cp))

        A = tm.makeTimeSeries()
        B = tm.makeTimeSeries()
        result = A.corr(B)
        expected, _ = stats.pearsonr(A, B)
        tm.assert_almost_equal(result, expected)

    @td.skip_if_no_scipy
    def test_corr_rank(self):
        import scipy.stats as stats

        # kendall and spearman
        A = tm.makeTimeSeries()
        B = tm.makeTimeSeries()
        A[-5:] = A[:5]
        result = A.corr(B, method="kendall")
        expected = stats.kendalltau(A, B)[0]
        tm.assert_almost_equal(result, expected)

        result = A.corr(B, method="spearman")
        expected = stats.spearmanr(A, B)[0]
        tm.assert_almost_equal(result, expected)

        # results from R
        A = Series(
            [
                -0.89926396,
                0.94209606,
                -1.03289164,
                -0.95445587,
                0.76910310,
                -0.06430576,
                -2.09704447,
                0.40660407,
                -0.89926396,
                0.94209606,
            ]
        )
        B = Series(
            [
                -1.01270225,
                -0.62210117,
                -1.56895827,
                0.59592943,
                -0.01680292,
                1.17258718,
                -1.06009347,
                -0.10222060,
                -0.89076239,
                0.89372375,
            ]
        )
        kexp = 0.4319297
        sexp = 0.5853767
        tm.assert_almost_equal(A.corr(B, method="kendall"), kexp)
        tm.assert_almost_equal(A.corr(B, method="spearman"), sexp)

    def test_corr_invalid_method(self):
        # GH PR #22298
        s1 = pd.Series(np.random.randn(10))
        s2 = pd.Series(np.random.randn(10))
        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
        with pytest.raises(ValueError, match=msg):
            s1.corr(s2, method="____")

    def test_corr_callable_method(self, datetime_series):
        # simple correlation example
        # returns 1 if exact equality, 0 otherwise
        my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0

        # simple example
        s1 = Series([1, 2, 3, 4, 5])
        s2 = Series([5, 4, 3, 2, 1])
        expected = 0
        tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)

        # full overlap
        tm.assert_almost_equal(
            datetime_series.corr(datetime_series, method=my_corr), 1.0
        )

        # partial overlap
        tm.assert_almost_equal(
            datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0
        )

        # No overlap
        assert np.isnan(
            datetime_series[::2].corr(datetime_series[1::2], method=my_corr)
        )

        # dataframe example
        df = pd.DataFrame([s1, s2])
        expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}])
        tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected)

    def test_cov(self, datetime_series):
        # full overlap
        tm.assert_almost_equal(
            datetime_series.cov(datetime_series), datetime_series.std() ** 2
        )

        # partial overlap
        tm.assert_almost_equal(
            datetime_series[:15].cov(datetime_series[5:]),
            datetime_series[5:15].std() ** 2,
        )

        # No overlap
        assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))

        # all NA
        cp = datetime_series[:10].copy()
        cp[:] = np.nan
        assert isna(cp.cov(cp))

        # min_periods
        assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12))

        ts1 = datetime_series[:15].reindex(datetime_series.index)
        ts2 = datetime_series[5:].reindex(datetime_series.index)
        assert isna(ts1.cov(ts2, min_periods=12))

    def test_count(self, datetime_series):
        assert datetime_series.count() == len(datetime_series)

        datetime_series[::2] = np.NaN

        assert datetime_series.count() == np.isfinite(datetime_series).sum()

        mi = MultiIndex.from_arrays([list("aabbcc"), [1, 2, 2, nan, 1, 2]])
        ts = Series(np.arange(len(mi)), index=mi)

        left = ts.count(level=1)
        right = Series([2, 3, 1], index=[1, 2, nan])
        assert_series_equal(left, right)

        ts.iloc[[0, 3, 5]] = nan
        assert_series_equal(ts.count(level=1), right - 1)

    def test_dot(self):
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

        result = a.dot(b)
        expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"])
        assert_series_equal(result, expected)

        # Check index alignment
        b2 = b.reindex(index=reversed(b.index))
        result = a.dot(b)
        assert_series_equal(result, expected)

        # Check ndarray argument
        result = a.dot(b.values)
        assert np.all(result == expected.values)
        assert_almost_equal(a.dot(b["2"].values), expected["2"])

        # Check series argument
        assert_almost_equal(a.dot(b["1"]), expected["1"])
        assert_almost_equal(a.dot(b2["1"]), expected["1"])

        msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"
        # exception raised is of type Exception
        with pytest.raises(Exception, match=msg):
            a.dot(a.values[:3])
        msg = "matrices are not aligned"
        with pytest.raises(ValueError, match=msg):
            a.dot(b.T)

    def test_matmul(self):
        # matmul test is for GH #10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

        # Series @ DataFrame -> Series
        result = operator.matmul(a, b)
        expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"])
        assert_series_equal(result, expected)

        # DataFrame @ Series -> Series
        result = operator.matmul(b.T, a)
        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
        assert_series_equal(result, expected)

