text: 
  from datetime import datetime, date, timedelta
  
  import pytest
  
  
  import numpy as np
  
  from collections import OrderedDict
  import pandas as pd
  from pandas import (DataFrame, Series, Index, MultiIndex,
                      Grouper, date_range, concat, Categorical)
  from pandas.core.reshape.pivot import pivot_table, crosstab
  from pandas.compat import range, product
  import pandas.util.testing as tm
  from pandas.api.types import CategoricalDtype as CDT
  
  
  class TestPivotTable(object):
  
      def setup_method(self, method):
          self.data = DataFrame({'A': ['foo', 'foo', 'foo', 'foo',
                                       'bar', 'bar', 'bar', 'bar',
                                       'foo', 'foo', 'foo'],
                                 'B': ['one', 'one', 'one', 'two',
                                       'one', 'one', 'one', 'two',
                                       'two', 'two', 'one'],
                                 'C': ['dull', 'dull', 'shiny', 'dull',
                                       'dull', 'shiny', 'shiny', 'dull',
                                       'shiny', 'shiny', 'shiny'],
                                 'D': np.random.randn(11),
                                 'E': np.random.randn(11),
                                 'F': np.random.randn(11)})
  
      def test_pivot_table(self):
          index = ['A', 'B']
          columns = 'C'
          table = pivot_table(self.data, values='D',
                              index=index, columns=columns)
  
          table2 = self.data.pivot_table(
              values='D', index=index, columns=columns)
          tm.assert_frame_equal(table, table2)
  
          # this works
          pivot_table(self.data, values='D', index=index)
  
          if len(index) > 1:
              assert table.index.names == tuple(index)
          else:
              assert table.index.name == index[0]
  
          if len(columns) > 1:
              assert table.columns.names == columns
          else:
              assert table.columns.name == columns[0]
  
          expected = self.data.groupby(
              index + [columns])['D'].agg(np.mean).unstack()
          tm.assert_frame_equal(table, expected)
  
      def test_pivot_table_nocols(self):
          df = DataFrame({'rows': ['a', 'b', 'c'],
                          'cols': ['x', 'y', 'z'],
                          'values': [1, 2, 3]})
          rs = df.pivot_table(columns='cols', aggfunc=np.sum)
          xp = df.pivot_table(index='cols', aggfunc=np.sum).T
          tm.assert_frame_equal(rs, xp)
  
          rs = df.pivot_table(columns='cols', aggfunc={'values': 'mean'})
          xp = df.pivot_table(index='cols', aggfunc={'values': 'mean'}).T
          tm.assert_frame_equal(rs, xp)
  
      def test_pivot_table_dropna(self):
          df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000},
                          'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'},
                          'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310},
                          'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'},
                          'quantity': {0: 2000000, 1: 500000,
                                       2: 1000000, 3: 1000000}})
          pv_col = df.pivot_table('quantity', 'month', [
                                  'customer', 'product'], dropna=False)
          pv_ind = df.pivot_table(
              'quantity', ['customer', 'product'], 'month', dropna=False)
  
          m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'),
                                      ('A', 'd'), ('B', 'a'), ('B', 'b'),
                                      ('B', 'c'), ('B', 'd'), ('C', 'a'),
                                      ('C', 'b'), ('C', 'c'), ('C', 'd')],
                                     names=['customer', 'product'])
          tm.assert_index_equal(pv_col.columns, m)
          tm.assert_index_equal(pv_ind.index, m)
  
      def test_pivot_table_categorical(self):
  
          cat1 = Categorical(["a", "a", "b", "b"],
                             categories=["a", "b", "z"], ordered=True)
          cat2 = Categorical(["c", "d", "c", "d"],
                             categories=["c", "d", "y"], ordered=True)
          df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
          result = pd.pivot_table(df, values='values', index=['A', 'B'],
                                  dropna=True)
  
          exp_index = pd.MultiIndex.from_arrays(
              [cat1, cat2],
              names=['A', 'B'])
          expected = DataFrame(
              {'values': [1, 2, 3, 4]},
              index=exp_index)
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize('dropna', [True, False])
      def test_pivot_table_dropna_categoricals(self, dropna):
          # GH 15193
          categories = ['a', 'b', 'c', 'd']
  
