""" test with the .transform """

import pytest

import numpy as np
import pandas as pd
from pandas.util import testing as tm
from pandas import Series, DataFrame, Timestamp, MultiIndex, concat, date_range
from pandas.core.dtypes.common import (
    _ensure_platform_int, is_timedelta64_dtype)
from pandas.compat import StringIO
from pandas._libs import groupby

from pandas.util.testing import assert_frame_equal, assert_series_equal
from pandas.core.groupby.groupby import DataError
from pandas.core.config import option_context


def assert_fp_equal(a, b):
    assert (np.abs(a - b) < 1e-12).all()


def test_transform():
    data = Series(np.arange(9) // 3, index=np.arange(9))

    index = np.arange(9)
    np.random.shuffle(index)
    data = data.reindex(index)

    grouped = data.groupby(lambda x: x // 3)

    transformed = grouped.transform(lambda x: x * x.sum())
    assert transformed[7] == 12

    # GH 8046
    # make sure that we preserve the input order

    df = DataFrame(
        np.arange(6, dtype='int64').reshape(
            3, 2), columns=["a", "b"], index=[0, 2, 1])
    key = [0, 0, 1]
    expected = df.sort_index().groupby(key).transform(
        lambda x: x - x.mean()).groupby(key).mean()
    result = df.groupby(key).transform(lambda x: x - x.mean()).groupby(
        key).mean()
    assert_frame_equal(result, expected)

    def demean(arr):
        return arr - arr.mean()

    people = DataFrame(np.random.randn(5, 5),
                       columns=['a', 'b', 'c', 'd', 'e'],
                       index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
    key = ['one', 'two', 'one', 'two', 'one']
    result = people.groupby(key).transform(demean).groupby(key).mean()
    expected = people.groupby(key).apply(demean).groupby(key).mean()
    assert_frame_equal(result, expected)

    # GH 8430
    df = tm.makeTimeDataFrame()
    g = df.groupby(pd.Grouper(freq='M'))
    g.transform(lambda x: x - 1)

    # GH 9700
    df = DataFrame({'a': range(5, 10), 'b': range(5)})
    result = df.groupby('a').transform(max)
    expected = DataFrame({'b': range(5)})
    tm.assert_frame_equal(result, expected)


def test_transform_fast():

    df = DataFrame({'id': np.arange(100000) / 3,
                    'val': np.random.randn(100000)})

    grp = df.groupby('id')['val']

    values = np.repeat(grp.mean().values,
                       _ensure_platform_int(grp.count().values))
    expected = pd.Series(values, index=df.index, name='val')

    result = grp.transform(np.mean)
    assert_series_equal(result, expected)

    result = grp.transform('mean')
    assert_series_equal(result, expected)

    # GH 12737
    df = pd.DataFrame({'grouping': [0, 1, 1, 3], 'f': [1.1, 2.1, 3.1, 4.5],
                       'd': pd.date_range('2014-1-1', '2014-1-4'),
                       'i': [1, 2, 3, 4]},
                      columns=['grouping', 'f', 'i', 'd'])
    result = df.groupby('grouping').transform('first')

    dates = [pd.Timestamp('2014-1-1'), pd.Timestamp('2014-1-2'),
             pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-4')]
    expected = pd.DataFrame({'f': [1.1, 2.1, 2.1, 4.5],
                             'd': dates,
                             'i': [1, 2, 2, 4]},
                            columns=['f', 'i', 'd'])
    assert_frame_equal(result, expected)

    # selection
    result = df.groupby('grouping')[['f', 'i']].transform('first')
    expected = expected[['f', 'i']]
    assert_frame_equal(result, expected)

    # dup columns
    df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['g', 'a', 'a'])
    result = df.groupby('g').transform('first')
    expected = df.drop('g', axis=1)
    assert_frame_equal(result, expected)


def test_transform_broadcast(tsframe, ts):
    grouped = ts.groupby(lambda x: x.month)
    result = grouped.transform(np.mean)

    tm.assert_index_equal(result.index, ts.index)
    for _, gp in grouped:
        assert_fp_equal(result.reindex(gp.index), gp.mean())

    grouped = tsframe.groupby(lambda x: x.month)
    result = grouped.transform(np.mean)
    tm.assert_index_equal(result.index, tsframe.index)
    for _, gp in grouped:
        agged = gp.mean()
        res = result.reindex(gp.index)
        for col in tsframe:
            assert_fp_equal(res[col], agged[col])

