from collections import OrderedDict
from itertools import product
import pytest
import warnings
from warnings import catch_warnings

from datetime import datetime, timedelta
from numpy.random import randn
import numpy as np
from pandas import _np_version_under1p12

import pandas as pd
from pandas import (Series, DataFrame, bdate_range,
                    isna, notna, concat, Timestamp, Index)
import pandas.core.window as rwindow
import pandas.tseries.offsets as offsets
from pandas.core.base import SpecificationError
from pandas.errors import UnsupportedFunctionCall
from pandas.core.sorting import safe_sort
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.compat import range, zip

N, K = 100, 10


def assert_equal(left, right):
    if isinstance(left, Series):
        tm.assert_series_equal(left, right)
    else:
        tm.assert_frame_equal(left, right)


@pytest.fixture(params=[True, False])
def raw(request):
    return request.param


@pytest.fixture(params=['triang', 'blackman', 'hamming', 'bartlett', 'bohman',
                        'blackmanharris', 'nuttall', 'barthann'])
def win_types(request):
    return request.param


@pytest.fixture(params=['kaiser', 'gaussian', 'general_gaussian'])
def win_types_special(request):
    return request.param


class Base(object):

    _nan_locs = np.arange(20, 40)
    _inf_locs = np.array([])

    def _create_data(self):
        arr = randn(N)
        arr[self._nan_locs] = np.NaN

        self.arr = arr
        self.rng = bdate_range(datetime(2009, 1, 1), periods=N)
        self.series = Series(arr.copy(), index=self.rng)
        self.frame = DataFrame(randn(N, K), index=self.rng,
                               columns=np.arange(K))


class TestApi(Base):

    def setup_method(self, method):
        self._create_data()

    def test_getitem(self):

        r = self.frame.rolling(window=5)
        tm.assert_index_equal(r._selected_obj.columns, self.frame.columns)

        r = self.frame.rolling(window=5)[1]
        assert r._selected_obj.name == self.frame.columns[1]

        # technically this is allowed
        r = self.frame.rolling(window=5)[1, 3]
        tm.assert_index_equal(r._selected_obj.columns,
                              self.frame.columns[[1, 3]])

        r = self.frame.rolling(window=5)[[1, 3]]
        tm.assert_index_equal(r._selected_obj.columns,
                              self.frame.columns[[1, 3]])

    def test_select_bad_cols(self):
        df = DataFrame([[1, 2]], columns=['A', 'B'])
        g = df.rolling(window=5)
        pytest.raises(KeyError, g.__getitem__, ['C'])  # g[['C']]

        pytest.raises(KeyError, g.__getitem__, ['A', 'C'])  # g[['A', 'C']]
        with tm.assert_raises_regex(KeyError, '^[^A]+$'):
            # A should not be referenced as a bad column...
            # will have to rethink regex if you change message!
            g[['A', 'C']]

    def test_attribute_access(self):

        df = DataFrame([[1, 2]], columns=['A', 'B'])
        r = df.rolling(window=5)
        tm.assert_series_equal(r.A.sum(), r['A'].sum())
        pytest.raises(AttributeError, lambda: r.F)

    def tests_skip_nuisance(self):

        df = DataFrame({'A': range(5), 'B': range(5, 10), 'C': 'foo'})
        r = df.rolling(window=3)
        result = r[['A', 'B']].sum()
        expected = DataFrame({'A': [np.nan, np.nan, 3, 6, 9],
                              'B': [np.nan, np.nan, 18, 21, 24]},
                             columns=list('AB'))
        tm.assert_frame_equal(result, expected)

    def test_skip_sum_object_raises(self):
        df = DataFrame({'A': range(5), 'B': range(5, 10), 'C': 'foo'})
        r = df.rolling(window=3)

        with tm.assert_raises_regex(TypeError, 'cannot handle this type'):
            r.sum()

    def test_agg(self):
        df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})

        r = df.rolling(window=3)
        a_mean = r['A'].mean()
        a_std = r['A'].std()
        a_sum = r['A'].sum()
        b_mean = r['B'].mean()
        b_std = r['B'].std()
        b_sum = r['B'].sum()

        result = r.aggregate([np.mean, np.std])
        expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
        expected.columns = pd.MultiIndex.from_product([['A', 'B'], ['mean',
                                                                    'std']])
        tm.assert_frame_equal(result, expected)

        result = r.aggregate({'A': np.mean, 'B': np.std})

        expected = concat([a_mean, b_std], axis=1)
        tm.assert_frame_equal(result, expected, check_like=True)

        result = r.aggregate({'A': ['mean', 'std']})
        expected = concat([a_mean, a_std], axis=1)
        expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'), ('A',
                                                                      'std')])
        tm.assert_frame_equal(result, expected)

        result = r['A'].aggregate(['mean', 'sum'])
        expected = concat([a_mean, a_sum], axis=1)
        expected.columns = ['mean', 'sum']
        tm.assert_frame_equal(result, expected)

        with catch_warnings(record=True):
            # using a dict with renaming
            warnings.simplefilter("ignore", FutureWarning)
            result = r.aggregate({'A': {'mean': 'mean', 'sum': 'sum'}})
        expected = concat([a_mean, a_sum], axis=1)
        expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
                                                      ('A', 'sum')])
        tm.assert_frame_equal(result, expected, check_like=True)

        with catch_warnings(record=True):
            warnings.simplefilter("ignore", FutureWarning)
            result = r.aggregate({'A': {'mean': 'mean',
                                        'sum': 'sum'},
                                  'B': {'mean2': 'mean',
                                        'sum2': 'sum'}})
        expected = concat([a_mean, a_sum, b_mean, b_sum], axis=1)
        exp_cols = [('A', 'mean'), ('A', 'sum'), ('B', 'mean2'), ('B', 'sum2')]
        expected.columns = pd.MultiIndex.from_tuples(exp_cols)
        tm.assert_frame_equal(result, expected, check_like=True)

        result = r.aggregate({'A': ['mean', 'std'], 'B': ['mean', 'std']})
        expected = concat([a_mean, a_std, b_mean, b_std], axis=1)

        exp_cols = [('A', 'mean'), ('A', 'std'), ('B', 'mean'), ('B', 'std')]
        expected.columns = pd.MultiIndex.from_tuples(exp_cols)
        tm.assert_frame_equal(result, expected, check_like=True)

    def test_agg_apply(self, raw):

        # passed lambda
        df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})

        r = df.rolling(window=3)
        a_sum = r['A'].sum()

        result = r.agg({'A': np.sum, 'B': lambda x: np.std(x, ddof=1)})
        rcustom = r['B'].apply(lambda x: np.std(x, ddof=1), raw=raw)
        expected = concat([a_sum, rcustom], axis=1)
        tm.assert_frame_equal(result, expected, check_like=True)

    def test_agg_consistency(self):

        df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
        r = df.rolling(window=3)

        result = r.agg([np.sum, np.mean]).columns
        expected = pd.MultiIndex.from_product([list('AB'), ['sum', 'mean']])
        tm.assert_index_equal(result, expected)

        result = r['A'].agg([np.sum, np.mean]).columns
        expected = Index(['sum', 'mean'])
        tm.assert_index_equal(result, expected)

        result = r.agg({'A': [np.sum, np.mean]}).columns
        expected = pd.MultiIndex.from_tuples([('A', 'sum'), ('A', 'mean')])
        tm.assert_index_equal(result, expected)

    def test_agg_nested_dicts(self):

        # API change for disallowing these types of nested dicts
        df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
        r = df.rolling(window=3)

        def f():
            r.aggregate({'r1': {'A': ['mean', 'sum']},
                         'r2': {'B': ['mean', 'sum']}})

        pytest.raises(SpecificationError, f)

        expected = concat([r['A'].mean(), r['A'].std(),
                           r['B'].mean(), r['B'].std()], axis=1)
        expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
            'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
        with catch_warnings(record=True):
            warnings.simplefilter("ignore", FutureWarning)
            result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
                                        'B': {'rb': ['mean', 'std']}})
        tm.assert_frame_equal(result, expected, check_like=True)

        with catch_warnings(record=True):
            warnings.simplefilter("ignore", FutureWarning)
            result = r.agg({'A': {'ra': ['mean', 'std']},
                            'B': {'rb': ['mean', 'std']}})
        expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
            'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
        tm.assert_frame_equal(result, expected, check_like=True)

    def test_count_nonnumeric_types(self):
        # GH12541
        cols = ['int', 'float', 'string', 'datetime', 'timedelta', 'periods',
                'fl_inf', 'fl_nan', 'str_nan', 'dt_nat', 'periods_nat']

        df = DataFrame(
            {'int': [1, 2, 3],
             'float': [4., 5., 6.],
             'string': list('abc'),
             'datetime': pd.date_range('20170101', periods=3),
             'timedelta': pd.timedelta_range('1 s', periods=3, freq='s'),
             'periods': [pd.Period('2012-01'), pd.Period('2012-02'),
                         pd.Period('2012-03')],
             'fl_inf': [1., 2., np.Inf],
             'fl_nan': [1., 2., np.NaN],
             'str_nan': ['aa', 'bb', np.NaN],
             'dt_nat': [Timestamp('20170101'), Timestamp('20170203'),
                        Timestamp(None)],
             'periods_nat': [pd.Period('2012-01'), pd.Period('2012-02'),
                             pd.Period(None)]},
            columns=cols)

        expected = DataFrame(
            {'int': [1., 2., 2.],
             'float': [1., 2., 2.],
             'string': [1., 2., 2.],
             'datetime': [1., 2., 2.],
             'timedelta': [1., 2., 2.],
             'periods': [1., 2., 2.],
             'fl_inf': [1., 2., 2.],
             'fl_nan': [1., 2., 1.],
             'str_nan': [1., 2., 1.],
             'dt_nat': [1., 2., 1.],
             'periods_nat': [1., 2., 1.]},
            columns=cols)

        result = df.rolling(window=2).count()
        tm.assert_frame_equal(result, expected)

        result = df.rolling(1).count()
        expected = df.notna().astype(float)
        tm.assert_frame_equal(result, expected)

    @td.skip_if_no_scipy
    @pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
    def test_window_with_args(self):
        # make sure that we are aggregating window functions correctly with arg
        r = Series(np.random.randn(100)).rolling(window=10, min_periods=1,
                                                 win_type='gaussian')
        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['<lambda>', '<lambda>']
        result = r.aggregate([lambda x: x.mean(std=10),
                              lambda x: x.mean(std=.01)])
        tm.assert_frame_equal(result, expected)

        def a(x):
            return x.mean(std=10)

        def b(x):
            return x.mean(std=0.01)

        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['a', 'b']
        result = r.aggregate([a, b])
        tm.assert_frame_equal(result, expected)

    def test_preserve_metadata(self):
        # GH 10565
        s = Series(np.arange(100), name='foo')

        s2 = s.rolling(30).sum()
        s3 = s.rolling(20).sum()
        assert s2.name == 'foo'
        assert s3.name == 'foo'

    @pytest.mark.parametrize("func,window_size,expected_vals", [
        ('rolling', 2, [[np.nan, np.nan, np.nan, np.nan],
                        [15., 20., 25., 20.],
                        [25., 30., 35., 30.],
                        [np.nan, np.nan, np.nan, np.nan],
                        [20., 30., 35., 30.],
                        [35., 40., 60., 40.],
                        [60., 80., 85., 80]]),
        ('expanding', None, [[10., 10., 20., 20.],
                             [15., 20., 25., 20.],
                             [20., 30., 30., 20.],
                             [10., 10., 30., 30.],
                             [20., 30., 35., 30.],
                             [26.666667, 40., 50., 30.],
                             [40., 80., 60., 30.]])])
    def test_multiple_agg_funcs(self, func, window_size, expected_vals):
        # GH 15072
        df = pd.DataFrame([
            ['A', 10, 20],
            ['A', 20, 30],
            ['A', 30, 40],
            ['B', 10, 30],
            ['B', 30, 40],
            ['B', 40, 80],
            ['B', 80, 90]], columns=['stock', 'low', 'high'])

        f = getattr(df.groupby('stock'), func)
        if window_size:
            window = f(window_size)
        else:
            window = f()

        index = pd.MultiIndex.from_tuples([
            ('A', 0), ('A', 1), ('A', 2),
            ('B', 3), ('B', 4), ('B', 5), ('B', 6)], names=['stock', None])
        columns = pd.MultiIndex.from_tuples([
            ('low', 'mean'), ('low', 'max'), ('high', 'mean'),
            ('high', 'min')])
        expected = pd.DataFrame(expected_vals, index=index, columns=columns)

        result = window.agg(OrderedDict((
            ('low', ['mean', 'max']),
            ('high', ['mean', 'min']),
        )))

        tm.assert_frame_equal(result, expected)


@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestWindow(Base):

    def setup_method(self, method):
        self._create_data()

    @td.skip_if_no_scipy
    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor(self, which):
        # GH 12669

        o = getattr(self, which)
        c = o.rolling

        # valid
        c(win_type='boxcar', window=2, min_periods=1)
        c(win_type='boxcar', window=2, min_periods=1, center=True)
        c(win_type='boxcar', window=2, min_periods=1, center=False)

        # not valid
        for w in [2., 'foo', np.array([2])]:
            with pytest.raises(ValueError):
                c(win_type='boxcar', window=2, min_periods=w)
            with pytest.raises(ValueError):
                c(win_type='boxcar', window=2, min_periods=1, center=w)

        for wt in ['foobar', 1]:
            with pytest.raises(ValueError):
                c(win_type=wt, window=2)

    @td.skip_if_no_scipy
    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor_with_win_type(self, which, win_types):
        # GH 12669
        o = getattr(self, which)
        c = o.rolling
        c(win_type=win_types, window=2)

    @pytest.mark.parametrize(
        'method', ['sum', 'mean'])
    def test_numpy_compat(self, method):
        # see gh-12811
        w = rwindow.Window(Series([2, 4, 6]), window=[0, 2])

        msg = "numpy operations are not valid with window objects"

        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(w, method), 1, 2, 3)
        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(w, method), dtype=np.float64)


class TestRolling(Base):

    def setup_method(self, method):
        self._create_data()

    def test_doc_string(self):

        df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
        df
        df.rolling(2).sum()
        df.rolling(2, min_periods=1).sum()

    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor(self, which):
        # GH 12669

        o = getattr(self, which)
        c = o.rolling

        # valid
        c(window=2)
        c(window=2, min_periods=1)
        c(window=2, min_periods=1, center=True)
        c(window=2, min_periods=1, center=False)

        # GH 13383
        with pytest.raises(ValueError):
            c(0)
            c(-1)

        # not valid
        for w in [2., 'foo', np.array([2])]:
            with pytest.raises(ValueError):
                c(window=w)
            with pytest.raises(ValueError):
                c(window=2, min_periods=w)
            with pytest.raises(ValueError):
                c(window=2, min_periods=1, center=w)

    @td.skip_if_no_scipy
    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor_with_win_type(self, which):
        # GH 13383
        o = getattr(self, which)
        c = o.rolling
        with pytest.raises(ValueError):
            c(-1, win_type='boxcar')

    @pytest.mark.parametrize(
        'window', [timedelta(days=3), pd.Timedelta(days=3)])
    def test_constructor_with_timedelta_window(self, window):
        # GH 15440
        n = 10
        df = DataFrame({'value': np.arange(n)},
                       index=pd.date_range('2015-12-24', periods=n, freq="D"))
        expected_data = np.append([0., 1.], np.arange(3., 27., 3))

        result = df.rolling(window=window).sum()
        expected = DataFrame({'value': expected_data},
                             index=pd.date_range('2015-12-24', periods=n,
                                                 freq="D"))
        tm.assert_frame_equal(result, expected)
        expected = df.rolling('3D').sum()
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        'window', [timedelta(days=3), pd.Timedelta(days=3), '3D'])
    def test_constructor_timedelta_window_and_minperiods(self, window, raw):
        # GH 15305
        n = 10
        df = DataFrame({'value': np.arange(n)},
                       index=pd.date_range('2017-08-08', periods=n, freq="D"))
        expected = DataFrame(
            {'value': np.append([np.NaN, 1.], np.arange(3., 27., 3))},
            index=pd.date_range('2017-08-08', periods=n, freq="D"))
        result_roll_sum = df.rolling(window=window, min_periods=2).sum()
        result_roll_generic = df.rolling(window=window,
                                         min_periods=2).apply(sum, raw=raw)
        tm.assert_frame_equal(result_roll_sum, expected)
        tm.assert_frame_equal(result_roll_generic, expected)