          df = DataFrame({'A': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'],
                          'B': [1, 2, 3, 1, 2, 3, 1, 2, 3],
                          'C': range(0, 9)})
  
          df['A'] = df['A'].astype(CDT(categories, ordered=False))
          result = df.pivot_table(index='B', columns='A', values='C',
                                  dropna=dropna)
          expected_columns = Series(['a', 'b', 'c'], name='A')
          expected_columns = expected_columns.astype(
              CDT(categories, ordered=False))
          expected_index = Series([1, 2, 3], name='B')
          expected = DataFrame([[0, 3, 6],
                                [1, 4, 7],
                                [2, 5, 8]],
                               index=expected_index,
                               columns=expected_columns,)
          if not dropna:
              # add back the non observed to compare
              expected = expected.reindex(
                  columns=Categorical(categories)).astype('float')
  
          tm.assert_frame_equal(result, expected)
  
      def test_pass_array(self):
          result = self.data.pivot_table(
              'D', index=self.data.A, columns=self.data.C)
          expected = self.data.pivot_table('D', index='A', columns='C')
          tm.assert_frame_equal(result, expected)
  
      def test_pass_function(self):
          result = self.data.pivot_table('D', index=lambda x: x // 5,
                                         columns=self.data.C)
          expected = self.data.pivot_table('D', index=self.data.index // 5,
                                           columns='C')
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_table_multiple(self):
          index = ['A', 'B']
          columns = 'C'
          table = pivot_table(self.data, index=index, columns=columns)
          expected = self.data.groupby(index + [columns]).agg(np.mean).unstack()
          tm.assert_frame_equal(table, expected)
  
      def test_pivot_dtypes(self):
  
          # can convert dtypes
          f = DataFrame({'a': ['cat', 'bat', 'cat', 'bat'], 'v': [
                        1, 2, 3, 4], 'i': ['a', 'b', 'a', 'b']})
          assert f.dtypes['v'] == 'int64'
  
          z = pivot_table(f, values='v', index=['a'], columns=[
                          'i'], fill_value=0, aggfunc=np.sum)
          result = z.get_dtype_counts()
          expected = Series(dict(int64=2))
          tm.assert_series_equal(result, expected)
  
          # cannot convert dtypes
          f = DataFrame({'a': ['cat', 'bat', 'cat', 'bat'], 'v': [
                        1.5, 2.5, 3.5, 4.5], 'i': ['a', 'b', 'a', 'b']})
          assert f.dtypes['v'] == 'float64'
  
          z = pivot_table(f, values='v', index=['a'], columns=[
                          'i'], fill_value=0, aggfunc=np.mean)
          result = z.get_dtype_counts()
          expected = Series(dict(float64=2))
          tm.assert_series_equal(result, expected)
  
      @pytest.mark.parametrize('columns,values',
                               [('bool1', ['float1', 'float2']),
                                ('bool1', ['float1', 'float2', 'bool1']),
                                ('bool2', ['float1', 'float2', 'bool1'])])
      def test_pivot_preserve_dtypes(self, columns, values):
          # GH 7142 regression test
          v = np.arange(5, dtype=np.float64)
          df = DataFrame({'float1': v, 'float2': v + 2.0,
                          'bool1': v <= 2, 'bool2': v <= 3})
  
          df_res = df.reset_index().pivot_table(
              index='index', columns=columns, values=values)
  
          result = dict(df_res.dtypes)
          expected = {col: np.dtype('O') if col[0].startswith('b')
                      else np.dtype('float64') for col in df_res}
          assert result == expected
  
      def test_pivot_no_values(self):
          # GH 14380
          idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-01-02',
                                  '2011-01-01', '2011-01-02'])
          df = pd.DataFrame({'A': [1, 2, 3, 4, 5]},
                            index=idx)
          res = df.pivot_table(index=df.index.month, columns=df.index.day)
  
          exp_columns = pd.MultiIndex.from_tuples([('A', 1), ('A', 2)])
          exp = pd.DataFrame([[2.5, 4.0], [2.0, np.nan]],
                             index=[1, 2], columns=exp_columns)
          tm.assert_frame_equal(res, exp)
  