    # group columns
    grouped = tsframe.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
                              axis=1)
    result = grouped.transform(np.mean)
    tm.assert_index_equal(result.index, tsframe.index)
    tm.assert_index_equal(result.columns, tsframe.columns)
    for _, gp in grouped:
        agged = gp.mean(1)
        res = result.reindex(columns=gp.columns)
        for idx in gp.index:
            assert_fp_equal(res.xs(idx), agged[idx])


def test_transform_axis(tsframe):

    # make sure that we are setting the axes
    # correctly when on axis=0 or 1
    # in the presence of a non-monotonic indexer
    # GH12713

    base = tsframe.iloc[0:5]
    r = len(base.index)
    c = len(base.columns)
    tso = DataFrame(np.random.randn(r, c),
                    index=base.index,
                    columns=base.columns,
                    dtype='float64')
    # monotonic
    ts = tso
    grouped = ts.groupby(lambda x: x.weekday())
    result = ts - grouped.transform('mean')
    expected = grouped.apply(lambda x: x - x.mean())
    assert_frame_equal(result, expected)

    ts = ts.T
    grouped = ts.groupby(lambda x: x.weekday(), axis=1)
    result = ts - grouped.transform('mean')
    expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
    assert_frame_equal(result, expected)

    # non-monotonic
    ts = tso.iloc[[1, 0] + list(range(2, len(base)))]
    grouped = ts.groupby(lambda x: x.weekday())
    result = ts - grouped.transform('mean')
    expected = grouped.apply(lambda x: x - x.mean())
    assert_frame_equal(result, expected)

    ts = ts.T
    grouped = ts.groupby(lambda x: x.weekday(), axis=1)
    result = ts - grouped.transform('mean')
    expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
    assert_frame_equal(result, expected)


def test_transform_dtype():
    # GH 9807
    # Check transform dtype output is preserved
    df = DataFrame([[1, 3], [2, 3]])
    result = df.groupby(1).transform('mean')
    expected = DataFrame([[1.5], [1.5]])
    assert_frame_equal(result, expected)


def test_transform_bug():
    # GH 5712
    # transforming on a datetime column
    df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5)))
    result = df.groupby('A')['B'].transform(
        lambda x: x.rank(ascending=False))
    expected = Series(np.arange(5, 0, step=-1), name='B')
    assert_series_equal(result, expected)


def test_transform_numeric_to_boolean():
    # GH 16875
    # inconsistency in transforming boolean values
    expected = pd.Series([True, True], name='A')

    df = pd.DataFrame({'A': [1.1, 2.2], 'B': [1, 2]})
    result = df.groupby('B').A.transform(lambda x: True)
    assert_series_equal(result, expected)

    df = pd.DataFrame({'A': [1, 2], 'B': [1, 2]})
    result = df.groupby('B').A.transform(lambda x: True)
    assert_series_equal(result, expected)


def test_transform_datetime_to_timedelta():
    # GH 15429
    # transforming a datetime to timedelta
    df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5)))
    expected = pd.Series([
        Timestamp('20130101') - Timestamp('20130101')] * 5, name='A')

    # this does date math without changing result type in transform
    base_time = df['A'][0]
    result = df.groupby('A')['A'].transform(
        lambda x: x.max() - x.min() + base_time) - base_time
    assert_series_equal(result, expected)

    # this does date math and causes the transform to return timedelta
    result = df.groupby('A')['A'].transform(lambda x: x.max() - x.min())
    assert_series_equal(result, expected)


def test_transform_datetime_to_numeric():
    # GH 10972
    # convert dt to float
    df = DataFrame({
        'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')})
    result = df.groupby('a').b.transform(
        lambda x: x.dt.dayofweek - x.dt.dayofweek.mean())

    expected = Series([-0.5, 0.5], name='b')
    assert_series_equal(result, expected)