        # Series @ Series -> scalar
        result = operator.matmul(a, a)
        expected = np.dot(a.values, a.values)
        assert_almost_equal(result, expected)

        # GH 21530
        # vector (1D np.array) @ Series (__rmatmul__)
        result = operator.matmul(a.values, a)
        expected = np.dot(a.values, a.values)
        assert_almost_equal(result, expected)

        # GH 21530
        # vector (1D list) @ Series (__rmatmul__)
        result = operator.matmul(a.values.tolist(), a)
        expected = np.dot(a.values, a.values)
        assert_almost_equal(result, expected)

        # GH 21530
        # matrix (2D np.array) @ Series (__rmatmul__)
        result = operator.matmul(b.T.values, a)
        expected = np.dot(b.T.values, a.values)
        assert_almost_equal(result, expected)

        # GH 21530
        # matrix (2D nested lists) @ Series (__rmatmul__)
        result = operator.matmul(b.T.values.tolist(), a)
        expected = np.dot(b.T.values, a.values)
        assert_almost_equal(result, expected)

        # mixed dtype DataFrame @ Series
        a["p"] = int(a.p)
        result = operator.matmul(b.T, a)
        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
        assert_series_equal(result, expected)

        # different dtypes DataFrame @ Series
        a = a.astype(int)
        result = operator.matmul(b.T, a)
        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
        assert_series_equal(result, expected)

        msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"
        # exception raised is of type Exception
        with pytest.raises(Exception, match=msg):
            a.dot(a.values[:3])
        msg = "matrices are not aligned"
        with pytest.raises(ValueError, match=msg):
            a.dot(b.T)

    def test_clip(self, datetime_series):
        val = datetime_series.median()

        with tm.assert_produces_warning(FutureWarning):
            assert datetime_series.clip_lower(val).min() == val
        with tm.assert_produces_warning(FutureWarning):
            assert datetime_series.clip_upper(val).max() == val

        assert datetime_series.clip(lower=val).min() == val
        assert datetime_series.clip(upper=val).max() == val

        result = datetime_series.clip(-0.5, 0.5)
        expected = np.clip(datetime_series, -0.5, 0.5)
        assert_series_equal(result, expected)
        assert isinstance(expected, Series)

    def test_clip_types_and_nulls(self):

        sers = [
            Series([np.nan, 1.0, 2.0, 3.0]),
            Series([None, "a", "b", "c"]),
            Series(pd.to_datetime([np.nan, 1, 2, 3], unit="D")),
        ]

        for s in sers:
            thresh = s[2]
            with tm.assert_produces_warning(FutureWarning):
                lower = s.clip_lower(thresh)
            with tm.assert_produces_warning(FutureWarning):
                upper = s.clip_upper(thresh)
            assert lower[notna(lower)].min() == thresh
            assert upper[notna(upper)].max() == thresh
            assert list(isna(s)) == list(isna(lower))
            assert list(isna(s)) == list(isna(upper))

    def test_clip_with_na_args(self):
        """Should process np.nan argument as None """
        # GH # 17276
        s = Series([1, 2, 3])

        assert_series_equal(s.clip(np.nan), Series([1, 2, 3]))
        assert_series_equal(s.clip(upper=np.nan, lower=np.nan), Series([1, 2, 3]))

        # GH #19992
        assert_series_equal(s.clip(lower=[0, 4, np.nan]), Series([1, 4, np.nan]))
        assert_series_equal(s.clip(upper=[1, np.nan, 1]), Series([1, np.nan, 1]))

    def test_clip_against_series(self):
        # GH #6966

        s = Series([1.0, 1.0, 4.0])
        threshold = Series([1.0, 2.0, 3.0])

        with tm.assert_produces_warning(FutureWarning):
            assert_series_equal(s.clip_lower(threshold), Series([1.0, 2.0, 4.0]))
        with tm.assert_produces_warning(FutureWarning):
            assert_series_equal(s.clip_upper(threshold), Series([1.0, 1.0, 3.0]))

        lower = Series([1.0, 2.0, 3.0])
        upper = Series([1.5, 2.5, 3.5])

        assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5]))
        assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5]))

    @pytest.mark.parametrize("inplace", [True, False])
    @pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])])
    def test_clip_against_list_like(self, inplace, upper):
        # GH #15390
        original = pd.Series([5, 6, 7])
        result = original.clip(upper=upper, inplace=inplace)
        expected = pd.Series([1, 2, 3])

        if inplace:
            result = original
        tm.assert_series_equal(result, expected, check_exact=True)

    def test_clip_with_datetimes(self):