          df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
                             'dt': pd.date_range('2011-01-01', freq='D',
                                                 periods=5)},
                            index=idx)
          res = df.pivot_table(index=df.index.month,
                               columns=pd.Grouper(key='dt', freq='M'))
          exp_columns = pd.MultiIndex.from_tuples([('A',
                                                    pd.Timestamp('2011-01-31'))])
          exp_columns.names = [None, 'dt']
          exp = pd.DataFrame([3.25, 2.0],
                             index=[1, 2], columns=exp_columns)
          tm.assert_frame_equal(res, exp)
  
          res = df.pivot_table(index=pd.Grouper(freq='A'),
                               columns=pd.Grouper(key='dt', freq='M'))
          exp = pd.DataFrame([3],
                             index=pd.DatetimeIndex(['2011-12-31']),
                             columns=exp_columns)
          tm.assert_frame_equal(res, exp)
  
      def test_pivot_multi_values(self):
          result = pivot_table(self.data, values=['D', 'E'],
                               index='A', columns=['B', 'C'], fill_value=0)
          expected = pivot_table(self.data.drop(['F'], axis=1),
                                 index='A', columns=['B', 'C'], fill_value=0)
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_multi_functions(self):
          f = lambda func: pivot_table(self.data, values=['D', 'E'],
                                       index=['A', 'B'], columns='C',
                                       aggfunc=func)
          result = f([np.mean, np.std])
          means = f(np.mean)
          stds = f(np.std)
          expected = concat([means, stds], keys=['mean', 'std'], axis=1)
          tm.assert_frame_equal(result, expected)
  
          # margins not supported??
          f = lambda func: pivot_table(self.data, values=['D', 'E'],
                                       index=['A', 'B'], columns='C',
                                       aggfunc=func, margins=True)
          result = f([np.mean, np.std])
          means = f(np.mean)
          stds = f(np.std)
          expected = concat([means, stds], keys=['mean', 'std'], axis=1)
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_index_with_nan(self):
          # GH 3588
          nan = np.nan
          df = DataFrame({'a': ['R1', 'R2', nan, 'R4'],
                          'b': ['C1', 'C2', 'C3', 'C4'],
                          'c': [10, 15, 17, 20]})
          result = df.pivot('a', 'b', 'c')
          expected = DataFrame([[nan, nan, 17, nan], [10, nan, nan, nan],
                                [nan, 15, nan, nan], [nan, nan, nan, 20]],
                               index=Index([nan, 'R1', 'R2', 'R4'], name='a'),
                               columns=Index(['C1', 'C2', 'C3', 'C4'], name='b'))
          tm.assert_frame_equal(result, expected)
          tm.assert_frame_equal(df.pivot('b', 'a', 'c'), expected.T)
  
          # GH9491
          df = DataFrame({'a': pd.date_range('2014-02-01', periods=6, freq='D'),
                          'c': 100 + np.arange(6)})
          df['b'] = df['a'] - pd.Timestamp('2014-02-02')
          df.loc[1, 'a'] = df.loc[3, 'a'] = nan
          df.loc[1, 'b'] = df.loc[4, 'b'] = nan
  
          pv = df.pivot('a', 'b', 'c')
          assert pv.notna().values.sum() == len(df)
  
          for _, row in df.iterrows():
              assert pv.loc[row['a'], row['b']] == row['c']
  
          tm.assert_frame_equal(df.pivot('b', 'a', 'c'), pv.T)
  
      def test_pivot_with_tz(self):
          # GH 5878
          df = DataFrame({'dt1': [datetime(2013, 1, 1, 9, 0),
                                  datetime(2013, 1, 2, 9, 0),
                                  datetime(2013, 1, 1, 9, 0),
                                  datetime(2013, 1, 2, 9, 0)],
                          'dt2': [datetime(2014, 1, 1, 9, 0),
                                  datetime(2014, 1, 1, 9, 0),
                                  datetime(2014, 1, 2, 9, 0),
                                  datetime(2014, 1, 2, 9, 0)],
                          'data1': np.arange(4, dtype='int64'),
                          'data2': np.arange(4, dtype='int64')})
  
          df['dt1'] = df['dt1'].apply(lambda d: pd.Timestamp(d, tz='US/Pacific'))
          df['dt2'] = df['dt2'].apply(lambda d: pd.Timestamp(d, tz='Asia/Tokyo'))
  