    # convert dt to int
    df = DataFrame({
        'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')})
    result = df.groupby('a').b.transform(
        lambda x: x.dt.dayofweek - x.dt.dayofweek.min())

    expected = Series([0, 1], name='b')
    assert_series_equal(result, expected)


def test_transform_casting():
    # 13046
    data = """
    idx     A         ID3              DATETIME
    0   B-028  b76cd912ff "2014-10-08 13:43:27"
    1   B-054  4a57ed0b02 "2014-10-08 14:26:19"
    2   B-076  1a682034f8 "2014-10-08 14:29:01"
    3   B-023  b76cd912ff "2014-10-08 18:39:34"
    4   B-023  f88g8d7sds "2014-10-08 18:40:18"
    5   B-033  b76cd912ff "2014-10-08 18:44:30"
    6   B-032  b76cd912ff "2014-10-08 18:46:00"
    7   B-037  b76cd912ff "2014-10-08 18:52:15"
    8   B-046  db959faf02 "2014-10-08 18:59:59"
    9   B-053  b76cd912ff "2014-10-08 19:17:48"
    10  B-065  b76cd912ff "2014-10-08 19:21:38"
    """
    df = pd.read_csv(StringIO(data), sep=r'\s+',
                     index_col=[0], parse_dates=['DATETIME'])

    result = df.groupby('ID3')['DATETIME'].transform(lambda x: x.diff())
    assert is_timedelta64_dtype(result.dtype)

    result = df[['ID3', 'DATETIME']].groupby('ID3').transform(
        lambda x: x.diff())
    assert is_timedelta64_dtype(result.DATETIME.dtype)


def test_transform_multiple(ts):
    grouped = ts.groupby([lambda x: x.year, lambda x: x.month])

    grouped.transform(lambda x: x * 2)
    grouped.transform(np.mean)


def test_dispatch_transform(tsframe):
    df = tsframe[::5].reindex(tsframe.index)

    grouped = df.groupby(lambda x: x.month)

    filled = grouped.fillna(method='pad')
    fillit = lambda x: x.fillna(method='pad')
    expected = df.groupby(lambda x: x.month).transform(fillit)
    assert_frame_equal(filled, expected)


def test_transform_select_columns(df):
    f = lambda x: x.mean()
    result = df.groupby('A')['C', 'D'].transform(f)

    selection = df[['C', 'D']]
    expected = selection.groupby(df['A']).transform(f)

    assert_frame_equal(result, expected)


def test_transform_exclude_nuisance(df):

    # this also tests orderings in transform between
    # series/frame to make sure it's consistent
    expected = {}
    grouped = df.groupby('A')
    expected['C'] = grouped['C'].transform(np.mean)
    expected['D'] = grouped['D'].transform(np.mean)
    expected = DataFrame(expected)
    result = df.groupby('A').transform(np.mean)

    assert_frame_equal(result, expected)


def test_transform_function_aliases(df):
    result = df.groupby('A').transform('mean')
    expected = df.groupby('A').transform(np.mean)
    assert_frame_equal(result, expected)

    result = df.groupby('A')['C'].transform('mean')
    expected = df.groupby('A')['C'].transform(np.mean)
    assert_series_equal(result, expected)


def test_series_fast_transform_date():
    # GH 13191
    df = pd.DataFrame({'grouping': [np.nan, 1, 1, 3],
                       'd': pd.date_range('2014-1-1', '2014-1-4')})
    result = df.groupby('grouping')['d'].transform('first')
    dates = [pd.NaT, pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-2'),
             pd.Timestamp('2014-1-4')]
    expected = pd.Series(dates, name='d')
    assert_series_equal(result, expected)


def test_transform_length():
    # GH 9697
    df = pd.DataFrame({'col1': [1, 1, 2, 2], 'col2': [1, 2, 3, np.nan]})
    expected = pd.Series([3.0] * 4)

    def nsum(x):
        return np.nansum(x)

    results = [df.groupby('col1').transform(sum)['col2'],
               df.groupby('col1')['col2'].transform(sum),
               df.groupby('col1').transform(nsum)['col2'],
               df.groupby('col1')['col2'].transform(nsum)]
    for result in results:
        assert_series_equal(result, expected, check_names=False)


def test_transform_coercion():