        # GH 11838
        # naive and tz-aware datetimes

        t = Timestamp("2015-12-01 09:30:30")
        s = Series([Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:31:00")])
        result = s.clip(upper=t)
        expected = Series(
            [Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:30:30")]
        )
        assert_series_equal(result, expected)

        t = Timestamp("2015-12-01 09:30:30", tz="US/Eastern")
        s = Series(
            [
                Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
                Timestamp("2015-12-01 09:31:00", tz="US/Eastern"),
            ]
        )
        result = s.clip(upper=t)
        expected = Series(
            [
                Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
                Timestamp("2015-12-01 09:30:30", tz="US/Eastern"),
            ]
        )
        assert_series_equal(result, expected)

    def test_cummethods_bool(self):
        # GH 6270

        a = pd.Series([False, False, False, True, True, False, False])
        b = ~a
        c = pd.Series([False] * len(b))
        d = ~c
        methods = {
            "cumsum": np.cumsum,
            "cumprod": np.cumprod,
            "cummin": np.minimum.accumulate,
            "cummax": np.maximum.accumulate,
        }
        args = product((a, b, c, d), methods)
        for s, method in args:
            expected = Series(methods[method](s.values))
            result = getattr(s, method)()
            assert_series_equal(result, expected)

        e = pd.Series([False, True, nan, False])
        cse = pd.Series([0, 1, nan, 1], dtype=object)
        cpe = pd.Series([False, 0, nan, 0])
        cmin = pd.Series([False, False, nan, False])
        cmax = pd.Series([False, True, nan, True])
        expecteds = {"cumsum": cse, "cumprod": cpe, "cummin": cmin, "cummax": cmax}

        for method in methods:
            res = getattr(e, method)()
            assert_series_equal(res, expecteds[method])

    def test_isin(self):
        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])

        result = s.isin(["A", "C"])
        expected = Series([True, False, True, False, False, False, True, True])
        assert_series_equal(result, expected)

        # GH: 16012
        # This specific issue has to have a series over 1e6 in len, but the
        # comparison array (in_list) must be large enough so that numpy doesn't
        # do a manual masking trick that will avoid this issue altogether
        s = Series(list("abcdefghijk" * 10 ** 5))
        # If numpy doesn't do the manual comparison/mask, these
        # unorderable mixed types are what cause the exception in numpy
        in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6

        assert s.isin(in_list).sum() == 200000

    def test_isin_with_string_scalar(self):
        # GH4763
        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
        msg = (
            r"only list-like objects are allowed to be passed to isin\(\),"
            r" you passed a \[str\]"
        )
        with pytest.raises(TypeError, match=msg):
            s.isin("a")

        s = Series(["aaa", "b", "c"])
        with pytest.raises(TypeError, match=msg):
            s.isin("aaa")

    def test_isin_with_i8(self):
        # GH 5021

        expected = Series([True, True, False, False, False])
        expected2 = Series([False, True, False, False, False])

        # datetime64[ns]
        s = Series(date_range("jan-01-2013", "jan-05-2013"))

        result = s.isin(s[0:2])
        assert_series_equal(result, expected)

        result = s.isin(s[0:2].values)
        assert_series_equal(result, expected)

        # fails on dtype conversion in the first place
        result = s.isin(s[0:2].values.astype("datetime64[D]"))
        assert_series_equal(result, expected)

        result = s.isin([s[1]])
        assert_series_equal(result, expected2)

        result = s.isin([np.datetime64(s[1])])
        assert_series_equal(result, expected2)

        result = s.isin(set(s[0:2]))
        assert_series_equal(result, expected)

        # timedelta64[ns]
        s = Series(pd.to_timedelta(range(5), unit="d"))
        result = s.isin(s[0:2])
        assert_series_equal(result, expected)

    @pytest.mark.parametrize("empty", [[], Series(), np.array([])])
    def test_isin_empty(self, empty):
        # see gh-16991
        s = Series(["a", "b"])
        expected = Series([False, False])

        result = s.isin(empty)
        tm.assert_series_equal(expected, result)

    def test_ptp(self):
        # GH21614
        N = 1000
        arr = np.random.randn(N)
        ser = Series(arr)
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            assert np.ptp(ser) == np.ptp(arr)

        # GH11163
        s = Series([3, 5, np.nan, -3, 10])
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            assert s.ptp() == 13
            assert pd.isna(s.ptp(skipna=False))

        mi = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]])
        s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)

        expected = pd.Series([6, 2], index=["a", "b"], dtype=np.float64)
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            tm.assert_series_equal(s.ptp(level=0), expected)

        expected = pd.Series([np.nan, np.nan], index=["a", "b"])
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)

        msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>"
        with pytest.raises(ValueError, match=msg):
            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
                s.ptp(axis=1)

        s = pd.Series(["a", "b", "c", "d", "e"])
        msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
        with pytest.raises(TypeError, match=msg):
            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
                s.ptp()

        msg = r"Series\.ptp does not implement numeric_only\."
        with pytest.raises(NotImplementedError, match=msg):
            with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
                s.ptp(numeric_only=True)

    def test_repeat(self):
        s = Series(np.random.randn(3), index=["a", "b", "c"])

        reps = s.repeat(5)
        exp = Series(s.values.repeat(5), index=s.index.values.repeat(5))
        assert_series_equal(reps, exp)

        to_rep = [2, 3, 4]
        reps = s.repeat(to_rep)
        exp = Series(s.values.repeat(to_rep), index=s.index.values.repeat(to_rep))
        assert_series_equal(reps, exp)

    def test_numpy_repeat(self):
        s = Series(np.arange(3), name="x")
        expected = Series(s.values.repeat(2), name="x", index=s.index.values.repeat(2))
        assert_series_equal(np.repeat(s, 2), expected)