          exp_col1 = Index(['data1', 'data1', 'data2', 'data2'])
          exp_col2 = pd.DatetimeIndex(['2014/01/01 09:00',
                                       '2014/01/02 09:00'] * 2,
                                      name='dt2', tz='Asia/Tokyo')
          exp_col = pd.MultiIndex.from_arrays([exp_col1, exp_col2])
          expected = DataFrame([[0, 2, 0, 2], [1, 3, 1, 3]],
                               index=pd.DatetimeIndex(['2013/01/01 09:00',
                                                       '2013/01/02 09:00'],
                                                      name='dt1',
                                                      tz='US/Pacific'),
                               columns=exp_col)
  
          pv = df.pivot(index='dt1', columns='dt2')
          tm.assert_frame_equal(pv, expected)
  
          expected = DataFrame([[0, 2], [1, 3]],
                               index=pd.DatetimeIndex(['2013/01/01 09:00',
                                                       '2013/01/02 09:00'],
                                                      name='dt1',
                                                      tz='US/Pacific'),
                               columns=pd.DatetimeIndex(['2014/01/01 09:00',
                                                         '2014/01/02 09:00'],
                                                        name='dt2',
                                                        tz='Asia/Tokyo'))
  
          pv = df.pivot(index='dt1', columns='dt2', values='data1')
          tm.assert_frame_equal(pv, expected)
  
      def test_pivot_periods(self):
          df = DataFrame({'p1': [pd.Period('2013-01-01', 'D'),
                                 pd.Period('2013-01-02', 'D'),
                                 pd.Period('2013-01-01', 'D'),
                                 pd.Period('2013-01-02', 'D')],
                          'p2': [pd.Period('2013-01', 'M'),
                                 pd.Period('2013-01', 'M'),
                                 pd.Period('2013-02', 'M'),
                                 pd.Period('2013-02', 'M')],
                          'data1': np.arange(4, dtype='int64'),
                          'data2': np.arange(4, dtype='int64')})
  
          exp_col1 = Index(['data1', 'data1', 'data2', 'data2'])
          exp_col2 = pd.PeriodIndex(['2013-01', '2013-02'] * 2,
                                    name='p2', freq='M')
          exp_col = pd.MultiIndex.from_arrays([exp_col1, exp_col2])
          expected = DataFrame([[0, 2, 0, 2], [1, 3, 1, 3]],
                               index=pd.PeriodIndex(['2013-01-01', '2013-01-02'],
                                                    name='p1', freq='D'),
                               columns=exp_col)
  
          pv = df.pivot(index='p1', columns='p2')
          tm.assert_frame_equal(pv, expected)
  
          expected = DataFrame([[0, 2], [1, 3]],
                               index=pd.PeriodIndex(['2013-01-01', '2013-01-02'],
                                                    name='p1', freq='D'),
                               columns=pd.PeriodIndex(['2013-01', '2013-02'],
                                                      name='p2', freq='M'))
  
          pv = df.pivot(index='p1', columns='p2', values='data1')
          tm.assert_frame_equal(pv, expected)
  
      @pytest.mark.parametrize('values', [
          ['baz', 'zoo'], np.array(['baz', 'zoo']),
          pd.Series(['baz', 'zoo']), pd.Index(['baz', 'zoo'])
      ])
      def test_pivot_with_list_like_values(self, values):
          # issue #17160
          df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
                             'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                             'baz': [1, 2, 3, 4, 5, 6],
                             'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
  
          result = df.pivot(index='foo', columns='bar', values=values)
  
          data = [[1, 2, 3, 'x', 'y', 'z'],
                  [4, 5, 6, 'q', 'w', 't']]
          index = Index(data=['one', 'two'], name='foo')
          columns = MultiIndex(levels=[['baz', 'zoo'], ['A', 'B', 'C']],
                               labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
                               names=[None, 'bar'])
          expected = DataFrame(data=data, index=index,
                               columns=columns, dtype='object')
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize('values', [
          ['bar', 'baz'], np.array(['bar', 'baz']),
          pd.Series(['bar', 'baz']), pd.Index(['bar', 'baz'])
      ])
      def test_pivot_with_list_like_values_nans(self, values):
          # issue #17160
          df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
                             'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                             'baz': [1, 2, 3, 4, 5, 6],
                             'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
  
