"""
Tests for statistical reductions of 2nd moment or higher: var, skew, kurt, ...
"""
import inspect

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (
    DataFrame,
    Series,
)
import pandas._testing as tm
from pandas.core.arrays import (
    DatetimeArray,
    PeriodArray,
    TimedeltaArray,
)


class TestDatetimeLikeStatReductions:
    @pytest.mark.parametrize("box", [Series, pd.Index, DatetimeArray])
    def test_dt64_mean(self, tz_naive_fixture, box):
        tz = tz_naive_fixture

        dti = pd.date_range("2001-01-01", periods=11, tz=tz)
        # shuffle so that we are not just working with monotone-increasing
        dti = dti.take([4, 1, 3, 10, 9, 7, 8, 5, 0, 2, 6])
        dtarr = dti._data

        obj = box(dtarr)
        assert obj.mean() == pd.Timestamp("2001-01-06", tz=tz)
        assert obj.mean(skipna=False) == pd.Timestamp("2001-01-06", tz=tz)

        # dtarr[-2] will be the first date 2001-01-1
        dtarr[-2] = pd.NaT

        obj = box(dtarr)
        assert obj.mean() == pd.Timestamp("2001-01-06 07:12:00", tz=tz)
        assert obj.mean(skipna=False) is pd.NaT

    @pytest.mark.parametrize("box", [Series, pd.Index, PeriodArray])
    @pytest.mark.parametrize("freq", ["S", "H", "D", "W", "B"])
    def test_period_mean(self, box, freq):
        # GH#24757
        dti = pd.date_range("2001-01-01", periods=11)
        # shuffle so that we are not just working with monotone-increasing
        dti = dti.take([4, 1, 3, 10, 9, 7, 8, 5, 0, 2, 6])

        parr = dti._data.to_period(freq)
        obj = box(parr)
        with pytest.raises(TypeError, match="ambiguous"):
            obj.mean()
        with pytest.raises(TypeError, match="ambiguous"):
            obj.mean(skipna=True)

        # parr[-2] will be the first date 2001-01-1
        parr[-2] = pd.NaT

        with pytest.raises(TypeError, match="ambiguous"):
            obj.mean()
        with pytest.raises(TypeError, match="ambiguous"):
            obj.mean(skipna=True)

    @pytest.mark.parametrize("box", [Series, pd.Index, TimedeltaArray])
    def test_td64_mean(self, box):
        tdi = pd.TimedeltaIndex([0, 3, -2, -7, 1, 2, -1, 3, 5, -2, 4], unit="D")

        tdarr = tdi._data
        obj = box(tdarr)

        result = obj.mean()
        expected = np.array(tdarr).mean()
        assert result == expected

        tdarr[0] = pd.NaT
        assert obj.mean(skipna=False) is pd.NaT

        result2 = obj.mean(skipna=True)
        assert result2 == tdi[1:].mean()

        # exact equality fails by 1 nanosecond
        assert result2.round("us") == (result * 11.0 / 10).round("us")


class TestSeriesStatReductions:
    # Note: the name TestSeriesStatReductions indicates these tests
    #  were moved from a series-specific test file, _not_ that these tests are
    #  intended long-term to be series-specific

    def _check_stat_op(
        self, name, alternate, string_series_, check_objects=False, check_allna=False
    ):

        with pd.option_context("use_bottleneck", False):
            f = getattr(Series, name)

            # add some NaNs
            string_series_[5:15] = np.NaN

            # mean, idxmax, idxmin, min, and max are valid for dates
            if name not in ["max", "min", "mean", "median", "std"]:
                ds = Series(pd.date_range("1/1/2001", periods=10))
                msg = f"does not support reduction '{name}'"
                with pytest.raises(TypeError, match=msg):
                    f(ds)

            # skipna or no
            assert pd.notna(f(string_series_))
            assert pd.isna(f(string_series_, skipna=False))

            # check the result is correct
            nona = string_series_.dropna()
            tm.assert_almost_equal(f(nona), alternate(nona.values))
            tm.assert_almost_equal(f(string_series_), alternate(nona.values))

            allna = string_series_ * np.nan

            if check_allna:
                assert np.isnan(f(allna))

            # dtype=object with None, it works!
            s = Series([1, 2, 3, None, 5])
            f(s)

            # GH#2888
            items = [0]
            items.extend(range(2**40, 2**40 + 1000))
            s = Series(items, dtype="int64")
            tm.assert_almost_equal(float(f(s)), float(alternate(s.values)))