    @pytest.mark.parametrize(
        'method', ['std', 'mean', 'sum', 'max', 'min', 'var'])
    def test_numpy_compat(self, method):
        # see gh-12811
        r = rwindow.Rolling(Series([2, 4, 6]), window=2)

        msg = "numpy operations are not valid with window objects"

        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(r, method), 1, 2, 3)
        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(r, method), dtype=np.float64)

    def test_closed(self):
        df = DataFrame({'A': [0, 1, 2, 3, 4]})
        # closed only allowed for datetimelike
        with pytest.raises(ValueError):
            df.rolling(window=3, closed='neither')

    @pytest.mark.parametrize("input_dtype", ['int', 'float'])
    @pytest.mark.parametrize("func,closed,expected", [
        ('min', 'right', [0.0, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
        ('min', 'both', [0.0, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
        ('min', 'neither', [np.nan, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
        ('min', 'left', [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
        ('max', 'right', [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
        ('max', 'both', [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
        ('max', 'neither', [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
        ('max', 'left', [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8])
    ])
    def test_closed_min_max_datetime(self, input_dtype,
                                     func, closed,
                                     expected):
        # see gh-21704
        ser = pd.Series(data=np.arange(10).astype(input_dtype),
                        index=pd.date_range('2000', periods=10))

        result = getattr(ser.rolling('3D', closed=closed), func)()
        expected = pd.Series(expected, index=ser.index)
        tm.assert_series_equal(result, expected)

    def test_closed_uneven(self):
        # see gh-21704
        ser = pd.Series(data=np.arange(10),
                        index=pd.date_range('2000', periods=10))

        # uneven
        ser = ser.drop(index=ser.index[[1, 5]])
        result = ser.rolling('3D', closed='left').min()
        expected = pd.Series([np.nan, 0, 0, 2, 3, 4, 6, 6],
                             index=ser.index)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("func,closed,expected", [
        ('min', 'right', [np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
        ('min', 'both', [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, np.nan]),
        ('min', 'neither', [np.nan, np.nan, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
        ('min', 'left', [np.nan, np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan]),
        ('max', 'right', [np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan, np.nan]),
        ('max', 'both', [np.nan, 1, 2, 3, 4, 5, 6, 6, 6, np.nan]),
        ('max', 'neither', [np.nan, np.nan, 1, 2, 3, 4, 5, 6, np.nan, np.nan]),
        ('max', 'left', [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan])
    ])
    def test_closed_min_max_minp(self, func, closed, expected):
        # see gh-21704
        ser = pd.Series(data=np.arange(10),
                        index=pd.date_range('2000', periods=10))
        ser[ser.index[-3:]] = np.nan
        result = getattr(ser.rolling('3D', min_periods=2, closed=closed),
                         func)()
        expected = pd.Series(expected, index=ser.index)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize('roller', ['1s', 1])
    def tests_empty_df_rolling(self, roller):
        # GH 15819 Verifies that datetime and integer rolling windows can be
        # applied to empty DataFrames
        expected = DataFrame()
        result = DataFrame().rolling(roller).sum()
        tm.assert_frame_equal(result, expected)

        # Verifies that datetime and integer rolling windows can be applied to
        # empty DataFrames with datetime index
        expected = DataFrame(index=pd.DatetimeIndex([]))
        result = DataFrame(index=pd.DatetimeIndex([])).rolling(roller).sum()
        tm.assert_frame_equal(result, expected)

    def test_missing_minp_zero(self):
        # https://github.com/pandas-dev/pandas/pull/18921
        # minp=0
        x = pd.Series([np.nan])
        result = x.rolling(1, min_periods=0).sum()
        expected = pd.Series([0.0])
        tm.assert_series_equal(result, expected)

        # minp=1
        result = x.rolling(1, min_periods=1).sum()
        expected = pd.Series([np.nan])
        tm.assert_series_equal(result, expected)

    def test_missing_minp_zero_variable(self):
        # https://github.com/pandas-dev/pandas/pull/18921
        x = pd.Series([np.nan] * 4,
                      index=pd.DatetimeIndex(['2017-01-01', '2017-01-04',
                                              '2017-01-06', '2017-01-07']))
        result = x.rolling(pd.Timedelta("2d"), min_periods=0).sum()
        expected = pd.Series(0.0, index=x.index)
        tm.assert_series_equal(result, expected)

    def test_multi_index_names(self):

        # GH 16789, 16825
        cols = pd.MultiIndex.from_product([['A', 'B'], ['C', 'D', 'E']],
                                          names=['1', '2'])
        df = DataFrame(np.ones((10, 6)), columns=cols)
        result = df.rolling(3).cov()

        tm.assert_index_equal(result.columns, df.columns)
        assert result.index.names == [None, '1', '2']

    @pytest.mark.parametrize('klass', [pd.Series, pd.DataFrame])
    def test_iter_raises(self, klass):
        # https://github.com/pandas-dev/pandas/issues/11704
        # Iteration over a Window
        obj = klass([1, 2, 3, 4])
        with pytest.raises(NotImplementedError):
            iter(obj.rolling(2))


class TestExpanding(Base):

    def setup_method(self, method):
        self._create_data()

    def test_doc_string(self):

        df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
        df
        df.expanding(2).sum()

    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor(self, which):
        # GH 12669

        o = getattr(self, which)
        c = o.expanding

        # valid
        c(min_periods=1)
        c(min_periods=1, center=True)
        c(min_periods=1, center=False)

        # not valid
        for w in [2., 'foo', np.array([2])]:
            with pytest.raises(ValueError):
                c(min_periods=w)
            with pytest.raises(ValueError):
                c(min_periods=1, center=w)

    @pytest.mark.parametrize(
        'method', ['std', 'mean', 'sum', 'max', 'min', 'var'])
    def test_numpy_compat(self, method):
        # see gh-12811
        e = rwindow.Expanding(Series([2, 4, 6]), window=2)

        msg = "numpy operations are not valid with window objects"

        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(e, method), 1, 2, 3)
        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(e, method), dtype=np.float64)

    @pytest.mark.parametrize(
        'expander',
        [1, pytest.param('ls', marks=pytest.mark.xfail(
                         reason='GH#16425 expanding with '
                                'offset not supported',
                         strict=True))])
    def test_empty_df_expanding(self, expander):
        # GH 15819 Verifies that datetime and integer expanding windows can be
        # applied to empty DataFrames

        expected = DataFrame()
        result = DataFrame().expanding(expander).sum()
        tm.assert_frame_equal(result, expected)

        # Verifies that datetime and integer expanding windows can be applied
        # to empty DataFrames with datetime index
        expected = DataFrame(index=pd.DatetimeIndex([]))
        result = DataFrame(
            index=pd.DatetimeIndex([])).expanding(expander).sum()
        tm.assert_frame_equal(result, expected)

    def test_missing_minp_zero(self):
        # https://github.com/pandas-dev/pandas/pull/18921
        # minp=0
        x = pd.Series([np.nan])
        result = x.expanding(min_periods=0).sum()
        expected = pd.Series([0.0])
        tm.assert_series_equal(result, expected)

        # minp=1
        result = x.expanding(min_periods=1).sum()
        expected = pd.Series([np.nan])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize('klass', [pd.Series, pd.DataFrame])
    def test_iter_raises(self, klass):
        # https://github.com/pandas-dev/pandas/issues/11704
        # Iteration over a Window
        obj = klass([1, 2, 3, 4])
        with pytest.raises(NotImplementedError):
            iter(obj.expanding(2))


class TestEWM(Base):

    def setup_method(self, method):
        self._create_data()

    def test_doc_string(self):

        df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
        df
        df.ewm(com=0.5).mean()

    @pytest.mark.parametrize(
        'which', ['series', 'frame'])
    def test_constructor(self, which):
        o = getattr(self, which)
        c = o.ewm

        # valid
        c(com=0.5)
        c(span=1.5)
        c(alpha=0.5)
        c(halflife=0.75)
        c(com=0.5, span=None)
        c(alpha=0.5, com=None)
        c(halflife=0.75, alpha=None)

        # not valid: mutually exclusive
        with pytest.raises(ValueError):
            c(com=0.5, alpha=0.5)
        with pytest.raises(ValueError):
            c(span=1.5, halflife=0.75)
        with pytest.raises(ValueError):
            c(alpha=0.5, span=1.5)

        # not valid: com < 0
        with pytest.raises(ValueError):
            c(com=-0.5)

        # not valid: span < 1
        with pytest.raises(ValueError):
            c(span=0.5)

        # not valid: halflife <= 0
        with pytest.raises(ValueError):
            c(halflife=0)

        # not valid: alpha <= 0 or alpha > 1
        for alpha in (-0.5, 1.5):
            with pytest.raises(ValueError):
                c(alpha=alpha)

    @pytest.mark.parametrize(
        'method', ['std', 'mean', 'var'])
    def test_numpy_compat(self, method):
        # see gh-12811
        e = rwindow.EWM(Series([2, 4, 6]), alpha=0.5)

        msg = "numpy operations are not valid with window objects"

        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(e, method), 1, 2, 3)
        tm.assert_raises_regex(UnsupportedFunctionCall, msg,
                               getattr(e, method), dtype=np.float64)


# gh-12373 : rolling functions error on float32 data
# make sure rolling functions works for different dtypes
#
# NOTE that these are yielded tests and so _create_data
# is explicitly called.
#
# further note that we are only checking rolling for fully dtype
# compliance (though both expanding and ewm inherit)
class Dtype(object):
    window = 2

    funcs = {
        'count': lambda v: v.count(),
        'max': lambda v: v.max(),
        'min': lambda v: v.min(),
        'sum': lambda v: v.sum(),
        'mean': lambda v: v.mean(),
        'std': lambda v: v.std(),
        'var': lambda v: v.var(),
        'median': lambda v: v.median()
    }

    def get_expects(self):
        expects = {
            'sr1': {
                'count': Series([1, 2, 2, 2, 2], dtype='float64'),
                'max': Series([np.nan, 1, 2, 3, 4], dtype='float64'),
                'min': Series([np.nan, 0, 1, 2, 3], dtype='float64'),
                'sum': Series([np.nan, 1, 3, 5, 7], dtype='float64'),
                'mean': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64'),
                'std': Series([np.nan] + [np.sqrt(.5)] * 4, dtype='float64'),
                'var': Series([np.nan, .5, .5, .5, .5], dtype='float64'),
                'median': Series([np.nan, .5, 1.5, 2.5, 3.5], dtype='float64')
            },
            'sr2': {
                'count': Series([1, 2, 2, 2, 2], dtype='float64'),
                'max': Series([np.nan, 10, 8, 6, 4], dtype='float64'),
                'min': Series([np.nan, 8, 6, 4, 2], dtype='float64'),
                'sum': Series([np.nan, 18, 14, 10, 6], dtype='float64'),
                'mean': Series([np.nan, 9, 7, 5, 3], dtype='float64'),
                'std': Series([np.nan] + [np.sqrt(2)] * 4, dtype='float64'),
                'var': Series([np.nan, 2, 2, 2, 2], dtype='float64'),
                'median': Series([np.nan, 9, 7, 5, 3], dtype='float64')
            },
            'df': {
                'count': DataFrame({0: Series([1, 2, 2, 2, 2]),
                                    1: Series([1, 2, 2, 2, 2])},
                                   dtype='float64'),
                'max': DataFrame({0: Series([np.nan, 2, 4, 6, 8]),
                                  1: Series([np.nan, 3, 5, 7, 9])},
                                 dtype='float64'),
                'min': DataFrame({0: Series([np.nan, 0, 2, 4, 6]),
                                  1: Series([np.nan, 1, 3, 5, 7])},
                                 dtype='float64'),
                'sum': DataFrame({0: Series([np.nan, 2, 6, 10, 14]),
                                  1: Series([np.nan, 4, 8, 12, 16])},
                                 dtype='float64'),
                'mean': DataFrame({0: Series([np.nan, 1, 3, 5, 7]),
                                   1: Series([np.nan, 2, 4, 6, 8])},
                                  dtype='float64'),
                'std': DataFrame({0: Series([np.nan] + [np.sqrt(2)] * 4),
                                  1: Series([np.nan] + [np.sqrt(2)] * 4)},
                                 dtype='float64'),
                'var': DataFrame({0: Series([np.nan, 2, 2, 2, 2]),
                                  1: Series([np.nan, 2, 2, 2, 2])},
                                 dtype='float64'),
                'median': DataFrame({0: Series([np.nan, 1, 3, 5, 7]),
                                     1: Series([np.nan, 2, 4, 6, 8])},
                                    dtype='float64'),
            }
        }
        return expects

    def _create_dtype_data(self, dtype):
        sr1 = Series(np.arange(5), dtype=dtype)
        sr2 = Series(np.arange(10, 0, -2), dtype=dtype)
        df = DataFrame(np.arange(10).reshape((5, 2)), dtype=dtype)

        data = {
            'sr1': sr1,
            'sr2': sr2,
            'df': df
        }

        return data

    def _create_data(self):
        self.data = self._create_dtype_data(self.dtype)
        self.expects = self.get_expects()

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
            self.check_dtypes(f, f_name, d, d_name, exp)

    def check_dtypes(self, f, f_name, d, d_name, exp):
        roll = d.rolling(window=self.window)
        result = f(roll)
        tm.assert_almost_equal(result, exp)


class TestDtype_object(Dtype):
    dtype = object


class Dtype_integer(Dtype):
    pass


class TestDtype_int8(Dtype_integer):
    dtype = np.int8


class TestDtype_int16(Dtype_integer):
    dtype = np.int16


class TestDtype_int32(Dtype_integer):
    dtype = np.int32


class TestDtype_int64(Dtype_integer):
    dtype = np.int64


class Dtype_uinteger(Dtype):
    pass


class TestDtype_uint8(Dtype_uinteger):
    dtype = np.uint8


class TestDtype_uint16(Dtype_uinteger):
    dtype = np.uint16


class TestDtype_uint32(Dtype_uinteger):
    dtype = np.uint32


class TestDtype_uint64(Dtype_uinteger):
    dtype = np.uint64


class Dtype_float(Dtype):
    pass


class TestDtype_float16(Dtype_float):
    dtype = np.float16


class TestDtype_float32(Dtype_float):
    dtype = np.float32


class TestDtype_float64(Dtype_float):
    dtype = np.float64


class TestDtype_category(Dtype):
    dtype = 'category'
    include_df = False

    def _create_dtype_data(self, dtype):
        sr1 = Series(range(5), dtype=dtype)
        sr2 = Series(range(10, 0, -2), dtype=dtype)

        data = {
            'sr1': sr1,
            'sr2': sr2
        }

        return data


class DatetimeLike(Dtype):

    def check_dtypes(self, f, f_name, d, d_name, exp):

        roll = d.rolling(window=self.window)

        if f_name == 'count':
            result = f(roll)
            tm.assert_almost_equal(result, exp)

        else:

            # other methods not Implemented ATM
            with pytest.raises(NotImplementedError):
                f(roll)


class TestDtype_timedelta(DatetimeLike):
    dtype = np.dtype('m8[ns]')


class TestDtype_datetime(DatetimeLike):
    dtype = np.dtype('M8[ns]')


class TestDtype_datetime64UTC(DatetimeLike):
    dtype = 'datetime64[ns, UTC]'

    def _create_data(self):
        pytest.skip("direct creation of extension dtype "
                    "datetime64[ns, UTC] is not supported ATM")


@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class TestMoments(Base):

    def setup_method(self, method):
        self._create_data()

    def test_centered_axis_validation(self):

        # ok
        Series(np.ones(10)).rolling(window=3, center=True, axis=0).mean()

        # bad axis
        with pytest.raises(ValueError):
            Series(np.ones(10)).rolling(window=3, center=True, axis=1).mean()

        # ok ok
        DataFrame(np.ones((10, 10))).rolling(window=3, center=True,
                                             axis=0).mean()
        DataFrame(np.ones((10, 10))).rolling(window=3, center=True,
                                             axis=1).mean()

        # bad axis
        with pytest.raises(ValueError):
            (DataFrame(np.ones((10, 10)))
             .rolling(window=3, center=True, axis=2).mean())

    def test_rolling_sum(self):
        self._check_moment_func(np.nansum, name='sum',
                                zero_min_periods_equal=False)

    def test_rolling_count(self):
        counter = lambda x: np.isfinite(x).astype(float).sum()
        self._check_moment_func(counter, name='count', has_min_periods=False,
                                fill_value=0)

    def test_rolling_mean(self):
        self._check_moment_func(np.mean, name='mean')