        msg = "the 'axis' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.repeat(s, 2, axis=0)

    def test_searchsorted(self):
        s = Series([1, 2, 3])

        result = s.searchsorted(1, side="left")
        assert is_scalar(result)
        assert result == 0

        result = s.searchsorted(1, side="right")
        assert is_scalar(result)
        assert result == 1

    def test_searchsorted_numeric_dtypes_scalar(self):
        s = Series([1, 2, 90, 1000, 3e9])
        r = s.searchsorted(30)
        assert is_scalar(r)
        assert r == 2

        r = s.searchsorted([30])
        e = np.array([2], dtype=np.intp)
        tm.assert_numpy_array_equal(r, e)

    def test_searchsorted_numeric_dtypes_vector(self):
        s = Series([1, 2, 90, 1000, 3e9])
        r = s.searchsorted([91, 2e6])
        e = np.array([3, 4], dtype=np.intp)
        tm.assert_numpy_array_equal(r, e)

    def test_search_sorted_datetime64_scalar(self):
        s = Series(pd.date_range("20120101", periods=10, freq="2D"))
        v = pd.Timestamp("20120102")
        r = s.searchsorted(v)
        assert is_scalar(r)
        assert r == 1

    def test_search_sorted_datetime64_list(self):
        s = Series(pd.date_range("20120101", periods=10, freq="2D"))
        v = [pd.Timestamp("20120102"), pd.Timestamp("20120104")]
        r = s.searchsorted(v)
        e = np.array([1, 2], dtype=np.intp)
        tm.assert_numpy_array_equal(r, e)

    def test_searchsorted_sorter(self):
        # GH8490
        s = Series([3, 1, 2])
        r = s.searchsorted([0, 3], sorter=np.argsort(s))
        e = np.array([0, 2], dtype=np.intp)
        tm.assert_numpy_array_equal(r, e)

    def test_is_monotonic(self):

        s = Series(np.random.randint(0, 10, size=1000))
        assert not s.is_monotonic
        s = Series(np.arange(1000))
        assert s.is_monotonic is True
        assert s.is_monotonic_increasing is True
        s = Series(np.arange(1000, 0, -1))
        assert s.is_monotonic_decreasing is True

        s = Series(pd.date_range("20130101", periods=10))
        assert s.is_monotonic is True
        assert s.is_monotonic_increasing is True
        s = Series(list(reversed(s.tolist())))
        assert s.is_monotonic is False
        assert s.is_monotonic_decreasing is True

    def test_sort_index_level(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
        s = Series([1, 2], mi)
        backwards = s.iloc[[1, 0]]

        res = s.sort_index(level="A")
        assert_series_equal(backwards, res)

        res = s.sort_index(level=["A", "B"])
        assert_series_equal(backwards, res)

        res = s.sort_index(level="A", sort_remaining=False)
        assert_series_equal(s, res)

        res = s.sort_index(level=["A", "B"], sort_remaining=False)
        assert_series_equal(s, res)

    def test_apply_categorical(self):
        values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
        s = pd.Series(values, name="XX", index=list("abcdefg"))
        result = s.apply(lambda x: x.lower())

        # should be categorical dtype when the number of categories are
        # the same
        values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
        exp = pd.Series(values, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        tm.assert_categorical_equal(result.values, exp.values)

        result = s.apply(lambda x: "A")
        exp = pd.Series(["A"] * 7, name="XX", index=list("abcdefg"))
        tm.assert_series_equal(result, exp)
        assert result.dtype == np.object

    def test_shift_int(self, datetime_series):
        ts = datetime_series.astype(int)
        shifted = ts.shift(1)
        expected = ts.astype(float).shift(1)
        assert_series_equal(shifted, expected)

    def test_shift_categorical(self):
        # GH 9416
        s = pd.Series(["a", "b", "c", "d"], dtype="category")

        assert_series_equal(s.iloc[:-1], s.shift(1).shift(-1).dropna())

        sp1 = s.shift(1)
        assert_index_equal(s.index, sp1.index)
        assert np.all(sp1.values.codes[:1] == -1)
        assert np.all(s.values.codes[:-1] == sp1.values.codes[1:])

        sn2 = s.shift(-2)
        assert_index_equal(s.index, sn2.index)
        assert np.all(sn2.values.codes[-2:] == -1)
        assert np.all(s.values.codes[2:] == sn2.values.codes[:-2])

        assert_index_equal(s.values.categories, sp1.values.categories)
        assert_index_equal(s.values.categories, sn2.values.categories)

    def test_unstack(self):
        from numpy import nan

        index = MultiIndex(
            levels=[["bar", "foo"], ["one", "three", "two"]],
            codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
        )

        s = Series(np.arange(4.0), index=index)
        unstacked = s.unstack()

        expected = DataFrame(
            [[2.0, nan, 3.0], [0.0, 1.0, nan]],
            index=["bar", "foo"],
            columns=["one", "three", "two"],
        )

        assert_frame_equal(unstacked, expected)

        unstacked = s.unstack(level=0)
        assert_frame_equal(unstacked, expected.T)

        index = MultiIndex(
            levels=[["bar"], ["one", "two", "three"], [0, 1]],
            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
        )
        s = Series(np.random.randn(6), index=index)
        exp_index = MultiIndex(
            levels=[["one", "two", "three"], [0, 1]],
            codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
        )
        expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
        unstacked = s.unstack(0).sort_index()
        assert_frame_equal(unstacked, expected)