          result = df.pivot(index='zoo', columns='foo', values=values)
  
          data = [[np.nan, 'A', np.nan, 4],
                  [np.nan, 'C', np.nan, 6],
                  [np.nan, 'B', np.nan, 5],
                  ['A', np.nan, 1, np.nan],
                  ['B', np.nan, 2, np.nan],
                  ['C', np.nan, 3, np.nan]]
          index = Index(data=['q', 't', 'w', 'x', 'y', 'z'], name='zoo')
          columns = MultiIndex(levels=[['bar', 'baz'], ['one', 'two']],
                               labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
                               names=[None, 'foo'])
          expected = DataFrame(data=data, index=index,
                               columns=columns, dtype='object')
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.xfail(reason='MultiIndexed unstack with tuple names fails'
                                'with KeyError #19966')
      def test_pivot_with_multiindex(self):
          # issue #17160
          index = Index(data=[0, 1, 2, 3, 4, 5])
          data = [['one', 'A', 1, 'x'],
                  ['one', 'B', 2, 'y'],
                  ['one', 'C', 3, 'z'],
                  ['two', 'A', 4, 'q'],
                  ['two', 'B', 5, 'w'],
                  ['two', 'C', 6, 't']]
          columns = MultiIndex(levels=[['bar', 'baz'], ['first', 'second']],
                               labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
          df = DataFrame(data=data, index=index, columns=columns, dtype='object')
          result = df.pivot(index=('bar', 'first'), columns=('bar', 'second'),
                            values=('baz', 'first'))
  
          data = {'A': Series([1, 4], index=['one', 'two']),
                  'B': Series([2, 5], index=['one', 'two']),
                  'C': Series([3, 6], index=['one', 'two'])}
          expected = DataFrame(data)
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_with_tuple_of_values(self):
          # issue #17160
          df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
                             'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                             'baz': [1, 2, 3, 4, 5, 6],
                             'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
          with pytest.raises(KeyError):
              # tuple is seen as a single column name
              df.pivot(index='zoo', columns='foo', values=('bar', 'baz'))
  
      def test_margins(self):
          def _check_output(result, values_col, index=['A', 'B'],
                            columns=['C'],
                            margins_col='All'):
              col_margins = result.loc[result.index[:-1], margins_col]
              expected_col_margins = self.data.groupby(index)[values_col].mean()
              tm.assert_series_equal(col_margins, expected_col_margins,
                                     check_names=False)
              assert col_margins.name == margins_col
  
              result = result.sort_index()
              index_margins = result.loc[(margins_col, '')].iloc[:-1]
  
              expected_ix_margins = self.data.groupby(columns)[values_col].mean()
              tm.assert_series_equal(index_margins, expected_ix_margins,
                                     check_names=False)
              assert index_margins.name == (margins_col, '')
  
              grand_total_margins = result.loc[(margins_col, ''), margins_col]
              expected_total_margins = self.data[values_col].mean()
              assert grand_total_margins == expected_total_margins
  
          # column specified
          result = self.data.pivot_table(values='D', index=['A', 'B'],
                                         columns='C',
                                         margins=True, aggfunc=np.mean)
          _check_output(result, 'D')
  
          # Set a different margins_name (not 'All')
          result = self.data.pivot_table(values='D', index=['A', 'B'],
                                         columns='C',
                                         margins=True, aggfunc=np.mean,
                                         margins_name='Totals')
          _check_output(result, 'D', margins_col='Totals')
  
          # no column specified
          table = self.data.pivot_table(index=['A', 'B'], columns='C',
                                        margins=True, aggfunc=np.mean)
          for value_col in table.columns.levels[0]:
              _check_output(table[value_col], value_col)
  
          # no col
  
          # to help with a buglet
          self.data.columns = [k * 2 for k in self.data.columns]
          table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
                                        aggfunc=np.mean)
          for value_col in table.columns:
              totals = table.loc[('All', ''), value_col]
              assert totals == self.data[value_col].mean()
  