    @td.skip_if_no_scipy
    def test_cmov_mean(self):
        # GH 8238
        vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
                         10.63, 14.48])
        result = Series(vals).rolling(5, center=True).mean()
        expected = Series([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516,
                           12.818, 12.952, np.nan, np.nan])
        tm.assert_series_equal(expected, result)

    @td.skip_if_no_scipy
    def test_cmov_window(self):
        # GH 8238
        vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
                         10.63, 14.48])
        result = Series(vals).rolling(5, win_type='boxcar', center=True).mean()
        expected = Series([np.nan, np.nan, 9.962, 11.27, 11.564, 12.516,
                           12.818, 12.952, np.nan, np.nan])
        tm.assert_series_equal(expected, result)

    @td.skip_if_no_scipy
    def test_cmov_window_corner(self):
        # GH 8238
        # all nan
        vals = pd.Series([np.nan] * 10)
        result = vals.rolling(5, center=True, win_type='boxcar').mean()
        assert np.isnan(result).all()

        # empty
        vals = pd.Series([])
        result = vals.rolling(5, center=True, win_type='boxcar').mean()
        assert len(result) == 0

        # shorter than window
        vals = pd.Series(np.random.randn(5))
        result = vals.rolling(10, win_type='boxcar').mean()
        assert np.isnan(result).all()
        assert len(result) == 5

    @td.skip_if_no_scipy
    def test_cmov_window_frame(self):
        # Gh 8238
        vals = np.array([[12.18, 3.64], [10.18, 9.16], [13.24, 14.61],
                         [4.51, 8.11], [6.15, 11.44], [9.14, 6.21],
                         [11.31, 10.67], [2.94, 6.51], [9.42, 8.39], [12.44,
                                                                      7.34]])

        xp = np.array([[np.nan, np.nan], [np.nan, np.nan], [9.252, 9.392],
                       [8.644, 9.906], [8.87, 10.208], [6.81, 8.588],
                       [7.792, 8.644], [9.05, 7.824], [np.nan, np.nan
                                                       ], [np.nan, np.nan]])

        # DataFrame
        rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).mean()
        tm.assert_frame_equal(DataFrame(xp), rs)

        # invalid method
        with pytest.raises(AttributeError):
            (DataFrame(vals).rolling(5, win_type='boxcar', center=True)
             .std())

        # sum
        xp = np.array([[np.nan, np.nan], [np.nan, np.nan], [46.26, 46.96],
                       [43.22, 49.53], [44.35, 51.04], [34.05, 42.94],
                       [38.96, 43.22], [45.25, 39.12], [np.nan, np.nan
                                                        ], [np.nan, np.nan]])

        rs = DataFrame(vals).rolling(5, win_type='boxcar', center=True).sum()
        tm.assert_frame_equal(DataFrame(xp), rs)

    @td.skip_if_no_scipy
    def test_cmov_window_na_min_periods(self):
        # min_periods
        vals = Series(np.random.randn(10))
        vals[4] = np.nan
        vals[8] = np.nan

        xp = vals.rolling(5, min_periods=4, center=True).mean()
        rs = vals.rolling(5, win_type='boxcar', min_periods=4,
                          center=True).mean()
        tm.assert_series_equal(xp, rs)

    @td.skip_if_no_scipy
    def test_cmov_window_regular(self, win_types):
        # GH 8238
        vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
                         10.63, 14.48])
        xps = {
            'hamming': [np.nan, np.nan, 8.71384, 9.56348, 12.38009, 14.03687,
                        13.8567, 11.81473, np.nan, np.nan],
            'triang': [np.nan, np.nan, 9.28667, 10.34667, 12.00556, 13.33889,
                       13.38, 12.33667, np.nan, np.nan],
            'barthann': [np.nan, np.nan, 8.4425, 9.1925, 12.5575, 14.3675,
                         14.0825, 11.5675, np.nan, np.nan],
            'bohman': [np.nan, np.nan, 7.61599, 9.1764, 12.83559, 14.17267,
                       14.65923, 11.10401, np.nan, np.nan],
            'blackmanharris': [np.nan, np.nan, 6.97691, 9.16438, 13.05052,
                               14.02156, 15.10512, 10.74574, np.nan, np.nan],
            'nuttall': [np.nan, np.nan, 7.04618, 9.16786, 13.02671, 14.03559,
                        15.05657, 10.78514, np.nan, np.nan],
            'blackman': [np.nan, np.nan, 7.73345, 9.17869, 12.79607, 14.20036,
                         14.57726, 11.16988, np.nan, np.nan],
            'bartlett': [np.nan, np.nan, 8.4425, 9.1925, 12.5575, 14.3675,
                         14.0825, 11.5675, np.nan, np.nan]
        }

        xp = Series(xps[win_types])
        rs = Series(vals).rolling(5, win_type=win_types, center=True).mean()
        tm.assert_series_equal(xp, rs)

    @td.skip_if_no_scipy
    def test_cmov_window_regular_linear_range(self, win_types):
        # GH 8238
        vals = np.array(range(10), dtype=np.float)
        xp = vals.copy()
        xp[:2] = np.nan
        xp[-2:] = np.nan
        xp = Series(xp)

        rs = Series(vals).rolling(5, win_type=win_types, center=True).mean()
        tm.assert_series_equal(xp, rs)

    @td.skip_if_no_scipy
    def test_cmov_window_regular_missing_data(self, win_types):
        # GH 8238
        vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, np.nan,
                         10.63, 14.48])
        xps = {
            'bartlett': [np.nan, np.nan, 9.70333, 10.5225, 8.4425, 9.1925,
                         12.5575, 14.3675, 15.61667, 13.655],
            'blackman': [np.nan, np.nan, 9.04582, 11.41536, 7.73345, 9.17869,
                         12.79607, 14.20036, 15.8706, 13.655],
            'barthann': [np.nan, np.nan, 9.70333, 10.5225, 8.4425, 9.1925,
                         12.5575, 14.3675, 15.61667, 13.655],
            'bohman': [np.nan, np.nan, 8.9444, 11.56327, 7.61599, 9.1764,
                       12.83559, 14.17267, 15.90976, 13.655],
            'hamming': [np.nan, np.nan, 9.59321, 10.29694, 8.71384, 9.56348,
                        12.38009, 14.20565, 15.24694, 13.69758],
            'nuttall': [np.nan, np.nan, 8.47693, 12.2821, 7.04618, 9.16786,
                        13.02671, 14.03673, 16.08759, 13.65553],
            'triang': [np.nan, np.nan, 9.33167, 9.76125, 9.28667, 10.34667,
                       12.00556, 13.82125, 14.49429, 13.765],
            'blackmanharris': [np.nan, np.nan, 8.42526, 12.36824, 6.97691,
                               9.16438, 13.05052, 14.02175, 16.1098, 13.65509]
        }

        xp = Series(xps[win_types])
        rs = Series(vals).rolling(5, win_type=win_types, min_periods=3).mean()
        tm.assert_series_equal(xp, rs)

    @td.skip_if_no_scipy
    def test_cmov_window_special(self, win_types_special):
        # GH 8238
        kwds = {
            'kaiser': {'beta': 1.},
            'gaussian': {'std': 1.},
            'general_gaussian': {'power': 2., 'width': 2.}}

        vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48,
                         10.63, 14.48])

        xps = {
            'gaussian': [np.nan, np.nan, 8.97297, 9.76077, 12.24763, 13.89053,
                         13.65671, 12.01002, np.nan, np.nan],
            'general_gaussian': [np.nan, np.nan, 9.85011, 10.71589, 11.73161,
                                 13.08516, 12.95111, 12.74577, np.nan, np.nan],
            'kaiser': [np.nan, np.nan, 9.86851, 11.02969, 11.65161, 12.75129,
                       12.90702, 12.83757, np.nan, np.nan]
        }

        xp = Series(xps[win_types_special])
        rs = Series(vals).rolling(
            5, win_type=win_types_special, center=True).mean(
            **kwds[win_types_special])
        tm.assert_series_equal(xp, rs)

    @td.skip_if_no_scipy
    def test_cmov_window_special_linear_range(self, win_types_special):
        # GH 8238
        kwds = {
            'kaiser': {'beta': 1.},
            'gaussian': {'std': 1.},
            'general_gaussian': {'power': 2., 'width': 2.},
            'slepian': {'width': 0.5}}

        vals = np.array(range(10), dtype=np.float)
        xp = vals.copy()
        xp[:2] = np.nan
        xp[-2:] = np.nan
        xp = Series(xp)

        rs = Series(vals).rolling(
            5, win_type=win_types_special, center=True).mean(
            **kwds[win_types_special])
        tm.assert_series_equal(xp, rs)

    def test_rolling_median(self):
        self._check_moment_func(np.median, name='median')

    def test_rolling_min(self):
        self._check_moment_func(np.min, name='min')

        a = pd.Series([1, 2, 3, 4, 5])
        result = a.rolling(window=100, min_periods=1).min()
        expected = pd.Series(np.ones(len(a)))
        tm.assert_series_equal(result, expected)

        with pytest.raises(ValueError):
            pd.Series([1, 2, 3]).rolling(window=3, min_periods=5).min()

    def test_rolling_max(self):
        self._check_moment_func(np.max, name='max')

        a = pd.Series([1, 2, 3, 4, 5], dtype=np.float64)
        b = a.rolling(window=100, min_periods=1).max()
        tm.assert_almost_equal(a, b)

        with pytest.raises(ValueError):
            pd.Series([1, 2, 3]).rolling(window=3, min_periods=5).max()

    @pytest.mark.parametrize('q', [0.0, .1, .5, .9, 1.0])
    def test_rolling_quantile(self, q):

        def scoreatpercentile(a, per):
            values = np.sort(a, axis=0)

            idx = int(per / 1. * (values.shape[0] - 1))

            if idx == values.shape[0] - 1:
                retval = values[-1]

            else:
                qlow = float(idx) / float(values.shape[0] - 1)
                qhig = float(idx + 1) / float(values.shape[0] - 1)
                vlow = values[idx]
                vhig = values[idx + 1]
                retval = vlow + (vhig - vlow) * (per - qlow) / (qhig - qlow)

            return retval

        def quantile_func(x):
            return scoreatpercentile(x, q)

        self._check_moment_func(quantile_func, name='quantile',
                                quantile=q)

    def test_rolling_quantile_np_percentile(self):
        # #9413: Tests that rolling window's quantile default behavior
        # is analogus to Numpy's percentile
        row = 10
        col = 5
        idx = pd.date_range('20100101', periods=row, freq='B')
        df = DataFrame(np.random.rand(row * col).reshape((row, -1)), index=idx)

        df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)
        np_percentile = np.percentile(df, [25, 50, 75], axis=0)

        tm.assert_almost_equal(df_quantile.values, np.array(np_percentile))

    @pytest.mark.skipif(_np_version_under1p12,
                        reason='numpy midpoint interpolation is broken')
    @pytest.mark.parametrize('quantile', [0.0, 0.1, 0.45, 0.5, 1])
    @pytest.mark.parametrize('interpolation', ['linear', 'lower', 'higher',
                                               'nearest', 'midpoint'])
    @pytest.mark.parametrize('data', [[1., 2., 3., 4., 5., 6., 7.],
                                      [8., 1., 3., 4., 5., 2., 6., 7.],
                                      [0., np.nan, 0.2, np.nan, 0.4],
                                      [np.nan, np.nan, np.nan, np.nan],
                                      [np.nan, 0.1, np.nan, 0.3, 0.4, 0.5],
                                      [0.5], [np.nan, 0.7, 0.6]])
    def test_rolling_quantile_interpolation_options(self, quantile,
                                                    interpolation, data):
        # Tests that rolling window's quantile behavior is analogous to
        # Series' quantile for each interpolation option
        s = Series(data)

        q1 = s.quantile(quantile, interpolation)
        q2 = s.expanding(min_periods=1).quantile(
            quantile, interpolation).iloc[-1]

        if np.isnan(q1):
            assert np.isnan(q2)
        else:
            assert q1 == q2

    def test_invalid_quantile_value(self):
        data = np.arange(5)
        s = Series(data)

        with pytest.raises(ValueError, match="Interpolation 'invalid'"
                                             " is not supported"):
            s.rolling(len(data), min_periods=1).quantile(
                0.5, interpolation='invalid')

    def test_rolling_quantile_param(self):
        ser = Series([0.0, .1, .5, .9, 1.0])

        with pytest.raises(ValueError):
            ser.rolling(3).quantile(-0.1)

        with pytest.raises(ValueError):
            ser.rolling(3).quantile(10.0)

        with pytest.raises(TypeError):
            ser.rolling(3).quantile('foo')

    def test_rolling_apply(self, raw):
        # suppress warnings about empty slices, as we are deliberately testing
        # with a 0-length Series

        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",
                                    message=".*(empty slice|0 for slice).*",
                                    category=RuntimeWarning)

            def f(x):
                return x[np.isfinite(x)].mean()

            self._check_moment_func(np.mean, name='apply', func=f, raw=raw)

            expected = Series([])
            result = expected.rolling(10).apply(lambda x: x.mean(), raw=raw)
            tm.assert_series_equal(result, expected)

        # gh-8080
        s = Series([None, None, None])
        result = s.rolling(2, min_periods=0).apply(lambda x: len(x), raw=raw)
        expected = Series([1., 2., 2.])
        tm.assert_series_equal(result, expected)

        result = s.rolling(2, min_periods=0).apply(len, raw=raw)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize('klass', [Series, DataFrame])
    @pytest.mark.parametrize(
        'method', [lambda x: x.rolling(window=2), lambda x: x.expanding()])
    def test_apply_future_warning(self, klass, method):

        # gh-5071
        s = klass(np.arange(3))

        with tm.assert_produces_warning(FutureWarning):
            method(s).apply(lambda x: len(x))

    def test_rolling_apply_out_of_bounds(self, raw):
        # gh-1850
        vals = pd.Series([1, 2, 3, 4])

        result = vals.rolling(10).apply(np.sum, raw=raw)
        assert result.isna().all()

        result = vals.rolling(10, min_periods=1).apply(np.sum, raw=raw)
        expected = pd.Series([1, 3, 6, 10], dtype=float)
        tm.assert_almost_equal(result, expected)

    @pytest.mark.parametrize('window', [2, '2s'])
    def test_rolling_apply_with_pandas_objects(self, window):
        # 5071
        df = pd.DataFrame({'A': np.random.randn(5),
                           'B': np.random.randint(0, 10, size=5)},
                          index=pd.date_range('20130101', periods=5, freq='s'))

        # we have an equal spaced timeseries index
        # so simulate removing the first period
        def f(x):
            if x.index[0] == df.index[0]:
                return np.nan
            return x.iloc[-1]

        result = df.rolling(window).apply(f, raw=False)
        expected = df.iloc[2:].reindex_like(df)
        tm.assert_frame_equal(result, expected)

        with pytest.raises(AttributeError):
            df.rolling(window).apply(f, raw=True)

    def test_rolling_std(self):
        self._check_moment_func(lambda x: np.std(x, ddof=1),
                                name='std')
        self._check_moment_func(lambda x: np.std(x, ddof=0),
                                name='std', ddof=0)

    def test_rolling_std_1obs(self):
        vals = pd.Series([1., 2., 3., 4., 5.])

        result = vals.rolling(1, min_periods=1).std()
        expected = pd.Series([np.nan] * 5)
        tm.assert_series_equal(result, expected)

        result = vals.rolling(1, min_periods=1).std(ddof=0)
        expected = pd.Series([0.] * 5)
        tm.assert_series_equal(result, expected)

        result = (pd.Series([np.nan, np.nan, 3, 4, 5])
                    .rolling(3, min_periods=2).std())
        assert np.isnan(result[2])

    def test_rolling_std_neg_sqrt(self):
        # unit test from Bottleneck

        # Test move_nanstd for neg sqrt.

        a = pd.Series([0.0011448196318903589, 0.00028718669878572767,
                       0.00028718669878572767, 0.00028718669878572767,
                       0.00028718669878572767])
        b = a.rolling(window=3).std()
        assert np.isfinite(b[2:]).all()

        b = a.ewm(span=3).std()
        assert np.isfinite(b[2:]).all()

    def test_rolling_var(self):
        self._check_moment_func(lambda x: np.var(x, ddof=1),
                                name='var')
        self._check_moment_func(lambda x: np.var(x, ddof=0),
                                name='var', ddof=0)