        # GH5873
        idx = pd.MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
        ts = pd.Series([1, 2], index=idx)
        left = ts.unstack()
        right = DataFrame([[nan, 1], [2, nan]], index=[101, 102], columns=[nan, 3.5])
        assert_frame_equal(left, right)

        idx = pd.MultiIndex.from_arrays(
            [
                ["cat", "cat", "cat", "dog", "dog"],
                ["a", "a", "b", "a", "b"],
                [1, 2, 1, 1, np.nan],
            ]
        )
        ts = pd.Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
        right = DataFrame(
            [[1.0, 1.3], [1.1, nan], [nan, 1.4], [1.2, nan]], columns=["cat", "dog"]
        )
        tpls = [("a", 1), ("a", 2), ("b", nan), ("b", 1)]
        right.index = pd.MultiIndex.from_tuples(tpls)
        assert_frame_equal(ts.unstack(level=0), right)

    def test_value_counts_datetime(self):
        # most dtypes are tested in test_base.py
        values = [
            pd.Timestamp("2011-01-01 09:00"),
            pd.Timestamp("2011-01-01 10:00"),
            pd.Timestamp("2011-01-01 11:00"),
            pd.Timestamp("2011-01-01 09:00"),
            pd.Timestamp("2011-01-01 09:00"),
            pd.Timestamp("2011-01-01 11:00"),
        ]

        exp_idx = pd.DatetimeIndex(
            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"]
        )
        exp = pd.Series([3, 2, 1], index=exp_idx, name="xxx")

        s = pd.Series(values, name="xxx")
        tm.assert_series_equal(s.value_counts(), exp)
        # check DatetimeIndex outputs the same result
        idx = pd.DatetimeIndex(values, name="xxx")
        tm.assert_series_equal(idx.value_counts(), exp)

        # normalize
        exp = pd.Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
        tm.assert_series_equal(s.value_counts(normalize=True), exp)
        tm.assert_series_equal(idx.value_counts(normalize=True), exp)

    def test_value_counts_datetime_tz(self):
        values = [
            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
            pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"),
            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
        ]

        exp_idx = pd.DatetimeIndex(
            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
            tz="US/Eastern",
        )
        exp = pd.Series([3, 2, 1], index=exp_idx, name="xxx")

        s = pd.Series(values, name="xxx")
        tm.assert_series_equal(s.value_counts(), exp)
        idx = pd.DatetimeIndex(values, name="xxx")
        tm.assert_series_equal(idx.value_counts(), exp)

        exp = pd.Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
        tm.assert_series_equal(s.value_counts(normalize=True), exp)
        tm.assert_series_equal(idx.value_counts(normalize=True), exp)

    def test_value_counts_period(self):
        values = [
            pd.Period("2011-01", freq="M"),
            pd.Period("2011-02", freq="M"),
            pd.Period("2011-03", freq="M"),
            pd.Period("2011-01", freq="M"),
            pd.Period("2011-01", freq="M"),
            pd.Period("2011-03", freq="M"),
        ]

        exp_idx = pd.PeriodIndex(["2011-01", "2011-03", "2011-02"], freq="M")
        exp = pd.Series([3, 2, 1], index=exp_idx, name="xxx")

        s = pd.Series(values, name="xxx")
        tm.assert_series_equal(s.value_counts(), exp)
        # check DatetimeIndex outputs the same result
        idx = pd.PeriodIndex(values, name="xxx")
        tm.assert_series_equal(idx.value_counts(), exp)

        # normalize
        exp = pd.Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
        tm.assert_series_equal(s.value_counts(normalize=True), exp)
        tm.assert_series_equal(idx.value_counts(normalize=True), exp)

    def test_value_counts_categorical_ordered(self):
        # most dtypes are tested in test_base.py
        values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True)

        exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=True)
        exp = pd.Series([3, 2, 1], index=exp_idx, name="xxx")

        s = pd.Series(values, name="xxx")
        tm.assert_series_equal(s.value_counts(), exp)
        # check CategoricalIndex outputs the same result
        idx = pd.CategoricalIndex(values, name="xxx")
        tm.assert_series_equal(idx.value_counts(), exp)

        # normalize
        exp = pd.Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
        tm.assert_series_equal(s.value_counts(normalize=True), exp)
        tm.assert_series_equal(idx.value_counts(normalize=True), exp)

    def test_value_counts_categorical_not_ordered(self):
        values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=False)

        exp_idx = pd.CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=False)
        exp = pd.Series([3, 2, 1], index=exp_idx, name="xxx")

        s = pd.Series(values, name="xxx")
        tm.assert_series_equal(s.value_counts(), exp)
        # check CategoricalIndex outputs the same result
        idx = pd.CategoricalIndex(values, name="xxx")
        tm.assert_series_equal(idx.value_counts(), exp)