    # 14457
    # when we are transforming be sure to not coerce
    # via assignment
    df = pd.DataFrame(dict(A=['a', 'a'], B=[0, 1]))
    g = df.groupby('A')

    expected = g.transform(np.mean)
    result = g.transform(lambda x: np.mean(x))
    assert_frame_equal(result, expected)


def test_groupby_transform_with_int():

    # GH 3740, make sure that we might upcast on item-by-item transform

    # floats
    df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=Series(1, dtype='float64'),
                        C=Series(
                            [1, 2, 3, 1, 2, 3], dtype='float64'), D='foo'))
    with np.errstate(all='ignore'):
        result = df.groupby('A').transform(
            lambda x: (x - x.mean()) / x.std())
    expected = DataFrame(dict(B=np.nan, C=Series(
        [-1, 0, 1, -1, 0, 1], dtype='float64')))
    assert_frame_equal(result, expected)

    # int case
    df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1,
                        C=[1, 2, 3, 1, 2, 3], D='foo'))
    with np.errstate(all='ignore'):
        result = df.groupby('A').transform(
            lambda x: (x - x.mean()) / x.std())
    expected = DataFrame(dict(B=np.nan, C=[-1, 0, 1, -1, 0, 1]))
    assert_frame_equal(result, expected)

    # int that needs float conversion
    s = Series([2, 3, 4, 10, 5, -1])
    df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, C=s, D='foo'))
    with np.errstate(all='ignore'):
        result = df.groupby('A').transform(
            lambda x: (x - x.mean()) / x.std())

    s1 = s.iloc[0:3]
    s1 = (s1 - s1.mean()) / s1.std()
    s2 = s.iloc[3:6]
    s2 = (s2 - s2.mean()) / s2.std()
    expected = DataFrame(dict(B=np.nan, C=concat([s1, s2])))
    assert_frame_equal(result, expected)

    # int downcasting
    result = df.groupby('A').transform(lambda x: x * 2 / 2)
    expected = DataFrame(dict(B=1, C=[2, 3, 4, 10, 5, -1]))
    assert_frame_equal(result, expected)


def test_groupby_transform_with_nan_group():
    # GH 9941
    df = pd.DataFrame({'a': range(10),
                       'b': [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]})
    result = df.groupby(df.b)['a'].transform(max)
    expected = pd.Series([1., 1., 2., 3., np.nan, 6., 6., 9., 9., 9.],
                         name='a')
    assert_series_equal(result, expected)


def test_transform_mixed_type():
    index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]
                                    ])
    df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                    'c': np.tile(['a', 'b', 'c'], 2),
                    'v': np.arange(1., 7.)}, index=index)

    def f(group):
        group['g'] = group['d'] * 2
        return group[:1]

    grouped = df.groupby('c')
    result = grouped.apply(f)

    assert result['d'].dtype == np.float64

    # this is by definition a mutating operation!
    with option_context('mode.chained_assignment', None):
        for key, group in grouped:
            res = f(group)
            assert_frame_equal(res, result.loc[key])


def test_cython_group_transform_algos():
    # GH 4095
    dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32,
              np.uint64, np.float32, np.float64]

    ops = [(groupby.group_cumprod_float64, np.cumproduct, [np.float64]),
           (groupby.group_cumsum, np.cumsum, dtypes)]

    is_datetimelike = False
    for pd_op, np_op, dtypes in ops:
        for dtype in dtypes:
            data = np.array([[1], [2], [3], [4]], dtype=dtype)
            ans = np.zeros_like(data)
            labels = np.array([0, 0, 0, 0], dtype=np.int64)
            pd_op(ans, data, labels, is_datetimelike)
            tm.assert_numpy_array_equal(np_op(data), ans[:, 0],
                                        check_dtype=False)