    @td.skip_if_no_scipy
    def test_rolling_skew(self):
        from scipy.stats import skew
        self._check_moment_func(lambda x: skew(x, bias=False), name='skew')

    @td.skip_if_no_scipy
    def test_rolling_kurt(self):
        from scipy.stats import kurtosis
        self._check_moment_func(lambda x: kurtosis(x, bias=False),
                                name='kurt')

    def _check_moment_func(self, static_comp, name, has_min_periods=True,
                           has_center=True, has_time_rule=True,
                           fill_value=None, zero_min_periods_equal=True,
                           **kwargs):

        def get_result(obj, window, min_periods=None, center=False):
            r = obj.rolling(window=window, min_periods=min_periods,
                            center=center)
            return getattr(r, name)(**kwargs)

        series_result = get_result(self.series, window=50)
        assert isinstance(series_result, Series)
        tm.assert_almost_equal(series_result.iloc[-1],
                               static_comp(self.series[-50:]))

        frame_result = get_result(self.frame, window=50)
        assert isinstance(frame_result, DataFrame)
        tm.assert_series_equal(
            frame_result.iloc[-1, :],
            self.frame.iloc[-50:, :].apply(static_comp, axis=0, raw=raw),
            check_names=False)

        # check time_rule works
        if has_time_rule:
            win = 25
            minp = 10
            series = self.series[::2].resample('B').mean()
            frame = self.frame[::2].resample('B').mean()

            if has_min_periods:
                series_result = get_result(series, window=win,
                                           min_periods=minp)
                frame_result = get_result(frame, window=win,
                                          min_periods=minp)
            else:
                series_result = get_result(series, window=win)
                frame_result = get_result(frame, window=win)

            last_date = series_result.index[-1]
            prev_date = last_date - 24 * offsets.BDay()

            trunc_series = self.series[::2].truncate(prev_date, last_date)
            trunc_frame = self.frame[::2].truncate(prev_date, last_date)

            tm.assert_almost_equal(series_result[-1],
                                   static_comp(trunc_series))

            tm.assert_series_equal(frame_result.xs(last_date),
                                   trunc_frame.apply(static_comp, raw=raw),
                                   check_names=False)

        # excluding NaNs correctly
        obj = Series(randn(50))
        obj[:10] = np.NaN
        obj[-10:] = np.NaN
        if has_min_periods:
            result = get_result(obj, 50, min_periods=30)
            tm.assert_almost_equal(result.iloc[-1], static_comp(obj[10:-10]))

            # min_periods is working correctly
            result = get_result(obj, 20, min_periods=15)
            assert isna(result.iloc[23])
            assert not isna(result.iloc[24])

            assert not isna(result.iloc[-6])
            assert isna(result.iloc[-5])

            obj2 = Series(randn(20))
            result = get_result(obj2, 10, min_periods=5)
            assert isna(result.iloc[3])
            assert notna(result.iloc[4])

            if zero_min_periods_equal:
                # min_periods=0 may be equivalent to min_periods=1
                result0 = get_result(obj, 20, min_periods=0)
                result1 = get_result(obj, 20, min_periods=1)
                tm.assert_almost_equal(result0, result1)
        else:
            result = get_result(obj, 50)
            tm.assert_almost_equal(result.iloc[-1], static_comp(obj[10:-10]))

        # window larger than series length (#7297)
        if has_min_periods:
            for minp in (0, len(self.series) - 1, len(self.series)):
                result = get_result(self.series, len(self.series) + 1,
                                    min_periods=minp)
                expected = get_result(self.series, len(self.series),
                                      min_periods=minp)
                nan_mask = isna(result)
                tm.assert_series_equal(nan_mask, isna(expected))

                nan_mask = ~nan_mask
                tm.assert_almost_equal(result[nan_mask],
                                       expected[nan_mask])
        else:
            result = get_result(self.series, len(self.series) + 1)
            expected = get_result(self.series, len(self.series))
            nan_mask = isna(result)
            tm.assert_series_equal(nan_mask, isna(expected))

            nan_mask = ~nan_mask
            tm.assert_almost_equal(result[nan_mask], expected[nan_mask])

        # check center=True
        if has_center:
            if has_min_periods:
                result = get_result(obj, 20, min_periods=15, center=True)
                expected = get_result(
                    pd.concat([obj, Series([np.NaN] * 9)]), 20,
                    min_periods=15)[9:].reset_index(drop=True)
            else:
                result = get_result(obj, 20, center=True)
                expected = get_result(
                    pd.concat([obj, Series([np.NaN] * 9)]),
                    20)[9:].reset_index(drop=True)

            tm.assert_series_equal(result, expected)

            # shifter index
            s = ['x%d' % x for x in range(12)]

            if has_min_periods:
                minp = 10

                series_xp = get_result(
                    self.series.reindex(list(self.series.index) + s),
                    window=25,
                    min_periods=minp).shift(-12).reindex(self.series.index)
                frame_xp = get_result(
                    self.frame.reindex(list(self.frame.index) + s),
                    window=25,
                    min_periods=minp).shift(-12).reindex(self.frame.index)

                series_rs = get_result(self.series, window=25,
                                       min_periods=minp, center=True)
                frame_rs = get_result(self.frame, window=25, min_periods=minp,
                                      center=True)

            else:
                series_xp = get_result(
                    self.series.reindex(list(self.series.index) + s),
                    window=25).shift(-12).reindex(self.series.index)
                frame_xp = get_result(
                    self.frame.reindex(list(self.frame.index) + s),
                    window=25).shift(-12).reindex(self.frame.index)

                series_rs = get_result(self.series, window=25, center=True)
                frame_rs = get_result(self.frame, window=25, center=True)

            if fill_value is not None:
                series_xp = series_xp.fillna(fill_value)
                frame_xp = frame_xp.fillna(fill_value)
            tm.assert_series_equal(series_xp, series_rs)
            tm.assert_frame_equal(frame_xp, frame_rs)

    def test_ewma(self):
        self._check_ew(name='mean')

        vals = pd.Series(np.zeros(1000))
        vals[5] = 1
        result = vals.ewm(span=100, adjust=False).mean().sum()
        assert np.abs(result - 1) < 1e-2

    @pytest.mark.parametrize('adjust', [True, False])
    @pytest.mark.parametrize('ignore_na', [True, False])
    def test_ewma_cases(self, adjust, ignore_na):
        # try adjust/ignore_na args matrix

        s = Series([1.0, 2.0, 4.0, 8.0])

        if adjust:
            expected = Series([1.0, 1.6, 2.736842, 4.923077])
        else:
            expected = Series([1.0, 1.333333, 2.222222, 4.148148])

        result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()
        tm.assert_series_equal(result, expected)

    def test_ewma_nan_handling(self):
        s = Series([1.] + [np.nan] * 5 + [1.])
        result = s.ewm(com=5).mean()
        tm.assert_series_equal(result, Series([1.] * len(s)))

        s = Series([np.nan] * 2 + [1.] + [np.nan] * 2 + [1.])
        result = s.ewm(com=5).mean()
        tm.assert_series_equal(result, Series([np.nan] * 2 + [1.] * 4))

        # GH 7603
        s0 = Series([np.nan, 1., 101.])
        s1 = Series([1., np.nan, 101.])
        s2 = Series([np.nan, 1., np.nan, np.nan, 101., np.nan])
        s3 = Series([1., np.nan, 101., 50.])
        com = 2.
        alpha = 1. / (1. + com)

        def simple_wma(s, w):
            return (s.multiply(w).cumsum() / w.cumsum()).fillna(method='ffill')

        for (s, adjust, ignore_na, w) in [
            (s0, True, False, [np.nan, (1. - alpha), 1.]),
            (s0, True, True, [np.nan, (1. - alpha), 1.]),
            (s0, False, False, [np.nan, (1. - alpha), alpha]),
            (s0, False, True, [np.nan, (1. - alpha), alpha]),
            (s1, True, False, [(1. - alpha) ** 2, np.nan, 1.]),
            (s1, True, True, [(1. - alpha), np.nan, 1.]),
            (s1, False, False, [(1. - alpha) ** 2, np.nan, alpha]),
            (s1, False, True, [(1. - alpha), np.nan, alpha]),
            (s2, True, False, [np.nan, (1. - alpha) **
                               3, np.nan, np.nan, 1., np.nan]),
            (s2, True, True, [np.nan, (1. - alpha),
                              np.nan, np.nan, 1., np.nan]),
            (s2, False, False, [np.nan, (1. - alpha) **
                                3, np.nan, np.nan, alpha, np.nan]),
            (s2, False, True, [np.nan, (1. - alpha),
                               np.nan, np.nan, alpha, np.nan]),
            (s3, True, False, [(1. - alpha) **
                               3, np.nan, (1. - alpha), 1.]),
            (s3, True, True, [(1. - alpha) **
                              2, np.nan, (1. - alpha), 1.]),
            (s3, False, False, [(1. - alpha) ** 3, np.nan,
                                (1. - alpha) * alpha,
                                alpha * ((1. - alpha) ** 2 + alpha)]),
            (s3, False, True, [(1. - alpha) ** 2,
                               np.nan, (1. - alpha) * alpha, alpha])]:
            expected = simple_wma(s, Series(w))
            result = s.ewm(com=com, adjust=adjust, ignore_na=ignore_na).mean()

            tm.assert_series_equal(result, expected)
            if ignore_na is False:
                # check that ignore_na defaults to False
                result = s.ewm(com=com, adjust=adjust).mean()
                tm.assert_series_equal(result, expected)

    def test_ewmvar(self):
        self._check_ew(name='var')

    def test_ewmvol(self):
        self._check_ew(name='vol')

    def test_ewma_span_com_args(self):
        A = self.series.ewm(com=9.5).mean()
        B = self.series.ewm(span=20).mean()
        tm.assert_almost_equal(A, B)

        with pytest.raises(ValueError):
            self.series.ewm(com=9.5, span=20)
        with pytest.raises(ValueError):
            self.series.ewm().mean()

    def test_ewma_halflife_arg(self):
        A = self.series.ewm(com=13.932726172912965).mean()
        B = self.series.ewm(halflife=10.0).mean()
        tm.assert_almost_equal(A, B)

        with pytest.raises(ValueError):
            self.series.ewm(span=20, halflife=50)
        with pytest.raises(ValueError):
            self.series.ewm(com=9.5, halflife=50)
        with pytest.raises(ValueError):
            self.series.ewm(com=9.5, span=20, halflife=50)
        with pytest.raises(ValueError):
            self.series.ewm()

    def test_ewm_alpha(self):
        # GH 10789
        s = Series(self.arr)
        a = s.ewm(alpha=0.61722699889169674).mean()
        b = s.ewm(com=0.62014947789973052).mean()
        c = s.ewm(span=2.240298955799461).mean()
        d = s.ewm(halflife=0.721792864318).mean()
        tm.assert_series_equal(a, b)
        tm.assert_series_equal(a, c)
        tm.assert_series_equal(a, d)

    def test_ewm_alpha_arg(self):
        # GH 10789
        s = self.series
        with pytest.raises(ValueError):
            s.ewm()
        with pytest.raises(ValueError):
            s.ewm(com=10.0, alpha=0.5)
        with pytest.raises(ValueError):
            s.ewm(span=10.0, alpha=0.5)
        with pytest.raises(ValueError):
            s.ewm(halflife=10.0, alpha=0.5)

    def test_ewm_domain_checks(self):
        # GH 12492
        s = Series(self.arr)
        # com must satisfy: com >= 0
        pytest.raises(ValueError, s.ewm, com=-0.1)
        s.ewm(com=0.0)
        s.ewm(com=0.1)
        # span must satisfy: span >= 1
        pytest.raises(ValueError, s.ewm, span=-0.1)
        pytest.raises(ValueError, s.ewm, span=0.0)
        pytest.raises(ValueError, s.ewm, span=0.9)
        s.ewm(span=1.0)
        s.ewm(span=1.1)
        # halflife must satisfy: halflife > 0
        pytest.raises(ValueError, s.ewm, halflife=-0.1)
        pytest.raises(ValueError, s.ewm, halflife=0.0)
        s.ewm(halflife=0.1)
        # alpha must satisfy: 0 < alpha <= 1
        pytest.raises(ValueError, s.ewm, alpha=-0.1)
        pytest.raises(ValueError, s.ewm, alpha=0.0)
        s.ewm(alpha=0.1)
        s.ewm(alpha=1.0)
        pytest.raises(ValueError, s.ewm, alpha=1.1)

    @pytest.mark.parametrize('method', ['mean', 'vol', 'var'])
    def test_ew_empty_series(self, method):
        vals = pd.Series([], dtype=np.float64)

        ewm = vals.ewm(3)
        result = getattr(ewm, method)()
        tm.assert_almost_equal(result, vals)

    def _check_ew(self, name=None, preserve_nan=False):
        series_result = getattr(self.series.ewm(com=10), name)()
        assert isinstance(series_result, Series)

        frame_result = getattr(self.frame.ewm(com=10), name)()
        assert type(frame_result) == DataFrame

        result = getattr(self.series.ewm(com=10), name)()
        if preserve_nan:
            assert result[self._nan_locs].isna().all()

        # excluding NaNs correctly
        arr = randn(50)
        arr[:10] = np.NaN
        arr[-10:] = np.NaN
        s = Series(arr)

        # check min_periods
        # GH 7898
        result = getattr(s.ewm(com=50, min_periods=2), name)()
        assert result[:11].isna().all()
        assert not result[11:].isna().any()

        for min_periods in (0, 1):
            result = getattr(s.ewm(com=50, min_periods=min_periods), name)()
            if name == 'mean':
                assert result[:10].isna().all()
                assert not result[10:].isna().any()
            else:
                # ewm.std, ewm.vol, ewm.var (with bias=False) require at least
                # two values
                assert result[:11].isna().all()
                assert not result[11:].isna().any()

            # check series of length 0
            result = getattr(Series().ewm(com=50, min_periods=min_periods),
                             name)()
            tm.assert_series_equal(result, Series())

            # check series of length 1
            result = getattr(Series([1.]).ewm(50, min_periods=min_periods),
                             name)()
            if name == 'mean':
                tm.assert_series_equal(result, Series([1.]))
            else:
                # ewm.std, ewm.vol, ewm.var with bias=False require at least
                # two values
                tm.assert_series_equal(result, Series([np.NaN]))

        # pass in ints
        result2 = getattr(Series(np.arange(50)).ewm(span=10), name)()
        assert result2.dtype == np.float_


class TestPairwise(object):

    # GH 7738
    df1s = [DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0, 1]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 0]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 1]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]],
                      columns=['C', 'C']),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1., 0]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0., 1]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=['C', 1]),
            DataFrame([[2., 4.], [1., 2.], [5., 2.], [8., 1.]],
                      columns=[1, 0.]),
            DataFrame([[2, 4.], [1, 2.], [5, 2.], [8, 1.]],
                      columns=[0, 1.]),
            DataFrame([[2, 4], [1, 2], [5, 2], [8, 1.]],
                      columns=[1., 'X']), ]
    df2 = DataFrame([[None, 1, 1], [None, 1, 2],
                     [None, 3, 2], [None, 8, 1]], columns=['Y', 'Z', 'X'])
    s = Series([1, 1, 3, 8])

    def compare(self, result, expected):

        # since we have sorted the results
        # we can only compare non-nans
        result = result.dropna().values
        expected = expected.dropna().values

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

    @pytest.mark.parametrize('f', [lambda x: x.cov(), lambda x: x.corr()])
    def test_no_flex(self, f):

        # DataFrame methods (which do not call _flex_binary_moment())

        results = [f(df) for df in self.df1s]
        for (df, result) in zip(self.df1s, results):
            tm.assert_index_equal(result.index, df.columns)
            tm.assert_index_equal(result.columns, df.columns)
        for i, result in enumerate(results):
            if i > 0:
                self.compare(result, results[0])

    @pytest.mark.parametrize(
        'f', [lambda x: x.expanding().cov(pairwise=True),
              lambda x: x.expanding().corr(pairwise=True),
              lambda x: x.rolling(window=3).cov(pairwise=True),
              lambda x: x.rolling(window=3).corr(pairwise=True),
              lambda x: x.ewm(com=3).cov(pairwise=True),
              lambda x: x.ewm(com=3).corr(pairwise=True)])
    def test_pairwise_with_self(self, f):