        # normalize
        exp = pd.Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
        tm.assert_series_equal(s.value_counts(normalize=True), exp)
        tm.assert_series_equal(idx.value_counts(normalize=True), exp)

    @pytest.mark.parametrize("func", [np.any, np.all])
    @pytest.mark.parametrize("kwargs", [dict(keepdims=True), dict(out=object())])
    @td.skip_if_np_lt("1.15")
    def test_validate_any_all_out_keepdims_raises(self, kwargs, func):
        s = pd.Series([1, 2])
        param = list(kwargs)[0]
        name = func.__name__

        msg = (
            r"the '{arg}' parameter is not "
            r"supported in the pandas "
            r"implementation of {fname}\(\)"
        ).format(arg=param, fname=name)
        with pytest.raises(ValueError, match=msg):
            func(s, **kwargs)

    @td.skip_if_np_lt("1.15")
    def test_validate_sum_initial(self):
        s = pd.Series([1, 2])
        msg = (
            r"the 'initial' parameter is not "
            r"supported in the pandas "
            r"implementation of sum\(\)"
        )
        with pytest.raises(ValueError, match=msg):
            np.sum(s, initial=10)

    def test_validate_median_initial(self):
        s = pd.Series([1, 2])
        msg = (
            r"the 'overwrite_input' parameter is not "
            r"supported in the pandas "
            r"implementation of median\(\)"
        )
        with pytest.raises(ValueError, match=msg):
            # It seems like np.median doesn't dispatch, so we use the
            # method instead of the ufunc.
            s.median(overwrite_input=True)

    @td.skip_if_np_lt("1.15")
    def test_validate_stat_keepdims(self):
        s = pd.Series([1, 2])
        msg = (
            r"the 'keepdims' parameter is not "
            r"supported in the pandas "
            r"implementation of sum\(\)"
        )
        with pytest.raises(ValueError, match=msg):
            np.sum(s, keepdims=True)

    def test_compound_deprecated(self):
        s = Series([0.1, 0.2, 0.3, 0.4])
        with tm.assert_produces_warning(FutureWarning):
            s.compound()

        df = pd.DataFrame({"s": s})
        with tm.assert_produces_warning(FutureWarning):
            df.compound()


main_dtypes = [
    "datetime",
    "datetimetz",
    "timedelta",
    "int8",
    "int16",
    "int32",
    "int64",
    "float32",
    "float64",
    "uint8",
    "uint16",
    "uint32",
    "uint64",
]


@pytest.fixture
def s_main_dtypes():
    """A DataFrame with many dtypes

    * datetime
    * datetimetz
    * timedelta
    * [u]int{8,16,32,64}
    * float{32,64}

    The columns are the name of the dtype.
    """
    df = pd.DataFrame(
        {
            "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
            "datetimetz": pd.to_datetime(
                ["2003", "2002", "2001", "2002", "2005"]
            ).tz_localize("US/Eastern"),
            "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
        }
    )

    for dtype in [
        "int8",
        "int16",
        "int32",
        "int64",
        "float32",
        "float64",
        "uint8",
        "uint16",
        "uint32",
        "uint64",
    ]:
        df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)

    return df


@pytest.fixture(params=main_dtypes)
def s_main_dtypes_split(request, s_main_dtypes):
    """Each series in s_main_dtypes."""
    return s_main_dtypes[request.param]


def assert_check_nselect_boundary(vals, dtype, method):
    # helper function for 'test_boundary_{dtype}' tests
    s = Series(vals, dtype=dtype)
    result = getattr(s, method)(3)
    expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
    expected = s.loc[expected_idxr]
    tm.assert_series_equal(result, expected)


class TestNLargestNSmallest:
    @pytest.mark.parametrize(
        "r",
        [
            Series([3.0, 2, 1, 2, "5"], dtype="object"),
            Series([3.0, 2, 1, 2, 5], dtype="object"),
            # not supported on some archs
            # Series([3., 2, 1, 2, 5], dtype='complex256'),
            Series([3.0, 2, 1, 2, 5], dtype="complex128"),
            Series(list("abcde")),
            Series(list("abcde"), dtype="category"),
        ],
    )
    def test_error(self, r):
        dt = r.dtype
        msg = "Cannot use method 'n(larg|small)est' with dtype {dt}".format(dt=dt)
        args = 2, len(r), 0, -1
        methods = r.nlargest, r.nsmallest
        for method, arg in product(methods, args):
            with pytest.raises(TypeError, match=msg):
                method(arg)

    def test_nsmallest_nlargest(self, s_main_dtypes_split):
        # float, int, datetime64 (use i8), timedelts64 (same),
        # object that are numbers, object that are strings
        s = s_main_dtypes_split

        assert_series_equal(s.nsmallest(2), s.iloc[[2, 1]])
        assert_series_equal(s.nsmallest(2, keep="last"), s.iloc[[2, 3]])