    # with nans
    labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)

    data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64')
    actual = np.zeros_like(data)
    actual.fill(np.nan)
    groupby.group_cumprod_float64(actual, data, labels, is_datetimelike)
    expected = np.array([1, 2, 6, np.nan, 24], dtype='float64')
    tm.assert_numpy_array_equal(actual[:, 0], expected)

    actual = np.zeros_like(data)
    actual.fill(np.nan)
    groupby.group_cumsum(actual, data, labels, is_datetimelike)
    expected = np.array([1, 3, 6, np.nan, 10], dtype='float64')
    tm.assert_numpy_array_equal(actual[:, 0], expected)

    # timedelta
    is_datetimelike = True
    data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None]
    actual = np.zeros_like(data, dtype='int64')
    groupby.group_cumsum(actual, data.view('int64'), labels,
                         is_datetimelike)
    expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64(
        2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'),
        np.timedelta64(5, 'ns')])
    tm.assert_numpy_array_equal(actual[:, 0].view('m8[ns]'), expected)


@pytest.mark.parametrize(
    "op, args, targop",
    [('cumprod', (), lambda x: x.cumprod()),
     ('cumsum', (), lambda x: x.cumsum()),
     ('shift', (-1, ), lambda x: x.shift(-1)),
     ('shift', (1, ), lambda x: x.shift())])
def test_cython_transform_series(op, args, targop):
    # GH 4095
    s = Series(np.random.randn(1000))
    s_missing = s.copy()
    s_missing.iloc[2:10] = np.nan
    labels = np.random.randint(0, 50, size=1000).astype(float)

    # series
    for data in [s, s_missing]:
        # print(data.head())
        expected = data.groupby(labels).transform(targop)

        tm.assert_series_equal(
            expected,
            data.groupby(labels).transform(op, *args))
        tm.assert_series_equal(expected, getattr(
            data.groupby(labels), op)(*args))


@pytest.mark.parametrize("op", ['cumprod', 'cumsum'])
@pytest.mark.parametrize("skipna", [False, True])
@pytest.mark.parametrize('input, exp', [
    # When everything is NaN
    ({'key': ['b'] * 10, 'value': np.nan},
     pd.Series([np.nan] * 10, name='value')),
    # When there is a single NaN
    ({'key': ['b'] * 10 + ['a'] * 2,
      'value': [3] * 3 + [np.nan] + [3] * 8},
     {('cumprod', False): [3.0, 9.0, 27.0] + [np.nan] * 7 + [3.0, 9.0],
      ('cumprod', True): [3.0, 9.0, 27.0, np.nan, 81., 243., 729.,
                          2187., 6561., 19683., 3.0, 9.0],
      ('cumsum', False): [3.0, 6.0, 9.0] + [np.nan] * 7 + [3.0, 6.0],
      ('cumsum', True): [3.0, 6.0, 9.0, np.nan, 12., 15., 18.,
                         21., 24., 27., 3.0, 6.0]})])
def test_groupby_cum_skipna(op, skipna, input, exp):
    df = pd.DataFrame(input)
    result = df.groupby('key')['value'].transform(op, skipna=skipna)
    if isinstance(exp, dict):
        expected = exp[(op, skipna)]
    else:
        expected = exp
    expected = pd.Series(expected, name='value')
    tm.assert_series_equal(expected, result)


@pytest.mark.parametrize(
    "op, args, targop",
    [('cumprod', (), lambda x: x.cumprod()),
     ('cumsum', (), lambda x: x.cumsum()),
     ('shift', (-1, ), lambda x: x.shift(-1)),
     ('shift', (1, ), lambda x: x.shift())])
def test_cython_transform_frame(op, args, targop):
    s = Series(np.random.randn(1000))
    s_missing = s.copy()
    s_missing.iloc[2:10] = np.nan
    labels = np.random.randint(0, 50, size=1000).astype(float)
    strings = list('qwertyuiopasdfghjklz')
    strings_missing = strings[:]
    strings_missing[5] = np.nan
    df = DataFrame({'float': s,
                    'float_missing': s_missing,
                    'int': [1, 1, 1, 1, 2] * 200,
                    'datetime': pd.date_range('1990-1-1', periods=1000),
                    'timedelta': pd.timedelta_range(1, freq='s',
                                                    periods=1000),
                    'string': strings * 50,
                    'string_missing': strings_missing * 50},
                   columns=['float', 'float_missing', 'int', 'datetime',
                            'timedelta', 'string', 'string_missing'])
    df['cat'] = df['string'].astype('category')