        # DataFrame with itself, pairwise=True
        # note that we may construct the 1st level of the MI
        # in a non-motononic way, so compare accordingly
        results = []
        for i, df in enumerate(self.df1s):
            result = f(df)
            tm.assert_index_equal(result.index.levels[0],
                                  df.index,
                                  check_names=False)
            tm.assert_numpy_array_equal(safe_sort(result.index.levels[1]),
                                        safe_sort(df.columns.unique()))
            tm.assert_index_equal(result.columns, df.columns)
            results.append(df)

        for i, result in enumerate(results):
            if i > 0:
                self.compare(result, results[0])

    @pytest.mark.parametrize(
        'f', [lambda x: x.expanding().cov(pairwise=False),
              lambda x: x.expanding().corr(pairwise=False),
              lambda x: x.rolling(window=3).cov(pairwise=False),
              lambda x: x.rolling(window=3).corr(pairwise=False),
              lambda x: x.ewm(com=3).cov(pairwise=False),
              lambda x: x.ewm(com=3).corr(pairwise=False), ])
    def test_no_pairwise_with_self(self, f):

        # DataFrame with itself, pairwise=False
        results = [f(df) for df in self.df1s]
        for (df, result) in zip(self.df1s, results):
            tm.assert_index_equal(result.index, df.index)
            tm.assert_index_equal(result.columns, df.columns)
        for i, result in enumerate(results):
            if i > 0:
                self.compare(result, results[0])

    @pytest.mark.parametrize(
        'f', [lambda x, y: x.expanding().cov(y, pairwise=True),
              lambda x, y: x.expanding().corr(y, pairwise=True),
              lambda x, y: x.rolling(window=3).cov(y, pairwise=True),
              lambda x, y: x.rolling(window=3).corr(y, pairwise=True),
              lambda x, y: x.ewm(com=3).cov(y, pairwise=True),
              lambda x, y: x.ewm(com=3).corr(y, pairwise=True), ])
    def test_pairwise_with_other(self, f):

        # DataFrame with another DataFrame, pairwise=True
        results = [f(df, self.df2) for df in self.df1s]
        for (df, result) in zip(self.df1s, results):
            tm.assert_index_equal(result.index.levels[0],
                                  df.index,
                                  check_names=False)
            tm.assert_numpy_array_equal(safe_sort(result.index.levels[1]),
                                        safe_sort(self.df2.columns.unique()))
        for i, result in enumerate(results):
            if i > 0:
                self.compare(result, results[0])

    @pytest.mark.parametrize(
        'f', [lambda x, y: x.expanding().cov(y, pairwise=False),
              lambda x, y: x.expanding().corr(y, pairwise=False),
              lambda x, y: x.rolling(window=3).cov(y, pairwise=False),
              lambda x, y: x.rolling(window=3).corr(y, pairwise=False),
              lambda x, y: x.ewm(com=3).cov(y, pairwise=False),
              lambda x, y: x.ewm(com=3).corr(y, pairwise=False), ])
    def test_no_pairwise_with_other(self, f):

        # DataFrame with another DataFrame, pairwise=False
        results = [f(df, self.df2) if df.columns.is_unique else None
                   for df in self.df1s]
        for (df, result) in zip(self.df1s, results):
            if result is not None:
                with catch_warnings(record=True):
                    warnings.simplefilter("ignore", RuntimeWarning)
                    # we can have int and str columns
                    expected_index = df.index.union(self.df2.index)
                    expected_columns = df.columns.union(self.df2.columns)
                tm.assert_index_equal(result.index, expected_index)
                tm.assert_index_equal(result.columns, expected_columns)
            else:
                tm.assert_raises_regex(
                    ValueError, "'arg1' columns are not unique", f, df,
                    self.df2)
                tm.assert_raises_regex(
                    ValueError, "'arg2' columns are not unique", f,
                    self.df2, df)

    @pytest.mark.parametrize(
        'f', [lambda x, y: x.expanding().cov(y),
              lambda x, y: x.expanding().corr(y),
              lambda x, y: x.rolling(window=3).cov(y),
              lambda x, y: x.rolling(window=3).corr(y),
              lambda x, y: x.ewm(com=3).cov(y),
              lambda x, y: x.ewm(com=3).corr(y), ])
    def test_pairwise_with_series(self, f):

        # DataFrame with a Series
        results = ([f(df, self.s) for df in self.df1s] +
                   [f(self.s, df) for df in self.df1s])
        for (df, result) in zip(self.df1s, results):
            tm.assert_index_equal(result.index, df.index)
            tm.assert_index_equal(result.columns, df.columns)
        for i, result in enumerate(results):
            if i > 0:
                self.compare(result, results[0])


# create the data only once as we are not setting it
def _create_consistency_data():
    def create_series():
        return [Series(),
                Series([np.nan]),
                Series([np.nan, np.nan]),
                Series([3.]),
                Series([np.nan, 3.]),
                Series([3., np.nan]),
                Series([1., 3.]),
                Series([2., 2.]),
                Series([3., 1.]),
                Series([5., 5., 5., 5., np.nan, np.nan, np.nan, 5., 5., np.nan,
                        np.nan]),
                Series([np.nan, 5., 5., 5., np.nan, np.nan, np.nan, 5., 5.,
                        np.nan, np.nan]),
                Series([np.nan, np.nan, 5., 5., np.nan, np.nan, np.nan, 5., 5.,
                        np.nan, np.nan]),
                Series([np.nan, 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7.,
                        12., 13., 14., 15.]),
                Series([np.nan, 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3.,
                        12., 13., 14., 15.]),
                Series([2., 3., np.nan, 3., 4., 5., 6., np.nan, np.nan, 7.,
                        12., 13., 14., 15.]),
                Series([2., 5., np.nan, 2., 4., 0., 9., np.nan, np.nan, 3.,
                        12., 13., 14., 15.]),
                Series(range(10)),
                Series(range(20, 0, -2)), ]

    def create_dataframes():
        return ([DataFrame(),
                 DataFrame(columns=['a']),
                 DataFrame(columns=['a', 'a']),
                 DataFrame(columns=['a', 'b']),
                 DataFrame(np.arange(10).reshape((5, 2))),
                 DataFrame(np.arange(25).reshape((5, 5))),
                 DataFrame(np.arange(25).reshape((5, 5)),
                           columns=['a', 'b', 99, 'd', 'd'])] +
                [DataFrame(s) for s in create_series()])

    def is_constant(x):
        values = x.values.ravel()
        return len(set(values[notna(values)])) == 1

    def no_nans(x):
        return x.notna().all().all()

    # data is a tuple(object, is_contant, no_nans)
    data = create_series() + create_dataframes()

    return [(x, is_constant(x), no_nans(x)) for x in data]


_consistency_data = _create_consistency_data()


def _rolling_consistency_cases():
    for window in [1, 2, 3, 10, 20]:
        for min_periods in {0, 1, 2, 3, 4, window}:
            if min_periods and (min_periods > window):
                continue
            for center in [False, True]:
                yield window, min_periods, center


class TestMomentsConsistency(Base):
    base_functions = [
        (lambda v: Series(v).count(), None, 'count'),
        (lambda v: Series(v).max(), None, 'max'),
        (lambda v: Series(v).min(), None, 'min'),
        (lambda v: Series(v).sum(), None, 'sum'),
        (lambda v: Series(v).mean(), None, 'mean'),
        (lambda v: Series(v).std(), 1, 'std'),
        (lambda v: Series(v).cov(Series(v)), None, 'cov'),
        (lambda v: Series(v).corr(Series(v)), None, 'corr'),
        (lambda v: Series(v).var(), 1, 'var'),

        # restore once GH 8086 is fixed
        # lambda v: Series(v).skew(), 3, 'skew'),
        # (lambda v: Series(v).kurt(), 4, 'kurt'),

        # restore once GH 8084 is fixed
        # lambda v: Series(v).quantile(0.3), None, 'quantile'),

        (lambda v: Series(v).median(), None, 'median'),
        (np.nanmax, 1, 'max'),
        (np.nanmin, 1, 'min'),
        (np.nansum, 1, 'sum'),
        (np.nanmean, 1, 'mean'),
        (lambda v: np.nanstd(v, ddof=1), 1, 'std'),
        (lambda v: np.nanvar(v, ddof=1), 1, 'var'),
        (np.nanmedian, 1, 'median'),
    ]
    no_nan_functions = [
        (np.max, None, 'max'),
        (np.min, None, 'min'),
        (np.sum, None, 'sum'),
        (np.mean, None, 'mean'),
        (lambda v: np.std(v, ddof=1), 1, 'std'),
        (lambda v: np.var(v, ddof=1), 1, 'var'),
        (np.median, None, 'median'),
    ]

    def _create_data(self):
        super(TestMomentsConsistency, self)._create_data()
        self.data = _consistency_data

    def setup_method(self, method):
        self._create_data()

    def _test_moments_consistency(self, min_periods, count, mean, mock_mean,
                                  corr, var_unbiased=None, std_unbiased=None,
                                  cov_unbiased=None, var_biased=None,
                                  std_biased=None, cov_biased=None,
                                  var_debiasing_factors=None):
        def _non_null_values(x):
            values = x.values.ravel()
            return set(values[notna(values)].tolist())

        for (x, is_constant, no_nans) in self.data:
            count_x = count(x)
            mean_x = mean(x)

            if mock_mean:
                # check that mean equals mock_mean
                expected = mock_mean(x)
                assert_equal(mean_x, expected.astype('float64'))

            # check that correlation of a series with itself is either 1 or NaN
            corr_x_x = corr(x, x)

            # assert _non_null_values(corr_x_x).issubset(set([1.]))
            # restore once rolling_cov(x, x) is identically equal to var(x)

            if is_constant:
                exp = x.max() if isinstance(x, Series) else x.max().max()

                # check mean of constant series
                expected = x * np.nan
                expected[count_x >= max(min_periods, 1)] = exp
                assert_equal(mean_x, expected)

                # check correlation of constant series with itself is NaN
                expected[:] = np.nan
                assert_equal(corr_x_x, expected)

            if var_unbiased and var_biased and var_debiasing_factors:
                # check variance debiasing factors
                var_unbiased_x = var_unbiased(x)
                var_biased_x = var_biased(x)
                var_debiasing_factors_x = var_debiasing_factors(x)
                assert_equal(var_unbiased_x, var_biased_x *
                             var_debiasing_factors_x)

            for (std, var, cov) in [(std_biased, var_biased, cov_biased),
                                    (std_unbiased, var_unbiased, cov_unbiased)
                                    ]:

                # check that var(x), std(x), and cov(x) are all >= 0
                var_x = var(x)
                std_x = std(x)
                assert not (var_x < 0).any().any()
                assert not (std_x < 0).any().any()
                if cov:
                    cov_x_x = cov(x, x)
                    assert not (cov_x_x < 0).any().any()

                    # check that var(x) == cov(x, x)
                    assert_equal(var_x, cov_x_x)

                # check that var(x) == std(x)^2
                assert_equal(var_x, std_x * std_x)

                if var is var_biased:
                    # check that biased var(x) == mean(x^2) - mean(x)^2
                    mean_x2 = mean(x * x)
                    assert_equal(var_x, mean_x2 - (mean_x * mean_x))

                if is_constant:
                    # check that variance of constant series is identically 0
                    assert not (var_x > 0).any().any()
                    expected = x * np.nan
                    expected[count_x >= max(min_periods, 1)] = 0.
                    if var is var_unbiased:
                        expected[count_x < 2] = np.nan
                    assert_equal(var_x, expected)

                if isinstance(x, Series):
                    for (y, is_constant, no_nans) in self.data:
                        if not x.isna().equals(y.isna()):
                            # can only easily test two Series with similar
                            # structure
                            continue

                        # check that cor(x, y) is symmetric
                        corr_x_y = corr(x, y)
                        corr_y_x = corr(y, x)
                        assert_equal(corr_x_y, corr_y_x)

                        if cov:
                            # check that cov(x, y) is symmetric
                            cov_x_y = cov(x, y)
                            cov_y_x = cov(y, x)
                            assert_equal(cov_x_y, cov_y_x)

                            # check that cov(x, y) == (var(x+y) - var(x) -
                            # var(y)) / 2
                            var_x_plus_y = var(x + y)
                            var_y = var(y)
                            assert_equal(cov_x_y, 0.5 *
                                         (var_x_plus_y - var_x - var_y))

                            # check that corr(x, y) == cov(x, y) / (std(x) *
                            # std(y))
                            std_y = std(y)
                            assert_equal(corr_x_y, cov_x_y / (std_x * std_y))

                            if cov is cov_biased:
                                # check that biased cov(x, y) == mean(x*y) -
                                # mean(x)*mean(y)
                                mean_y = mean(y)
                                mean_x_times_y = mean(x * y)
                                assert_equal(cov_x_y, mean_x_times_y -
                                             (mean_x * mean_y))

    @pytest.mark.slow
    @pytest.mark.parametrize('min_periods', [0, 1, 2, 3, 4])
    @pytest.mark.parametrize('adjust', [True, False])
    @pytest.mark.parametrize('ignore_na', [True, False])
    def test_ewm_consistency(self, min_periods, adjust, ignore_na):
        def _weights(s, com, adjust, ignore_na):
            if isinstance(s, DataFrame):
                if not len(s.columns):
                    return DataFrame(index=s.index, columns=s.columns)
                w = concat([
                    _weights(s.iloc[:, i], com=com, adjust=adjust,
                             ignore_na=ignore_na)
                    for i, _ in enumerate(s.columns)], axis=1)
                w.index = s.index
                w.columns = s.columns
                return w

            w = Series(np.nan, index=s.index)
            alpha = 1. / (1. + com)
            if ignore_na:
                w[s.notna()] = _weights(s[s.notna()], com=com,
                                        adjust=adjust, ignore_na=False)
            elif adjust:
                for i in range(len(s)):
                    if s.iat[i] == s.iat[i]:
                        w.iat[i] = pow(1. / (1. - alpha), i)
            else:
                sum_wts = 0.
                prev_i = -1
                for i in range(len(s)):
                    if s.iat[i] == s.iat[i]:
                        if prev_i == -1:
                            w.iat[i] = 1.
                        else:
                            w.iat[i] = alpha * sum_wts / pow(1. - alpha,
                                                             i - prev_i)
                        sum_wts += w.iat[i]
                        prev_i = i
            return w

        def _variance_debiasing_factors(s, com, adjust, ignore_na):
            weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
            cum_sum = weights.cumsum().fillna(method='ffill')
            cum_sum_sq = (weights * weights).cumsum().fillna(method='ffill')
            numerator = cum_sum * cum_sum
            denominator = numerator - cum_sum_sq
            denominator[denominator <= 0.] = np.nan
            return numerator / denominator

        def _ewma(s, com, min_periods, adjust, ignore_na):
            weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
            result = s.multiply(weights).cumsum().divide(weights.cumsum(
            )).fillna(method='ffill')
            result[s.expanding().count() < (max(min_periods, 1) if min_periods
                                            else 1)] = np.nan
            return result

        com = 3.
        # test consistency between different ewm* moments
        self._test_moments_consistency(
            min_periods=min_periods,
            count=lambda x: x.expanding().count(),
            mean=lambda x: x.ewm(com=com, min_periods=min_periods,
                                 adjust=adjust,
                                 ignore_na=ignore_na).mean(),
            mock_mean=lambda x: _ewma(x, com=com,
                                      min_periods=min_periods,
                                      adjust=adjust,
                                      ignore_na=ignore_na),
            corr=lambda x, y: x.ewm(com=com, min_periods=min_periods,
                                    adjust=adjust,
                                    ignore_na=ignore_na).corr(y),
            var_unbiased=lambda x: (
                x.ewm(com=com, min_periods=min_periods,
                      adjust=adjust,
                      ignore_na=ignore_na).var(bias=False)),
            std_unbiased=lambda x: (
                x.ewm(com=com, min_periods=min_periods,
                      adjust=adjust, ignore_na=ignore_na)
                .std(bias=False)),
            cov_unbiased=lambda x, y: (
                x.ewm(com=com, min_periods=min_periods,
                      adjust=adjust, ignore_na=ignore_na)
                .cov(y, bias=False)),
            var_biased=lambda x: (
                x.ewm(com=com, min_periods=min_periods,
                      adjust=adjust, ignore_na=ignore_na)
                .var(bias=True)),
            std_biased=lambda x: x.ewm(com=com, min_periods=min_periods,
                                       adjust=adjust,
                                       ignore_na=ignore_na).std(bias=True),
            cov_biased=lambda x, y: (
                x.ewm(com=com, min_periods=min_periods,
                      adjust=adjust, ignore_na=ignore_na)
                .cov(y, bias=True)),
            var_debiasing_factors=lambda x: (
                _variance_debiasing_factors(x, com=com, adjust=adjust,
                                            ignore_na=ignore_na)))

