text: """ 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("fill_method", ['ffill', 'bfill'])
  def test_pad_stable_sorting(fill_method):
      # GH 21207
      x = [0] * 20
      y = [np.nan] * 10 + [1] * 10
  
      if fill_method == 'bfill':
          y = y[::-1]
  
      df = pd.DataFrame({'x': x, 'y': y})
      expected = df.copy()
  
      result = getattr(df.groupby('x'), fill_method)()
  
      tm.assert_frame_equal(result, expected)
  
  
  @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)
  