          # no rows
          rtable = self.data.pivot_table(columns=['AA', 'BB'], margins=True,
                                         aggfunc=np.mean)
          assert isinstance(rtable, Series)
  
          table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
                                        aggfunc='mean')
          for item in ['DD', 'EE', 'FF']:
              totals = table.loc[('All', ''), item]
              assert totals == self.data[item].mean()
  
          # issue number #8349: pivot_table with margins and dictionary aggfunc
          data = [
              {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2013,
               'MONTH': 12, 'DAYS': 3, 'SALARY': 17},
              {'JOB': 'Employ', 'NAME':
               'Mary', 'YEAR': 2013, 'MONTH': 12, 'DAYS': 5, 'SALARY': 23},
              {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
               'MONTH': 1, 'DAYS': 10, 'SALARY': 100},
              {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
               'MONTH': 1, 'DAYS': 11, 'SALARY': 110},
              {'JOB': 'Employ', 'NAME': 'Mary', 'YEAR': 2014,
               'MONTH': 1, 'DAYS': 15, 'SALARY': 200},
              {'JOB': 'Worker', 'NAME': 'Bob', 'YEAR': 2014,
               'MONTH': 2, 'DAYS': 8, 'SALARY': 80},
              {'JOB': 'Employ', 'NAME': 'Mary', 'YEAR': 2014,
               'MONTH': 2, 'DAYS': 5, 'SALARY': 190},
          ]
  
          df = DataFrame(data)
  
          df = df.set_index(['JOB', 'NAME', 'YEAR', 'MONTH'], drop=False,
                            append=False)
  
          with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
              result = df.pivot_table(index=['JOB', 'NAME'],
                                      columns=['YEAR', 'MONTH'],
                                      values=['DAYS', 'SALARY'],
                                      aggfunc={'DAYS': 'mean', 'SALARY': 'sum'},
                                      margins=True)
  
          with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
              expected = df.pivot_table(index=['JOB', 'NAME'],
                                        columns=['YEAR', 'MONTH'],
                                        values=['DAYS'],
                                        aggfunc='mean', margins=True)
  
          tm.assert_frame_equal(result['DAYS'], expected['DAYS'])
  
          with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
              expected = df.pivot_table(index=['JOB', 'NAME'],
                                        columns=['YEAR', 'MONTH'],
                                        values=['SALARY'],
                                        aggfunc='sum', margins=True)
  
          tm.assert_frame_equal(result['SALARY'], expected['SALARY'])
  
      def test_margins_dtype(self):
          # GH 17013
  
          df = self.data.copy()
          df[['D', 'E', 'F']] = np.arange(len(df) * 3).reshape(len(df), 3)
  
          mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')]
          mi = MultiIndex.from_tuples(mi_val, names=('A', 'B'))
          expected = DataFrame({'dull': [12, 21, 3, 9, 45],
                                'shiny': [33, 0, 36, 51, 120]},
                               index=mi).rename_axis('C', axis=1)
          expected['All'] = expected['dull'] + expected['shiny']
  
          result = df.pivot_table(values='D', index=['A', 'B'],
                                         columns='C', margins=True,
                                         aggfunc=np.sum, fill_value=0)
  
          tm.assert_frame_equal(expected, result)
  
      @pytest.mark.xfail(reason='GH 17035 (len of floats is casted back to '
                                'floats)')
      def test_margins_dtype_len(self):
          mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')]
          mi = MultiIndex.from_tuples(mi_val, names=('A', 'B'))
          expected = DataFrame({'dull': [1, 1, 2, 1, 5],
                                'shiny': [2, 0, 2, 2, 6]},
                               index=mi).rename_axis('C', axis=1)
          expected['All'] = expected['dull'] + expected['shiny']
  
          result = self.data.pivot_table(values='D', index=['A', 'B'],
                                         columns='C', margins=True,
                                         aggfunc=len, fill_value=0)
  
          tm.assert_frame_equal(expected, result)
  
      def test_pivot_integer_columns(self):
          # caused by upstream bug in unstack
  
          d = date.min
          data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
                              [d + timedelta(i)
                               for i in range(20)], [1.0]))
          df = DataFrame(data)
          table = df.pivot_table(values=4, index=[0, 1, 3], columns=[2])
  
          df2 = df.rename(columns=str)
          table2 = df2.pivot_table(
              values='4', index=['0', '1', '3'], columns=['2'])
  