    @pytest.mark.slow
    @pytest.mark.parametrize(
        'min_periods', [0, 1, 2, 3, 4])
    def test_expanding_consistency(self, min_periods):

        # suppress warnings about empty slices, as we are deliberately testing
        # with empty/0-length Series/DataFrames
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",
                                    message=".*(empty slice|0 for slice).*",
                                    category=RuntimeWarning)

            # test consistency between different expanding_* moments
            self._test_moments_consistency(
                min_periods=min_periods,
                count=lambda x: x.expanding().count(),
                mean=lambda x: x.expanding(
                    min_periods=min_periods).mean(),
                mock_mean=lambda x: x.expanding(
                    min_periods=min_periods).sum() / x.expanding().count(),
                corr=lambda x, y: x.expanding(
                    min_periods=min_periods).corr(y),
                var_unbiased=lambda x: x.expanding(
                    min_periods=min_periods).var(),
                std_unbiased=lambda x: x.expanding(
                    min_periods=min_periods).std(),
                cov_unbiased=lambda x, y: x.expanding(
                    min_periods=min_periods).cov(y),
                var_biased=lambda x: x.expanding(
                    min_periods=min_periods).var(ddof=0),
                std_biased=lambda x: x.expanding(
                    min_periods=min_periods).std(ddof=0),
                cov_biased=lambda x, y: x.expanding(
                    min_periods=min_periods).cov(y, ddof=0),
                var_debiasing_factors=lambda x: (
                    x.expanding().count() /
                    (x.expanding().count() - 1.)
                    .replace(0., np.nan)))

            # test consistency between expanding_xyz() and either (a)
            # expanding_apply of Series.xyz(), or (b) expanding_apply of
            # np.nanxyz()
            for (x, is_constant, no_nans) in self.data:
                functions = self.base_functions

                # GH 8269
                if no_nans:
                    functions = self.base_functions + self.no_nan_functions
                for (f, require_min_periods, name) in functions:
                    expanding_f = getattr(
                        x.expanding(min_periods=min_periods), name)

                    if (require_min_periods and
                            (min_periods is not None) and
                            (min_periods < require_min_periods)):
                        continue

                    if name == 'count':
                        expanding_f_result = expanding_f()
                        expanding_apply_f_result = x.expanding(
                            min_periods=0).apply(func=f, raw=True)
                    else:
                        if name in ['cov', 'corr']:
                            expanding_f_result = expanding_f(
                                pairwise=False)
                        else:
                            expanding_f_result = expanding_f()
                        expanding_apply_f_result = x.expanding(
                            min_periods=min_periods).apply(func=f, raw=True)

                    # GH 9422
                    if name in ['sum', 'prod']:
                        assert_equal(expanding_f_result,
                                     expanding_apply_f_result)

    @pytest.mark.slow
    @pytest.mark.parametrize(
        'window,min_periods,center', list(_rolling_consistency_cases()))
    def test_rolling_consistency(self, window, min_periods, center):

        # suppress warnings about empty slices, as we are deliberately testing
        # with empty/0-length Series/DataFrames
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore",
                                    message=".*(empty slice|0 for slice).*",
                                    category=RuntimeWarning)

            # test consistency between different rolling_* moments
            self._test_moments_consistency(
                min_periods=min_periods,
                count=lambda x: (
                    x.rolling(window=window, center=center)
                    .count()),
                mean=lambda x: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).mean()),
                mock_mean=lambda x: (
                    x.rolling(window=window,
                              min_periods=min_periods,
                              center=center).sum()
                    .divide(x.rolling(window=window,
                                      min_periods=min_periods,
                                      center=center).count())),
                corr=lambda x, y: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).corr(y)),

                var_unbiased=lambda x: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).var()),

                std_unbiased=lambda x: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).std()),

                cov_unbiased=lambda x, y: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).cov(y)),

                var_biased=lambda x: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).var(ddof=0)),

                std_biased=lambda x: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).std(ddof=0)),

                cov_biased=lambda x, y: (
                    x.rolling(window=window, min_periods=min_periods,
                              center=center).cov(y, ddof=0)),
                var_debiasing_factors=lambda x: (
                    x.rolling(window=window, center=center).count()
                    .divide((x.rolling(window=window, center=center)
                             .count() - 1.)
                            .replace(0., np.nan))))

            # test consistency between rolling_xyz() and either (a)
            # rolling_apply of Series.xyz(), or (b) rolling_apply of
            # np.nanxyz()
            for (x, is_constant, no_nans) in self.data:
                functions = self.base_functions

                # GH 8269
                if no_nans:
                    functions = self.base_functions + self.no_nan_functions
                for (f, require_min_periods, name) in functions:
                    rolling_f = getattr(
                        x.rolling(window=window, center=center,
                                  min_periods=min_periods), name)

                    if require_min_periods and (
                            min_periods is not None) and (
                                min_periods < require_min_periods):
                        continue

                    if name == 'count':
                        rolling_f_result = rolling_f()
                        rolling_apply_f_result = x.rolling(
                            window=window, min_periods=0,
                            center=center).apply(func=f, raw=True)
                    else:
                        if name in ['cov', 'corr']:
                            rolling_f_result = rolling_f(
                                pairwise=False)
                        else:
                            rolling_f_result = rolling_f()
                        rolling_apply_f_result = x.rolling(
                            window=window, min_periods=min_periods,
                            center=center).apply(func=f, raw=True)

                    # GH 9422
                    if name in ['sum', 'prod']:
                        assert_equal(rolling_f_result,
                                     rolling_apply_f_result)

    # binary moments
    def test_rolling_cov(self):
        A = self.series
        B = A + randn(len(A))

        result = A.rolling(window=50, min_periods=25).cov(B)
        tm.assert_almost_equal(result[-1], np.cov(A[-50:], B[-50:])[0, 1])

    def test_rolling_cov_pairwise(self):
        self._check_pairwise_moment('rolling', 'cov', window=10, min_periods=5)

    def test_rolling_corr(self):
        A = self.series
        B = A + randn(len(A))

        result = A.rolling(window=50, min_periods=25).corr(B)
        tm.assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])

        # test for correct bias correction
        a = tm.makeTimeSeries()
        b = tm.makeTimeSeries()
        a[:5] = np.nan
        b[:10] = np.nan

        result = a.rolling(window=len(a), min_periods=1).corr(b)
        tm.assert_almost_equal(result[-1], a.corr(b))

    def test_rolling_corr_pairwise(self):
        self._check_pairwise_moment('rolling', 'corr', window=10,
                                    min_periods=5)

    @pytest.mark.parametrize('window', range(7))
    def test_rolling_corr_with_zero_variance(self, window):
        # GH 18430
        s = pd.Series(np.zeros(20))
        other = pd.Series(np.arange(20))

        assert s.rolling(window=window).corr(other=other).isna().all()

    def _check_pairwise_moment(self, dispatch, name, **kwargs):
        def get_result(obj, obj2=None):
            return getattr(getattr(obj, dispatch)(**kwargs), name)(obj2)

        result = get_result(self.frame)
        result = result.loc[(slice(None), 1), 5]
        result.index = result.index.droplevel(1)
        expected = get_result(self.frame[1], self.frame[5])
        tm.assert_series_equal(result, expected, check_names=False)

    def test_flex_binary_moment(self):
        # GH3155
        # don't blow the stack
        pytest.raises(TypeError, rwindow._flex_binary_moment, 5, 6, None)

    def test_corr_sanity(self):
        # GH 3155
        df = DataFrame(np.array(
            [[0.87024726, 0.18505595], [0.64355431, 0.3091617],
             [0.92372966, 0.50552513], [0.00203756, 0.04520709],
             [0.84780328, 0.33394331], [0.78369152, 0.63919667]]))

        res = df[0].rolling(5, center=True).corr(df[1])
        assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)

        # and some fuzzing
        for _ in range(10):
            df = DataFrame(np.random.rand(30, 2))
            res = df[0].rolling(5, center=True).corr(df[1])
            try:
                assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)
            except AssertionError:
                print(res)

    @pytest.mark.parametrize('method', ['corr', 'cov'])
    def test_flex_binary_frame(self, method):
        series = self.frame[1]

        res = getattr(series.rolling(window=10), method)(self.frame)
        res2 = getattr(self.frame.rolling(window=10), method)(series)
        exp = self.frame.apply(lambda x: getattr(
            series.rolling(window=10), method)(x))

        tm.assert_frame_equal(res, exp)
        tm.assert_frame_equal(res2, exp)

        frame2 = self.frame.copy()
        frame2.values[:] = np.random.randn(*frame2.shape)

        res3 = getattr(self.frame.rolling(window=10), method)(frame2)
        exp = DataFrame({k: getattr(self.frame[k].rolling(
            window=10), method)(frame2[k]) for k in self.frame})
        tm.assert_frame_equal(res3, exp)

    def test_ewmcov(self):
        self._check_binary_ew('cov')

    def test_ewmcov_pairwise(self):
        self._check_pairwise_moment('ewm', 'cov', span=10, min_periods=5)

    def test_ewmcorr(self):
        self._check_binary_ew('corr')

    def test_ewmcorr_pairwise(self):
        self._check_pairwise_moment('ewm', 'corr', span=10, min_periods=5)

    def _check_binary_ew(self, name):
        def func(A, B, com, **kwargs):
            return getattr(A.ewm(com, **kwargs), name)(B)

        A = Series(randn(50), index=np.arange(50))
        B = A[2:] + randn(48)

        A[:10] = np.NaN
        B[-10:] = np.NaN

        result = func(A, B, 20, min_periods=5)
        assert np.isnan(result.values[:14]).all()
        assert not np.isnan(result.values[14:]).any()

        # GH 7898
        for min_periods in (0, 1, 2):
            result = func(A, B, 20, min_periods=min_periods)
            # binary functions (ewmcov, ewmcorr) with bias=False require at
            # least two values
            assert np.isnan(result.values[:11]).all()
            assert not np.isnan(result.values[11:]).any()

            # check series of length 0
            result = func(Series([]), Series([]), 50, min_periods=min_periods)
            tm.assert_series_equal(result, Series([]))

            # check series of length 1
            result = func(
                Series([1.]), Series([1.]), 50, min_periods=min_periods)
            tm.assert_series_equal(result, Series([np.NaN]))

        pytest.raises(Exception, func, A, randn(50), 20, min_periods=5)

    def test_expanding_apply_args_kwargs(self, raw):

        def mean_w_arg(x, const):
            return np.mean(x) + const

        df = DataFrame(np.random.rand(20, 3))

        expected = df.expanding().apply(np.mean, raw=raw) + 20.

        result = df.expanding().apply(mean_w_arg,
                                      raw=raw,
                                      args=(20, ))
        tm.assert_frame_equal(result, expected)

        result = df.expanding().apply(mean_w_arg,
                                      raw=raw,
                                      kwargs={'const': 20})
        tm.assert_frame_equal(result, expected)

    def test_expanding_corr(self):
        A = self.series.dropna()
        B = (A + randn(len(A)))[:-5]

        result = A.expanding().corr(B)

        rolling_result = A.rolling(window=len(A), min_periods=1).corr(B)

        tm.assert_almost_equal(rolling_result, result)

    def test_expanding_count(self):
        result = self.series.expanding().count()
        tm.assert_almost_equal(result, self.series.rolling(
            window=len(self.series)).count())

    def test_expanding_quantile(self):
        result = self.series.expanding().quantile(0.5)

        rolling_result = self.series.rolling(window=len(self.series),
                                             min_periods=1).quantile(0.5)

        tm.assert_almost_equal(result, rolling_result)

    def test_expanding_cov(self):
        A = self.series
        B = (A + randn(len(A)))[:-5]

        result = A.expanding().cov(B)

        rolling_result = A.rolling(window=len(A), min_periods=1).cov(B)

        tm.assert_almost_equal(rolling_result, result)

    def test_expanding_cov_pairwise(self):
        result = self.frame.expanding().corr()

        rolling_result = self.frame.rolling(window=len(self.frame),
                                            min_periods=1).corr()

        tm.assert_frame_equal(result, rolling_result)

    def test_expanding_corr_pairwise(self):
        result = self.frame.expanding().corr()

        rolling_result = self.frame.rolling(window=len(self.frame),
                                            min_periods=1).corr()
        tm.assert_frame_equal(result, rolling_result)

    def test_expanding_cov_diff_index(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.expanding().cov(s2)
        expected = Series([None, None, 2.0])
        tm.assert_series_equal(result, expected)

        s2a = Series([1, None, 3], index=[0, 1, 2])
        result = s1.expanding().cov(s2a)
        tm.assert_series_equal(result, expected)

        s1 = Series([7, 8, 10], index=[0, 1, 3])
        s2 = Series([7, 9, 10], index=[0, 2, 3])
        result = s1.expanding().cov(s2)
        expected = Series([None, None, None, 4.5])
        tm.assert_series_equal(result, expected)

    def test_expanding_corr_diff_index(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.expanding().corr(s2)
        expected = Series([None, None, 1.0])
        tm.assert_series_equal(result, expected)

        s2a = Series([1, None, 3], index=[0, 1, 2])
        result = s1.expanding().corr(s2a)
        tm.assert_series_equal(result, expected)

        s1 = Series([7, 8, 10], index=[0, 1, 3])
        s2 = Series([7, 9, 10], index=[0, 2, 3])
        result = s1.expanding().corr(s2)
        expected = Series([None, None, None, 1.])
        tm.assert_series_equal(result, expected)

    def test_rolling_cov_diff_length(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.rolling(window=3, min_periods=2).cov(s2)
        expected = Series([None, None, 2.0])
        tm.assert_series_equal(result, expected)

        s2a = Series([1, None, 3], index=[0, 1, 2])
        result = s1.rolling(window=3, min_periods=2).cov(s2a)
        tm.assert_series_equal(result, expected)

    def test_rolling_corr_diff_length(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.rolling(window=3, min_periods=2).corr(s2)
        expected = Series([None, None, 1.0])
        tm.assert_series_equal(result, expected)

        s2a = Series([1, None, 3], index=[0, 1, 2])
        result = s1.rolling(window=3, min_periods=2).corr(s2a)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        'f',
        [
            lambda x: (x.rolling(window=10, min_periods=5)
                       .cov(x, pairwise=False)),
            lambda x: (x.rolling(window=10, min_periods=5)
                       .corr(x, pairwise=False)),
            lambda x: x.rolling(window=10, min_periods=5).max(),
            lambda x: x.rolling(window=10, min_periods=5).min(),
            lambda x: x.rolling(window=10, min_periods=5).sum(),
            lambda x: x.rolling(window=10, min_periods=5).mean(),
            lambda x: x.rolling(window=10, min_periods=5).std(),
            lambda x: x.rolling(window=10, min_periods=5).var(),
            lambda x: x.rolling(window=10, min_periods=5).skew(),
            lambda x: x.rolling(window=10, min_periods=5).kurt(),
            lambda x: x.rolling(
                window=10, min_periods=5).quantile(quantile=0.5),
            lambda x: x.rolling(window=10, min_periods=5).median(),
            lambda x: x.rolling(window=10, min_periods=5).apply(
                sum, raw=False),
            lambda x: x.rolling(window=10, min_periods=5).apply(
                sum, raw=True),
            lambda x: x.rolling(win_type='boxcar',
                                window=10, min_periods=5).mean()])
    def test_rolling_functions_window_non_shrinkage(self, f):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)

        try:
            s_result = f(s)
            tm.assert_series_equal(s_result, s_expected)

            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected)
        except (ImportError):

            # scipy needed for rolling_window
            pytest.skip("scipy not available")

    def test_rolling_functions_window_non_shrinkage_binary(self):