    df2 = df.copy()
    df2.index = pd.MultiIndex.from_product([range(100), range(10)])

    # DataFrame - Single and MultiIndex,
    # group by values, index level, columns
    for df in [df, df2]:
        for gb_target in [dict(by=labels), dict(level=0), dict(by='string')
                          ]:  # dict(by='string_missing')]:
            # dict(by=['int','string'])]:

            gb = df.groupby(**gb_target)
            # whitelisted methods set the selection before applying
            # bit a of hack to make sure the cythonized shift
            # is equivalent to pre 0.17.1 behavior
            if op == 'shift':
                gb._set_group_selection()

            if op != 'shift' and 'int' not in gb_target:
                # numeric apply fastpath promotes dtype so have
                # to apply separately and concat
                i = gb[['int']].apply(targop)
                f = gb[['float', 'float_missing']].apply(targop)
                expected = pd.concat([f, i], axis=1)
            else:
                expected = gb.apply(targop)

            expected = expected.sort_index(axis=1)
            tm.assert_frame_equal(expected,
                                  gb.transform(op, *args).sort_index(
                                      axis=1))
            tm.assert_frame_equal(
                expected,
                getattr(gb, op)(*args).sort_index(axis=1))
            # individual columns
            for c in df:
                if c not in ['float', 'int', 'float_missing'
                             ] and op != 'shift':
                    pytest.raises(DataError, gb[c].transform, op)
                    pytest.raises(DataError, getattr(gb[c], op))
                else:
                    expected = gb[c].apply(targop)
                    expected.name = c
                    tm.assert_series_equal(expected,
                                           gb[c].transform(op, *args))
                    tm.assert_series_equal(expected,
                                           getattr(gb[c], op)(*args))


def test_transform_with_non_scalar_group():
    # GH 10165
    cols = pd.MultiIndex.from_tuples([
        ('syn', 'A'), ('mis', 'A'), ('non', 'A'),
        ('syn', 'C'), ('mis', 'C'), ('non', 'C'),
        ('syn', 'T'), ('mis', 'T'), ('non', 'T'),
        ('syn', 'G'), ('mis', 'G'), ('non', 'G')])
    df = pd.DataFrame(np.random.randint(1, 10, (4, 12)),
                      columns=cols,
                      index=['A', 'C', 'G', 'T'])
    tm.assert_raises_regex(ValueError, 'transform must return '
                           'a scalar value for each '
                           'group.*',
                           df.groupby(axis=1, level=1).transform,
                           lambda z: z.div(z.sum(axis=1), axis=0))


@pytest.mark.parametrize('cols,exp,comp_func', [
    ('a', pd.Series([1, 1, 1], name='a'), tm.assert_series_equal),
    (['a', 'c'], pd.DataFrame({'a': [1, 1, 1], 'c': [1, 1, 1]}),
     tm.assert_frame_equal)
])
@pytest.mark.parametrize('agg_func', [
    'count', 'rank', 'size'])
def test_transform_numeric_ret(cols, exp, comp_func, agg_func):
    if agg_func == 'size' and isinstance(cols, list):
        pytest.xfail("'size' transformation not supported with "
                     "NDFrameGroupy")

    # GH 19200
    df = pd.DataFrame(
        {'a': pd.date_range('2018-01-01', periods=3),
         'b': range(3),
         'c': range(7, 10)})

    result = df.groupby('b')[cols].transform(agg_func)

    if agg_func == 'rank':
        exp = exp.astype('float')

    comp_func(result, exp)