            # check date range
            if check_objects:
                s = Series(pd.bdate_range("1/1/2000", periods=10))
                res = f(s)
                exp = alternate(s)
                assert res == exp

            # check on string data
            if name not in ["sum", "min", "max"]:
                with pytest.raises(TypeError, match=None):
                    f(Series(list("abc")))

            # Invalid axis.
            msg = "No axis named 1 for object type Series"
            with pytest.raises(ValueError, match=msg):
                f(string_series_, axis=1)

            # Unimplemented numeric_only parameter.
            if "numeric_only" in inspect.getfullargspec(f).args:
                with pytest.raises(NotImplementedError, match=name):
                    f(string_series_, numeric_only=True)

    def test_sum(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("sum", np.sum, string_series, check_allna=False)

    def test_mean(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("mean", np.mean, string_series)

    def test_median(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("median", np.median, string_series)

        # test with integers, test failure
        int_ts = Series(np.ones(10, dtype=int), index=range(10))
        tm.assert_almost_equal(np.median(int_ts), int_ts.median())

    def test_prod(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("prod", np.prod, string_series)

    def test_min(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("min", np.min, string_series, check_objects=True)

    def test_max(self):
        string_series = tm.makeStringSeries().rename("series")
        self._check_stat_op("max", np.max, string_series, check_objects=True)

    def test_var_std(self):
        string_series = tm.makeStringSeries().rename("series")
        datetime_series = tm.makeTimeSeries().rename("ts")

        alt = lambda x: np.std(x, ddof=1)
        self._check_stat_op("std", alt, string_series)

        alt = lambda x: np.var(x, ddof=1)
        self._check_stat_op("var", alt, string_series)

        result = datetime_series.std(ddof=4)
        expected = np.std(datetime_series.values, ddof=4)
        tm.assert_almost_equal(result, expected)

        result = datetime_series.var(ddof=4)
        expected = np.var(datetime_series.values, ddof=4)
        tm.assert_almost_equal(result, expected)

        # 1 - element series with ddof=1
        s = datetime_series.iloc[[0]]
        result = s.var(ddof=1)
        assert pd.isna(result)

        result = s.std(ddof=1)
        assert pd.isna(result)

    def test_sem(self):
        string_series = tm.makeStringSeries().rename("series")
        datetime_series = tm.makeTimeSeries().rename("ts")

        alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x))
        self._check_stat_op("sem", alt, string_series)

        result = datetime_series.sem(ddof=4)
        expected = np.std(datetime_series.values, ddof=4) / np.sqrt(
            len(datetime_series.values)
        )
        tm.assert_almost_equal(result, expected)

        # 1 - element series with ddof=1
        s = datetime_series.iloc[[0]]
        result = s.sem(ddof=1)
        assert pd.isna(result)

    @td.skip_if_no_scipy
    def test_skew(self):
        from scipy.stats import skew

        string_series = tm.makeStringSeries().rename("series")

        alt = lambda x: skew(x, bias=False)
        self._check_stat_op("skew", alt, string_series)

        # test corner cases, skew() returns NaN unless there's at least 3
        # values
        min_N = 3
        for i in range(1, min_N + 1):
            s = Series(np.ones(i))
            df = DataFrame(np.ones((i, i)))
            if i < min_N:
                assert np.isnan(s.skew())
                assert np.isnan(df.skew()).all()
            else:
                assert 0 == s.skew()
                assert (df.skew() == 0).all()

    @td.skip_if_no_scipy
    def test_kurt(self):
        from scipy.stats import kurtosis

        string_series = tm.makeStringSeries().rename("series")

        alt = lambda x: kurtosis(x, bias=False)
        self._check_stat_op("kurt", alt, string_series)

        index = pd.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)
        with tm.assert_produces_warning(FutureWarning):
            tm.assert_almost_equal(s.kurt(), s.kurt(level=0)["bar"])

        # test corner cases, kurt() returns NaN unless there's at least 4
        # values
        min_N = 4
        for i in range(1, min_N + 1):
            s = Series(np.ones(i))
            df = DataFrame(np.ones((i, i)))
            if i < min_N:
                assert np.isnan(s.kurt())
                assert np.isnan(df.kurt()).all()
            else:
                assert 0 == s.kurt()
                assert (df.kurt() == 0).all()