        empty = s.iloc[0:0]
        assert_series_equal(s.nsmallest(0), empty)
        assert_series_equal(s.nsmallest(-1), empty)
        assert_series_equal(s.nlargest(0), empty)
        assert_series_equal(s.nlargest(-1), empty)

        assert_series_equal(s.nsmallest(len(s)), s.sort_values())
        assert_series_equal(s.nsmallest(len(s) + 1), s.sort_values())
        assert_series_equal(s.nlargest(len(s)), s.iloc[[4, 0, 1, 3, 2]])
        assert_series_equal(s.nlargest(len(s) + 1), s.iloc[[4, 0, 1, 3, 2]])

    def test_misc(self):

        s = Series([3.0, np.nan, 1, 2, 5])
        assert_series_equal(s.nlargest(), s.iloc[[4, 0, 3, 2]])
        assert_series_equal(s.nsmallest(), s.iloc[[2, 3, 0, 4]])

        msg = 'keep must be either "first", "last"'
        with pytest.raises(ValueError, match=msg):
            s.nsmallest(keep="invalid")
        with pytest.raises(ValueError, match=msg):
            s.nlargest(keep="invalid")

        # GH 15297
        s = Series([1] * 5, index=[1, 2, 3, 4, 5])
        expected_first = Series([1] * 3, index=[1, 2, 3])
        expected_last = Series([1] * 3, index=[5, 4, 3])

        result = s.nsmallest(3)
        assert_series_equal(result, expected_first)

        result = s.nsmallest(3, keep="last")
        assert_series_equal(result, expected_last)

        result = s.nlargest(3)
        assert_series_equal(result, expected_first)

        result = s.nlargest(3, keep="last")
        assert_series_equal(result, expected_last)

    @pytest.mark.parametrize("n", range(1, 5))
    def test_n(self, n):

        # GH 13412
        s = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
        result = s.nlargest(n)
        expected = s.sort_values(ascending=False).head(n)
        assert_series_equal(result, expected)

        result = s.nsmallest(n)
        expected = s.sort_values().head(n)
        assert_series_equal(result, expected)

    def test_boundary_integer(self, nselect_method, any_int_dtype):
        # GH 21426
        dtype_info = np.iinfo(any_int_dtype)
        min_val, max_val = dtype_info.min, dtype_info.max
        vals = [min_val, min_val + 1, max_val - 1, max_val]
        assert_check_nselect_boundary(vals, any_int_dtype, nselect_method)

    def test_boundary_float(self, nselect_method, float_dtype):
        # GH 21426
        dtype_info = np.finfo(float_dtype)
        min_val, max_val = dtype_info.min, dtype_info.max
        min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_dtype)
        vals = [min_val, min_2nd, max_2nd, max_val]
        assert_check_nselect_boundary(vals, float_dtype, nselect_method)

    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
    def test_boundary_datetimelike(self, nselect_method, dtype):
        # GH 21426
        # use int64 bounds and +1 to min_val since true minimum is NaT
        # (include min_val/NaT at end to maintain same expected_idxr)
        dtype_info = np.iinfo("int64")
        min_val, max_val = dtype_info.min, dtype_info.max
        vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
        assert_check_nselect_boundary(vals, dtype, nselect_method)

    def test_duplicate_keep_all_ties(self):
        # see gh-16818
        s = Series([10, 9, 8, 7, 7, 7, 7, 6])
        result = s.nlargest(4, keep="all")
        expected = Series([10, 9, 8, 7, 7, 7, 7])
        assert_series_equal(result, expected)

        result = s.nsmallest(2, keep="all")
        expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
        assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
    )
    def test_boolean(self, data, expected):
        # GH 26154 : ensure True > False
        s = Series(data)
        result = s.nlargest(1)
        expected = Series(expected)
        assert_series_equal(result, expected)


class TestCategoricalSeriesAnalytics:
    def test_count(self):

        s = Series(
            Categorical(
                [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
            )
        )
        result = s.count()
        assert result == 2

    def test_value_counts(self):
        # GH 12835
        cats = Categorical(list("abcccb"), categories=list("cabd"))
        s = Series(cats, name="xxx")
        res = s.value_counts(sort=False)

        exp_index = CategoricalIndex(list("cabd"), categories=cats.categories)
        exp = Series([3, 1, 2, 0], name="xxx", index=exp_index)
        tm.assert_series_equal(res, exp)

        res = s.value_counts(sort=True)

        exp_index = CategoricalIndex(list("cbad"), categories=cats.categories)
        exp = Series([3, 2, 1, 0], name="xxx", index=exp_index)
        tm.assert_series_equal(res, exp)

        # check object dtype handles the Series.name as the same
        # (tested in test_base.py)
        s = Series(["a", "b", "c", "c", "c", "b"], name="xxx")
        res = s.value_counts()
        exp = Series([3, 2, 1], name="xxx", index=["c", "b", "a"])
        tm.assert_series_equal(res, exp)

    def test_value_counts_with_nan(self):
        # see gh-9443

        # sanity check
        s = Series(["a", "b", "a"], dtype="category")
        exp = Series([2, 1], index=CategoricalIndex(["a", "b"]))

        res = s.value_counts(dropna=True)
        tm.assert_series_equal(res, exp)

        res = s.value_counts(dropna=True)
        tm.assert_series_equal(res, exp)