@pytest.mark.parametrize("mix_groupings", [True, False])
@pytest.mark.parametrize("as_series", [True, False])
@pytest.mark.parametrize("val1,val2", [
    ('foo', 'bar'), (1, 2), (1., 2.)])
@pytest.mark.parametrize("fill_method,limit,exp_vals", [
    ("ffill", None,
     [np.nan, np.nan, 'val1', 'val1', 'val1', 'val2', 'val2', 'val2']),
    ("ffill", 1,
     [np.nan, np.nan, 'val1', 'val1', np.nan, 'val2', 'val2', np.nan]),
    ("bfill", None,
     ['val1', 'val1', 'val1', 'val2', 'val2', 'val2', np.nan, np.nan]),
    ("bfill", 1,
     [np.nan, 'val1', 'val1', np.nan, 'val2', 'val2', np.nan, np.nan])
])
def test_group_fill_methods(mix_groupings, as_series, val1, val2,
                            fill_method, limit, exp_vals):
    vals = [np.nan, np.nan, val1, np.nan, np.nan, val2, np.nan, np.nan]
    _exp_vals = list(exp_vals)
    # Overwrite placeholder values
    for index, exp_val in enumerate(_exp_vals):
        if exp_val == 'val1':
            _exp_vals[index] = val1
        elif exp_val == 'val2':
            _exp_vals[index] = val2

    # Need to modify values and expectations depending on the
    # Series / DataFrame that we ultimately want to generate
    if mix_groupings:  # ['a', 'b', 'a, 'b', ...]
        keys = ['a', 'b'] * len(vals)

        def interweave(list_obj):
            temp = list()
            for x in list_obj:
                temp.extend([x, x])

            return temp

        _exp_vals = interweave(_exp_vals)
        vals = interweave(vals)
    else:  # ['a', 'a', 'a', ... 'b', 'b', 'b']
        keys = ['a'] * len(vals) + ['b'] * len(vals)
        _exp_vals = _exp_vals * 2
        vals = vals * 2

    df = DataFrame({'key': keys, 'val': vals})
    if as_series:
        result = getattr(
            df.groupby('key')['val'], fill_method)(limit=limit)
        exp = Series(_exp_vals, name='val')
        assert_series_equal(result, exp)
    else:
        result = getattr(df.groupby('key'), fill_method)(limit=limit)
        exp = DataFrame({'key': keys, 'val': _exp_vals})
        assert_frame_equal(result, exp)


@pytest.mark.parametrize("test_series", [True, False])
@pytest.mark.parametrize("periods,fill_method,limit", [
    (1, 'ffill', None), (1, 'ffill', 1),
    (1, 'bfill', None), (1, 'bfill', 1),
    (-1, 'ffill', None), (-1, 'ffill', 1),
    (-1, 'bfill', None), (-1, 'bfill', 1)])
def test_pct_change(test_series, periods, fill_method, limit):
    vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan]
    exp_vals = Series(vals).pct_change(periods=periods,
                                       fill_method=fill_method,
                                       limit=limit).tolist()

    df = DataFrame({'key': ['a'] * len(vals) + ['b'] * len(vals),
                    'vals': vals * 2})
    grp = df.groupby('key')

    def get_result(grp_obj):
        return grp_obj.pct_change(periods=periods,
                                  fill_method=fill_method,
                                  limit=limit)

    if test_series:
        exp = pd.Series(exp_vals * 2)
        exp.name = 'vals'
        grp = grp['vals']
        result = get_result(grp)
        tm.assert_series_equal(result, exp)
    else:
        exp = DataFrame({'vals': exp_vals * 2})
        result = get_result(grp)
        tm.assert_frame_equal(result, exp)


@pytest.mark.parametrize("func", [np.any, np.all])
def test_any_all_np_func(func):
    # GH 20653
    df = pd.DataFrame([['foo', True],
                       [np.nan, True],
                       ['foo', True]], columns=['key', 'val'])

    exp = pd.Series([True, np.nan, True], name='val')

    res = df.groupby('key')['val'].transform(func)
    tm.assert_series_equal(res, exp)