        # corr/cov return a MI DataFrame
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]],
                       columns=Index(['A', 'B'], name='foo'),
                       index=Index(range(4), name='bar'))
        df_expected = DataFrame(
            columns=Index(['A', 'B'], name='foo'),
            index=pd.MultiIndex.from_product([df.index, df.columns],
                                             names=['bar', 'foo']),
            dtype='float64')
        functions = [lambda x: (x.rolling(window=10, min_periods=5)
                                .cov(x, pairwise=True)),
                     lambda x: (x.rolling(window=10, min_periods=5)
                                .corr(x, pairwise=True))]
        for f in functions:
            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected)

    def test_moment_functions_zero_length(self):
        # GH 8056
        s = Series()
        s_expected = s
        df1 = DataFrame()
        df1_expected = df1
        df2 = DataFrame(columns=['a'])
        df2['a'] = df2['a'].astype('float64')
        df2_expected = df2

        functions = [lambda x: x.expanding().count(),
                     lambda x: x.expanding(min_periods=5).cov(
                         x, pairwise=False),
                     lambda x: x.expanding(min_periods=5).corr(
                         x, pairwise=False),
                     lambda x: x.expanding(min_periods=5).max(),
                     lambda x: x.expanding(min_periods=5).min(),
                     lambda x: x.expanding(min_periods=5).sum(),
                     lambda x: x.expanding(min_periods=5).mean(),
                     lambda x: x.expanding(min_periods=5).std(),
                     lambda x: x.expanding(min_periods=5).var(),
                     lambda x: x.expanding(min_periods=5).skew(),
                     lambda x: x.expanding(min_periods=5).kurt(),
                     lambda x: x.expanding(min_periods=5).quantile(0.5),
                     lambda x: x.expanding(min_periods=5).median(),
                     lambda x: x.expanding(min_periods=5).apply(
                         sum, raw=False),
                     lambda x: x.expanding(min_periods=5).apply(
                         sum, raw=True),
                     lambda x: x.rolling(window=10).count(),
                     lambda x: x.rolling(window=10, min_periods=5).cov(
                         x, pairwise=False),
                     lambda x: x.rolling(window=10, min_periods=5).corr(
                         x, pairwise=False),
                     lambda x: x.rolling(window=10, min_periods=5).max(),
                     lambda x: x.rolling(window=10, min_periods=5).min(),
                     lambda x: x.rolling(window=10, min_periods=5).sum(),
                     lambda x: x.rolling(window=10, min_periods=5).mean(),
                     lambda x: x.rolling(window=10, min_periods=5).std(),
                     lambda x: x.rolling(window=10, min_periods=5).var(),
                     lambda x: x.rolling(window=10, min_periods=5).skew(),
                     lambda x: x.rolling(window=10, min_periods=5).kurt(),
                     lambda x: x.rolling(
                         window=10, min_periods=5).quantile(0.5),
                     lambda x: x.rolling(window=10, min_periods=5).median(),
                     lambda x: x.rolling(window=10, min_periods=5).apply(
                         sum, raw=False),
                     lambda x: x.rolling(window=10, min_periods=5).apply(
                         sum, raw=True),
                     lambda x: x.rolling(win_type='boxcar',
                                         window=10, min_periods=5).mean(),
                     ]
        for f in functions:
            try:
                s_result = f(s)
                tm.assert_series_equal(s_result, s_expected)

                df1_result = f(df1)
                tm.assert_frame_equal(df1_result, df1_expected)

                df2_result = f(df2)
                tm.assert_frame_equal(df2_result, df2_expected)
            except (ImportError):

                # scipy needed for rolling_window
                continue

    def test_moment_functions_zero_length_pairwise(self):

        df1 = DataFrame()
        df1_expected = df1
        df2 = DataFrame(columns=Index(['a'], name='foo'),
                        index=Index([], name='bar'))
        df2['a'] = df2['a'].astype('float64')

        df1_expected = DataFrame(
            index=pd.MultiIndex.from_product([df1.index, df1.columns]),
            columns=Index([]))
        df2_expected = DataFrame(
            index=pd.MultiIndex.from_product([df2.index, df2.columns],
                                             names=['bar', 'foo']),
            columns=Index(['a'], name='foo'),
            dtype='float64')

        functions = [lambda x: (x.expanding(min_periods=5)
                                .cov(x, pairwise=True)),
                     lambda x: (x.expanding(min_periods=5)
                                .corr(x, pairwise=True)),
                     lambda x: (x.rolling(window=10, min_periods=5)
                                .cov(x, pairwise=True)),
                     lambda x: (x.rolling(window=10, min_periods=5)
                                .corr(x, pairwise=True)),
                     ]
        for f in functions:
            df1_result = f(df1)
            tm.assert_frame_equal(df1_result, df1_expected)

            df2_result = f(df2)
            tm.assert_frame_equal(df2_result, df2_expected)

    def test_expanding_cov_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 5], [3, 2], [3, 9]],
                        columns=Index(['A', 'B'], name='foo'))
        df1a = DataFrame([[1, 5], [3, 9]],
                         index=[0, 2],
                         columns=Index(['A', 'B'], name='foo'))
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=Index(['X', 'Y'], name='foo'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=[0, 2],
                         columns=Index(['X', 'Y'], name='foo'))
        # TODO: xref gh-15826
        # .loc is not preserving the names
        result1 = df1.expanding().cov(df2a, pairwise=True).loc[2]
        result2 = df1.expanding().cov(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-3.0, -6.0], [-5.0, -10.0]],
                             columns=Index(['A', 'B'], name='foo'),
                             index=Index(['X', 'Y'], name='foo'))
        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected)
        tm.assert_frame_equal(result4, expected)

    def test_expanding_corr_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 2], [3, 2], [3, 4]],
                        columns=['A', 'B'],
                        index=Index(range(3), name='bar'))
        df1a = DataFrame([[1, 2], [3, 4]],
                         index=Index([0, 2], name='bar'),
                         columns=['A', 'B'])
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=['X', 'Y'],
                        index=Index(range(3), name='bar'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=Index([0, 2], name='bar'),
                         columns=['X', 'Y'])
        result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
        result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]],
                             columns=['A', 'B'],
                             index=Index(['X', 'Y']))
        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected)
        tm.assert_frame_equal(result4, expected)

    def test_rolling_skew_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = d.rolling(window=5).skew()
        tm.assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = d.rolling(window=2).skew()
        tm.assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 0.177994, 1.548824]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
                    ])
        expected = Series([np.NaN, np.NaN, np.NaN, 0.177994, 1.548824])
        x = d.rolling(window=4).skew()
        tm.assert_series_equal(expected, x)

    def test_rolling_kurt_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = d.rolling(window=5).kurt()
        tm.assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = d.rolling(window=3).kurt()
        tm.assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 1.224307, 2.671499]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
                    ])
        expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
        x = d.rolling(window=4).kurt()
        tm.assert_series_equal(expected, x)

    def test_rolling_skew_eq_value_fperr(self):
        # #18804 all rolling skew for all equal values should return Nan
        a = Series([1.1] * 15).rolling(window=10).skew()
        assert np.isnan(a).all()

    def test_rolling_kurt_eq_value_fperr(self):
        # #18804 all rolling kurt for all equal values should return Nan
        a = Series([1.1] * 15).rolling(window=10).kurt()
        assert np.isnan(a).all()

    @pytest.mark.parametrize('func,static_comp', [('sum', np.sum),
                                                  ('mean', np.mean),
                                                  ('max', np.max),
                                                  ('min', np.min)],
                             ids=['sum', 'mean', 'max', 'min'])
    def test_expanding_func(self, func, static_comp):
        def expanding_func(x, min_periods=1, center=False, axis=0):
            exp = x.expanding(min_periods=min_periods,
                              center=center, axis=axis)
            return getattr(exp, func)()
        self._check_expanding(expanding_func, static_comp, preserve_nan=False)

    def test_expanding_apply(self, raw):

        def expanding_mean(x, min_periods=1):

            exp = x.expanding(min_periods=min_periods)
            result = exp.apply(lambda x: x.mean(), raw=raw)
            return result

        # TODO(jreback), needed to add preserve_nan=False
        # here to make this pass
        self._check_expanding(expanding_mean, np.mean, preserve_nan=False)

        ser = Series([])
        tm.assert_series_equal(ser, ser.expanding().apply(
            lambda x: x.mean(), raw=raw))

        # GH 8080
        s = Series([None, None, None])
        result = s.expanding(min_periods=0).apply(lambda x: len(x), raw=raw)
        expected = Series([1., 2., 3.])
        tm.assert_series_equal(result, expected)

    def _check_expanding(self, func, static_comp, has_min_periods=True,
                         has_time_rule=True, preserve_nan=True):

        series_result = func(self.series)
        assert isinstance(series_result, Series)
        frame_result = func(self.frame)
        assert isinstance(frame_result, DataFrame)

        result = func(self.series)
        tm.assert_almost_equal(result[10], static_comp(self.series[:11]))

        if preserve_nan:
            assert result.iloc[self._nan_locs].isna().all()

        ser = Series(randn(50))

        if has_min_periods:
            result = func(ser, min_periods=30)
            assert result[:29].isna().all()
            tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))

            # min_periods is working correctly
            result = func(ser, min_periods=15)
            assert isna(result.iloc[13])
            assert notna(result.iloc[14])

            ser2 = Series(randn(20))
            result = func(ser2, min_periods=5)
            assert isna(result[3])
            assert notna(result[4])

            # min_periods=0
            result0 = func(ser, min_periods=0)
            result1 = func(ser, min_periods=1)
            tm.assert_almost_equal(result0, result1)
        else:
            result = func(ser)
            tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))

    def test_rolling_max_gh6297(self):
        """Replicate result expected in GH #6297"""

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 2 datapoints on one of the days
        indices.append(datetime(1975, 1, 3, 6, 0))
        series = Series(range(1, 7), index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        expected = Series([1.0, 2.0, 6.0, 4.0, 5.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').max().rolling(window=1).max()
        tm.assert_series_equal(expected, x)

    def test_rolling_max_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be max
        expected = Series([0.0, 1.0, 2.0, 3.0, 20.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').max().rolling(window=1).max()
        tm.assert_series_equal(expected, x)

        # Now specify median (10.0)
        expected = Series([0.0, 1.0, 2.0, 3.0, 10.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').median().rolling(window=1).max()
        tm.assert_series_equal(expected, x)

        # Now specify mean (4+10+20)/3
        v = (4.0 + 10.0 + 20.0) / 3.0
        expected = Series([0.0, 1.0, 2.0, 3.0, v],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').mean().rolling(window=1).max()
        tm.assert_series_equal(expected, x)

    def test_rolling_min_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be min
        expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        r = series.resample('D').min().rolling(window=1)
        tm.assert_series_equal(expected, r.min())

    def test_rolling_median_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be median
        expected = Series([0.0, 1.0, 2.0, 3.0, 10],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').median().rolling(window=1).median()
        tm.assert_series_equal(expected, x)

    def test_rolling_median_memory_error(self):
        # GH11722
        n = 20000
        Series(np.random.randn(n)).rolling(window=2, center=False).median()
        Series(np.random.randn(n)).rolling(window=2, center=False).median()

    def test_rolling_min_max_numeric_types(self):

        # GH12373
        types_test = [np.dtype("f{}".format(width)) for width in [4, 8]]
        types_test.extend([np.dtype("{}{}".format(sign, width))
                           for width in [1, 2, 4, 8] for sign in "ui"])
        for data_type in types_test:
            # Just testing that these don't throw exceptions and that
            # the return type is float64. Other tests will cover quantitative
            # correctness
            result = (DataFrame(np.arange(20, dtype=data_type))
                      .rolling(window=5).max())
            assert result.dtypes[0] == np.dtype("f8")
            result = (DataFrame(np.arange(20, dtype=data_type))
                      .rolling(window=5).min())
            assert result.dtypes[0] == np.dtype("f8")


class TestGrouperGrouping(object):

    def setup_method(self, method):
        self.series = Series(np.arange(10))
        self.frame = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                                'B': np.arange(40)})

    def test_mutated(self):

        def f():
            self.frame.groupby('A', foo=1)
        pytest.raises(TypeError, f)

        g = self.frame.groupby('A')
        assert not g.mutated
        g = self.frame.groupby('A', mutated=True)
        assert g.mutated

    def test_getitem(self):
        g = self.frame.groupby('A')
        g_mutated = self.frame.groupby('A', mutated=True)

        expected = g_mutated.B.apply(lambda x: x.rolling(2).mean())

        result = g.rolling(2).mean().B
        tm.assert_series_equal(result, expected)

        result = g.rolling(2).B.mean()
        tm.assert_series_equal(result, expected)

        result = g.B.rolling(2).mean()
        tm.assert_series_equal(result, expected)

        result = self.frame.B.groupby(self.frame.A).rolling(2).mean()
        tm.assert_series_equal(result, expected)

    def test_getitem_multiple(self):

        # GH 13174
        g = self.frame.groupby('A')
        r = g.rolling(2)
        g_mutated = self.frame.groupby('A', mutated=True)
        expected = g_mutated.B.apply(lambda x: x.rolling(2).count())

        result = r.B.count()
        tm.assert_series_equal(result, expected)

        result = r.B.count()
        tm.assert_series_equal(result, expected)

    def test_rolling(self):
        g = self.frame.groupby('A')
        r = g.rolling(window=4)

        for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']:

            result = getattr(r, f)()
            expected = g.apply(lambda x: getattr(x.rolling(4), f)())
            tm.assert_frame_equal(result, expected)

        for f in ['std', 'var']:
            result = getattr(r, f)(ddof=1)
            expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
            tm.assert_frame_equal(result, expected)

        result = r.quantile(0.5)
        expected = g.apply(lambda x: x.rolling(4).quantile(0.5))
        tm.assert_frame_equal(result, expected)

    def test_rolling_corr_cov(self):
        g = self.frame.groupby('A')
        r = g.rolling(window=4)

        for f in ['corr', 'cov']:
            result = getattr(r, f)(self.frame)

            def func(x):
                return getattr(x.rolling(4), f)(self.frame)
            expected = g.apply(func)
            tm.assert_frame_equal(result, expected)

            result = getattr(r.B, f)(pairwise=True)

            def func(x):
                return getattr(x.B.rolling(4), f)(pairwise=True)
            expected = g.apply(func)
            tm.assert_series_equal(result, expected)

    def test_rolling_apply(self, raw):
        g = self.frame.groupby('A')
        r = g.rolling(window=4)

        # reduction
        result = r.apply(lambda x: x.sum(), raw=raw)
        expected = g.apply(
            lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw))
        tm.assert_frame_equal(result, expected)

    def test_rolling_apply_mutability(self):
        # GH 14013
        df = pd.DataFrame({'A': ['foo'] * 3 + ['bar'] * 3, 'B': [1] * 6})
        g = df.groupby('A')

        mi = pd.MultiIndex.from_tuples([('bar', 3), ('bar', 4), ('bar', 5),
                                        ('foo', 0), ('foo', 1), ('foo', 2)])

        mi.names = ['A', None]
        # Grouped column should not be a part of the output
        expected = pd.DataFrame([np.nan, 2., 2.] * 2, columns=['B'], index=mi)

        result = g.rolling(window=2).sum()
        tm.assert_frame_equal(result, expected)

        # Call an arbitrary function on the groupby
        g.sum()

        # Make sure nothing has been mutated
        result = g.rolling(window=2).sum()
        tm.assert_frame_equal(result, expected)

    def test_expanding(self):
        g = self.frame.groupby('A')
        r = g.expanding()

        for f in ['sum', 'mean', 'min', 'max', 'count', 'kurt', 'skew']:

            result = getattr(r, f)()
            expected = g.apply(lambda x: getattr(x.expanding(), f)())
            tm.assert_frame_equal(result, expected)

        for f in ['std', 'var']:
            result = getattr(r, f)(ddof=0)
            expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
            tm.assert_frame_equal(result, expected)

        result = r.quantile(0.5)
        expected = g.apply(lambda x: x.expanding().quantile(0.5))
        tm.assert_frame_equal(result, expected)

    def test_expanding_corr_cov(self):
        g = self.frame.groupby('A')
        r = g.expanding()

        for f in ['corr', 'cov']:
            result = getattr(r, f)(self.frame)

            def func(x):
                return getattr(x.expanding(), f)(self.frame)
            expected = g.apply(func)
            tm.assert_frame_equal(result, expected)

            result = getattr(r.B, f)(pairwise=True)

            def func(x):
                return getattr(x.B.expanding(), f)(pairwise=True)
            expected = g.apply(func)
            tm.assert_series_equal(result, expected)

    def test_expanding_apply(self, raw):
        g = self.frame.groupby('A')
        r = g.expanding()

        # reduction
        result = r.apply(lambda x: x.sum(), raw=raw)
        expected = g.apply(
            lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw))
        tm.assert_frame_equal(result, expected)


class TestRollingTS(object):