        # same Series via two different constructions --> same behaviour
        series = [
            Series(["a", "b", None, "a", None, None], dtype="category"),
            Series(
                Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])
            ),
        ]

        for s in series:
            # None is a NaN value, so we exclude its count here
            exp = Series([2, 1], index=CategoricalIndex(["a", "b"]))
            res = s.value_counts(dropna=True)
            tm.assert_series_equal(res, exp)

            # we don't exclude the count of None and sort by counts
            exp = Series([3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]))
            res = s.value_counts(dropna=False)
            tm.assert_series_equal(res, exp)

            # When we aren't sorting by counts, and np.nan isn't a
            # category, it should be last.
            exp = Series([2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]))
            res = s.value_counts(dropna=False, sort=False)
            tm.assert_series_equal(res, exp)

    @pytest.mark.parametrize(
        "dtype",
        [
            "int_",
            "uint",
            "float_",
            "unicode_",
            "timedelta64[h]",
            pytest.param(
                "datetime64[D]", marks=pytest.mark.xfail(reason="GH#7996", strict=False)
            ),
        ],
    )
    def test_drop_duplicates_categorical_non_bool(self, dtype, ordered_fixture):
        cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))

        # Test case 1
        input1 = np.array([1, 2, 3, 3], dtype=np.dtype(dtype))
        tc1 = Series(Categorical(input1, categories=cat_array, ordered=ordered_fixture))

        expected = Series([False, False, False, True])
        tm.assert_series_equal(tc1.duplicated(), expected)
        tm.assert_series_equal(tc1.drop_duplicates(), tc1[~expected])
        sc = tc1.copy()
        sc.drop_duplicates(inplace=True)
        tm.assert_series_equal(sc, tc1[~expected])

        expected = Series([False, False, True, False])
        tm.assert_series_equal(tc1.duplicated(keep="last"), expected)
        tm.assert_series_equal(tc1.drop_duplicates(keep="last"), tc1[~expected])
        sc = tc1.copy()
        sc.drop_duplicates(keep="last", inplace=True)
        tm.assert_series_equal(sc, tc1[~expected])

        expected = Series([False, False, True, True])
        tm.assert_series_equal(tc1.duplicated(keep=False), expected)
        tm.assert_series_equal(tc1.drop_duplicates(keep=False), tc1[~expected])
        sc = tc1.copy()
        sc.drop_duplicates(keep=False, inplace=True)
        tm.assert_series_equal(sc, tc1[~expected])

        # Test case 2
        input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype))
        tc2 = Series(Categorical(input2, categories=cat_array, ordered=ordered_fixture))

        expected = Series([False, False, False, False, True, True, False])
        tm.assert_series_equal(tc2.duplicated(), expected)
        tm.assert_series_equal(tc2.drop_duplicates(), tc2[~expected])
        sc = tc2.copy()
        sc.drop_duplicates(inplace=True)
        tm.assert_series_equal(sc, tc2[~expected])

        expected = Series([False, True, True, False, False, False, False])
        tm.assert_series_equal(tc2.duplicated(keep="last"), expected)
        tm.assert_series_equal(tc2.drop_duplicates(keep="last"), tc2[~expected])
        sc = tc2.copy()
        sc.drop_duplicates(keep="last", inplace=True)
        tm.assert_series_equal(sc, tc2[~expected])

        expected = Series([False, True, True, False, True, True, False])
        tm.assert_series_equal(tc2.duplicated(keep=False), expected)
        tm.assert_series_equal(tc2.drop_duplicates(keep=False), tc2[~expected])
        sc = tc2.copy()
        sc.drop_duplicates(keep=False, inplace=True)
        tm.assert_series_equal(sc, tc2[~expected])

    def test_drop_duplicates_categorical_bool(self, ordered_fixture):
        tc = Series(
            Categorical(
                [True, False, True, False],
                categories=[True, False],
                ordered=ordered_fixture,
            )
        )

        expected = Series([False, False, True, True])
        tm.assert_series_equal(tc.duplicated(), expected)
        tm.assert_series_equal(tc.drop_duplicates(), tc[~expected])
        sc = tc.copy()
        sc.drop_duplicates(inplace=True)
        tm.assert_series_equal(sc, tc[~expected])

        expected = Series([True, True, False, False])
        tm.assert_series_equal(tc.duplicated(keep="last"), expected)
        tm.assert_series_equal(tc.drop_duplicates(keep="last"), tc[~expected])
        sc = tc.copy()
        sc.drop_duplicates(keep="last", inplace=True)
        tm.assert_series_equal(sc, tc[~expected])

        expected = Series([True, True, True, True])
        tm.assert_series_equal(tc.duplicated(keep=False), expected)
        tm.assert_series_equal(tc.drop_duplicates(keep=False), tc[~expected])
        sc = tc.copy()
        sc.drop_duplicates(keep=False, inplace=True)
        tm.assert_series_equal(sc, tc[~expected])