    # rolling time-series friendly
    # xref GH13327

    def setup_method(self, method):

        self.regular = DataFrame({'A': pd.date_range('20130101',
                                                     periods=5,
                                                     freq='s'),
                                  'B': range(5)}).set_index('A')

        self.ragged = DataFrame({'B': range(5)})
        self.ragged.index = [Timestamp('20130101 09:00:00'),
                             Timestamp('20130101 09:00:02'),
                             Timestamp('20130101 09:00:03'),
                             Timestamp('20130101 09:00:05'),
                             Timestamp('20130101 09:00:06')]

    def test_doc_string(self):

        df = DataFrame({'B': [0, 1, 2, np.nan, 4]},
                       index=[Timestamp('20130101 09:00:00'),
                              Timestamp('20130101 09:00:02'),
                              Timestamp('20130101 09:00:03'),
                              Timestamp('20130101 09:00:05'),
                              Timestamp('20130101 09:00:06')])
        df
        df.rolling('2s').sum()

    def test_valid(self):

        df = self.regular

        # not a valid freq
        with pytest.raises(ValueError):
            df.rolling(window='foobar')

        # not a datetimelike index
        with pytest.raises(ValueError):
            df.reset_index().rolling(window='foobar')

        # non-fixed freqs
        for freq in ['2MS', pd.offsets.MonthBegin(2)]:
            with pytest.raises(ValueError):
                df.rolling(window=freq)

        for freq in ['1D', pd.offsets.Day(2), '2ms']:
            df.rolling(window=freq)

        # non-integer min_periods
        for minp in [1.0, 'foo', np.array([1, 2, 3])]:
            with pytest.raises(ValueError):
                df.rolling(window='1D', min_periods=minp)

        # center is not implemented
        with pytest.raises(NotImplementedError):
            df.rolling(window='1D', center=True)

    def test_on(self):

        df = self.regular

        # not a valid column
        with pytest.raises(ValueError):
            df.rolling(window='2s', on='foobar')

        # column is valid
        df = df.copy()
        df['C'] = pd.date_range('20130101', periods=len(df))
        df.rolling(window='2d', on='C').sum()

        # invalid columns
        with pytest.raises(ValueError):
            df.rolling(window='2d', on='B')

        # ok even though on non-selected
        df.rolling(window='2d', on='C').B.sum()

    def test_monotonic_on(self):

        # on/index must be monotonic
        df = DataFrame({'A': pd.date_range('20130101',
                                           periods=5,
                                           freq='s'),
                        'B': range(5)})

        assert df.A.is_monotonic
        df.rolling('2s', on='A').sum()

        df = df.set_index('A')
        assert df.index.is_monotonic
        df.rolling('2s').sum()

        # non-monotonic
        df.index = reversed(df.index.tolist())
        assert not df.index.is_monotonic

        with pytest.raises(ValueError):
            df.rolling('2s').sum()

        df = df.reset_index()
        with pytest.raises(ValueError):
            df.rolling('2s', on='A').sum()

    def test_frame_on(self):

        df = DataFrame({'B': range(5),
                        'C': pd.date_range('20130101 09:00:00',
                                           periods=5,
                                           freq='3s')})

        df['A'] = [Timestamp('20130101 09:00:00'),
                   Timestamp('20130101 09:00:02'),
                   Timestamp('20130101 09:00:03'),
                   Timestamp('20130101 09:00:05'),
                   Timestamp('20130101 09:00:06')]

        # we are doing simulating using 'on'
        expected = (df.set_index('A')
                    .rolling('2s')
                    .B
                    .sum()
                    .reset_index(drop=True)
                    )

        result = (df.rolling('2s', on='A')
                    .B
                    .sum()
                  )
        tm.assert_series_equal(result, expected)

        # test as a frame
        # we should be ignoring the 'on' as an aggregation column
        # note that the expected is setting, computing, and resetting
        # so the columns need to be switched compared
        # to the actual result where they are ordered as in the
        # original
        expected = (df.set_index('A')
                      .rolling('2s')[['B']]
                      .sum()
                      .reset_index()[['B', 'A']]
                    )

        result = (df.rolling('2s', on='A')[['B']]
                    .sum()
                  )
        tm.assert_frame_equal(result, expected)

    def test_frame_on2(self):

        # using multiple aggregation columns
        df = DataFrame({'A': [0, 1, 2, 3, 4],
                        'B': [0, 1, 2, np.nan, 4],
                        'C': Index([Timestamp('20130101 09:00:00'),
                                    Timestamp('20130101 09:00:02'),
                                    Timestamp('20130101 09:00:03'),
                                    Timestamp('20130101 09:00:05'),
                                    Timestamp('20130101 09:00:06')])},
                       columns=['A', 'C', 'B'])

        expected1 = DataFrame({'A': [0., 1, 3, 3, 7],
                               'B': [0, 1, 3, np.nan, 4],
                               'C': df['C']},
                              columns=['A', 'C', 'B'])

        result = df.rolling('2s', on='C').sum()
        expected = expected1
        tm.assert_frame_equal(result, expected)

        expected = Series([0, 1, 3, np.nan, 4], name='B')
        result = df.rolling('2s', on='C').B.sum()
        tm.assert_series_equal(result, expected)

        expected = expected1[['A', 'B', 'C']]
        result = df.rolling('2s', on='C')[['A', 'B', 'C']].sum()
        tm.assert_frame_equal(result, expected)

    def test_basic_regular(self):

        df = self.regular.copy()

        df.index = pd.date_range('20130101', periods=5, freq='D')
        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='1D').sum()
        tm.assert_frame_equal(result, expected)

        df.index = pd.date_range('20130101', periods=5, freq='2D')
        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='2D', min_periods=1).sum()
        tm.assert_frame_equal(result, expected)

        expected = df.rolling(window=1, min_periods=1).sum()
        result = df.rolling(window='2D', min_periods=1).sum()
        tm.assert_frame_equal(result, expected)

        expected = df.rolling(window=1).sum()
        result = df.rolling(window='2D').sum()
        tm.assert_frame_equal(result, expected)

    def test_min_periods(self):

        # compare for min_periods
        df = self.regular

        # these slightly different
        expected = df.rolling(2, min_periods=1).sum()
        result = df.rolling('2s').sum()
        tm.assert_frame_equal(result, expected)

        expected = df.rolling(2, min_periods=1).sum()
        result = df.rolling('2s', min_periods=1).sum()
        tm.assert_frame_equal(result, expected)

    def test_closed(self):

        # xref GH13965

        df = DataFrame({'A': [1] * 5},
                       index=[Timestamp('20130101 09:00:01'),
                              Timestamp('20130101 09:00:02'),
                              Timestamp('20130101 09:00:03'),
                              Timestamp('20130101 09:00:04'),
                              Timestamp('20130101 09:00:06')])

        # closed must be 'right', 'left', 'both', 'neither'
        with pytest.raises(ValueError):
            self.regular.rolling(window='2s', closed="blabla")

        expected = df.copy()
        expected["A"] = [1.0, 2, 2, 2, 1]
        result = df.rolling('2s', closed='right').sum()
        tm.assert_frame_equal(result, expected)

        # default should be 'right'
        result = df.rolling('2s').sum()
        tm.assert_frame_equal(result, expected)

        expected = df.copy()
        expected["A"] = [1.0, 2, 3, 3, 2]
        result = df.rolling('2s', closed='both').sum()
        tm.assert_frame_equal(result, expected)

        expected = df.copy()
        expected["A"] = [np.nan, 1.0, 2, 2, 1]
        result = df.rolling('2s', closed='left').sum()
        tm.assert_frame_equal(result, expected)

        expected = df.copy()
        expected["A"] = [np.nan, 1.0, 1, 1, np.nan]
        result = df.rolling('2s', closed='neither').sum()
        tm.assert_frame_equal(result, expected)

    def test_ragged_sum(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 3, 3, 7]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=2).sum()
        expected = df.copy()
        expected['B'] = [np.nan, np.nan, 3, np.nan, 7]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s', min_periods=1).sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 3, 5, 7]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s').sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 3, 5, 7]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='4s', min_periods=1).sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 3, 6, 9]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='4s', min_periods=3).sum()
        expected = df.copy()
        expected['B'] = [np.nan, np.nan, 3, 6, 9]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).sum()
        expected = df.copy()
        expected['B'] = [0.0, 1, 3, 6, 10]
        tm.assert_frame_equal(result, expected)

    def test_ragged_mean(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).mean()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).mean()
        expected = df.copy()
        expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
        tm.assert_frame_equal(result, expected)

    def test_ragged_median(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).median()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).median()
        expected = df.copy()
        expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
        tm.assert_frame_equal(result, expected)

    def test_ragged_quantile(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).quantile(0.5)
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).quantile(0.5)
        expected = df.copy()
        expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
        tm.assert_frame_equal(result, expected)

    def test_ragged_std(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).std(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='1s', min_periods=1).std(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s', min_periods=1).std(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] + [0.5] * 4
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).std(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan, 0.707107, 1.0, 1.0, 1.290994]
        tm.assert_frame_equal(result, expected)

    def test_ragged_var(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).var(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='1s', min_periods=1).var(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='3s', min_periods=1).var(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] + [0.25] * 4
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).var(ddof=1)
        expected = df.copy()
        expected['B'] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.]
        tm.assert_frame_equal(result, expected)

    def test_ragged_skew(self):

        df = self.ragged
        result = df.rolling(window='3s', min_periods=1).skew()
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).skew()
        expected = df.copy()
        expected['B'] = [np.nan] * 2 + [0.0, 0.0, 0.0]
        tm.assert_frame_equal(result, expected)

    def test_ragged_kurt(self):

        df = self.ragged
        result = df.rolling(window='3s', min_periods=1).kurt()
        expected = df.copy()
        expected['B'] = [np.nan] * 5
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).kurt()
        expected = df.copy()
        expected['B'] = [np.nan] * 4 + [-1.2]
        tm.assert_frame_equal(result, expected)

    def test_ragged_count(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).count()
        expected = df.copy()
        expected['B'] = [1.0, 1, 1, 1, 1]
        tm.assert_frame_equal(result, expected)

        df = self.ragged
        result = df.rolling(window='1s').count()
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).count()
        expected = df.copy()
        expected['B'] = [1.0, 1, 2, 1, 2]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=2).count()
        expected = df.copy()
        expected['B'] = [np.nan, np.nan, 2, np.nan, 2]
        tm.assert_frame_equal(result, expected)

    def test_regular_min(self):

        df = DataFrame({'A': pd.date_range('20130101',
                                           periods=5,
                                           freq='s'),
                        'B': [0.0, 1, 2, 3, 4]}).set_index('A')
        result = df.rolling('1s').min()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        df = DataFrame({'A': pd.date_range('20130101',
                                           periods=5,
                                           freq='s'),
                        'B': [5, 4, 3, 4, 5]}).set_index('A')

        tm.assert_frame_equal(result, expected)
        result = df.rolling('2s').min()
        expected = df.copy()
        expected['B'] = [5.0, 4, 3, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling('5s').min()
        expected = df.copy()
        expected['B'] = [5.0, 4, 3, 3, 3]
        tm.assert_frame_equal(result, expected)

    def test_ragged_min(self):

        df = self.ragged

        result = df.rolling(window='1s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 1, 1, 3, 3]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).min()
        expected = df.copy()
        expected['B'] = [0.0, 0, 0, 1, 1]
        tm.assert_frame_equal(result, expected)

    def test_perf_min(self):

        N = 10000

        dfp = DataFrame({'B': np.random.randn(N)},
                        index=pd.date_range('20130101',
                                            periods=N,
                                            freq='s'))
        expected = dfp.rolling(2, min_periods=1).min()
        result = dfp.rolling('2s').min()
        assert ((result - expected) < 0.01).all().bool()

        expected = dfp.rolling(200, min_periods=1).min()
        result = dfp.rolling('200s').min()
        assert ((result - expected) < 0.01).all().bool()

    def test_ragged_max(self):

        df = self.ragged

        result = df.rolling(window='1s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).max()
        expected = df.copy()
        expected['B'] = [0.0, 1, 2, 3, 4]
        tm.assert_frame_equal(result, expected)

    def test_ragged_apply(self, raw):

        df = self.ragged

        f = lambda x: 1
        result = df.rolling(window='1s', min_periods=1).apply(f, raw=raw)
        expected = df.copy()
        expected['B'] = 1.
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='2s', min_periods=1).apply(f, raw=raw)
        expected = df.copy()
        expected['B'] = 1.
        tm.assert_frame_equal(result, expected)

        result = df.rolling(window='5s', min_periods=1).apply(f, raw=raw)
        expected = df.copy()
        expected['B'] = 1.
        tm.assert_frame_equal(result, expected)

    def test_all(self):

        # simple comparison of integer vs time-based windowing
        df = self.regular * 2
        er = df.rolling(window=1)
        r = df.rolling(window='1s')

        for f in ['sum', 'mean', 'count', 'median', 'std',
                  'var', 'kurt', 'skew', 'min', 'max']:

            result = getattr(r, f)()
            expected = getattr(er, f)()
            tm.assert_frame_equal(result, expected)

        result = r.quantile(0.5)
        expected = er.quantile(0.5)
        tm.assert_frame_equal(result, expected)

    def test_all_apply(self, raw):

        df = self.regular * 2
        er = df.rolling(window=1)
        r = df.rolling(window='1s')

        result = r.apply(lambda x: 1, raw=raw)
        expected = er.apply(lambda x: 1, raw=raw)
        tm.assert_frame_equal(result, expected)

    def test_all2(self):

        # more sophisticated comparison of integer vs.
        # time-based windowing
        df = DataFrame({'B': np.arange(50)},
                       index=pd.date_range('20130101',
                                           periods=50, freq='H')
                       )
        # in-range data
        dft = df.between_time("09:00", "16:00")

        r = dft.rolling(window='5H')

        for f in ['sum', 'mean', 'count', 'median', 'std',
                  'var', 'kurt', 'skew', 'min', 'max']:

            result = getattr(r, f)()

            # we need to roll the days separately
            # to compare with a time-based roll
            # finally groupby-apply will return a multi-index
            # so we need to drop the day
            def agg_by_day(x):
                x = x.between_time("09:00", "16:00")
                return getattr(x.rolling(5, min_periods=1), f)()
            expected = df.groupby(df.index.day).apply(
                agg_by_day).reset_index(level=0, drop=True)

            tm.assert_frame_equal(result, expected)

    def test_groupby_monotonic(self):

        # GH 15130
        # we don't need to validate monotonicity when grouping

        data = [
            ['David', '1/1/2015', 100], ['David', '1/5/2015', 500],
            ['David', '5/30/2015', 50], ['David', '7/25/2015', 50],
            ['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500],
            ['Ryan', '3/31/2016', 50], ['Joe', '7/1/2015', 100],
            ['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50]]

        df = DataFrame(data=data, columns=['name', 'date', 'amount'])
        df['date'] = pd.to_datetime(df['date'])

        expected = df.set_index('date').groupby('name').apply(
            lambda x: x.rolling('180D')['amount'].sum())
        result = df.groupby('name').rolling('180D', on='date')['amount'].sum()
        tm.assert_series_equal(result, expected)

    def test_non_monotonic(self):
        # GH 13966 (similar to #15130, closed by #15175)

        dates = pd.date_range(start='2016-01-01 09:30:00',
                              periods=20, freq='s')
        df = DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                        'B': np.concatenate((dates, dates)),
                        'C': np.arange(40)})

        result = df.groupby('A').rolling('4s', on='B').C.mean()
        expected = df.set_index('B').groupby('A').apply(
            lambda x: x.rolling('4s')['C'].mean())
        tm.assert_series_equal(result, expected)

        df2 = df.sort_values('B')
        result = df2.groupby('A').rolling('4s', on='B').C.mean()
        tm.assert_series_equal(result, expected)

    def test_rolling_cov_offset(self):
        # GH16058

        idx = pd.date_range('2017-01-01', periods=24, freq='1h')
        ss = Series(np.arange(len(idx)), index=idx)

        result = ss.rolling('2h').cov()
        expected = Series([np.nan] + [0.5] * (len(idx) - 1), index=idx)
        tm.assert_series_equal(result, expected)

        expected2 = ss.rolling(2, min_periods=1).cov()
        tm.assert_series_equal(result, expected2)

        result = ss.rolling('3h').cov()
        expected = Series([np.nan, 0.5] + [1.0] * (len(idx) - 2), index=idx)
        tm.assert_series_equal(result, expected)

        expected2 = ss.rolling(3, min_periods=1).cov()
        tm.assert_series_equal(result, expected2)