          tm.assert_frame_equal(table, table2, check_names=False)
  
      def test_pivot_no_level_overlap(self):
          # GH #1181
  
          data = DataFrame({'a': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'] * 2,
                            'b': [0, 0, 0, 0, 1, 1, 1, 1] * 2,
                            'c': (['foo'] * 4 + ['bar'] * 4) * 2,
                            'value': np.random.randn(16)})
  
          table = data.pivot_table('value', index='a', columns=['b', 'c'])
  
          grouped = data.groupby(['a', 'b', 'c'])['value'].mean()
          expected = grouped.unstack('b').unstack('c').dropna(axis=1, how='all')
          tm.assert_frame_equal(table, expected)
  
      def test_pivot_columns_lexsorted(self):
  
          n = 10000
  
          dtype = np.dtype([
              ("Index", object),
              ("Symbol", object),
              ("Year", int),
              ("Month", int),
              ("Day", int),
              ("Quantity", int),
              ("Price", float),
          ])
  
          products = np.array([
              ('SP500', 'ADBE'),
              ('SP500', 'NVDA'),
              ('SP500', 'ORCL'),
              ('NDQ100', 'AAPL'),
              ('NDQ100', 'MSFT'),
              ('NDQ100', 'GOOG'),
              ('FTSE', 'DGE.L'),
              ('FTSE', 'TSCO.L'),
              ('FTSE', 'GSK.L'),
          ], dtype=[('Index', object), ('Symbol', object)])
          items = np.empty(n, dtype=dtype)
          iproduct = np.random.randint(0, len(products), n)
          items['Index'] = products['Index'][iproduct]
          items['Symbol'] = products['Symbol'][iproduct]
          dr = pd.date_range(date(2000, 1, 1),
                             date(2010, 12, 31))
          dates = dr[np.random.randint(0, len(dr), n)]
          items['Year'] = dates.year
          items['Month'] = dates.month
          items['Day'] = dates.day
          items['Price'] = np.random.lognormal(4.0, 2.0, n)
  
          df = DataFrame(items)
  
          pivoted = df.pivot_table('Price', index=['Month', 'Day'],
                                   columns=['Index', 'Symbol', 'Year'],
                                   aggfunc='mean')
  
          assert pivoted.columns.is_monotonic
  
      def test_pivot_complex_aggfunc(self):
          f = OrderedDict([('D', ['std']), ('E', ['sum'])])
          expected = self.data.groupby(['A', 'B']).agg(f).unstack('B')
          result = self.data.pivot_table(index='A', columns='B', aggfunc=f)
  
          tm.assert_frame_equal(result, expected)
  
      def test_margins_no_values_no_cols(self):
          # Regression test on pivot table: no values or cols passed.
          result = self.data[['A', 'B']].pivot_table(
              index=['A', 'B'], aggfunc=len, margins=True)
          result_list = result.tolist()
          assert sum(result_list[:-1]) == result_list[-1]
  
      def test_margins_no_values_two_rows(self):
          # Regression test on pivot table: no values passed but rows are a
          # multi-index
          result = self.data[['A', 'B', 'C']].pivot_table(
              index=['A', 'B'], columns='C', aggfunc=len, margins=True)
          assert result.All.tolist() == [3.0, 1.0, 4.0, 3.0, 11.0]
  
      def test_margins_no_values_one_row_one_col(self):
          # Regression test on pivot table: no values passed but row and col
          # defined
          result = self.data[['A', 'B']].pivot_table(
              index='A', columns='B', aggfunc=len, margins=True)
          assert result.All.tolist() == [4.0, 7.0, 11.0]
  
      def test_margins_no_values_two_row_two_cols(self):
          # Regression test on pivot table: no values passed but rows and cols
          # are multi-indexed
          self.data['D'] = ['a', 'b', 'c', 'd',
                            'e', 'f', 'g', 'h', 'i', 'j', 'k']
          result = self.data[['A', 'B', 'C', 'D']].pivot_table(
              index=['A', 'B'], columns=['C', 'D'], aggfunc=len, margins=True)
          assert result.All.tolist() == [3.0, 1.0, 4.0, 3.0, 11.0]
  
      def test_pivot_table_with_margins_set_margin_name(self):
          # see gh-3335
          for margin_name in ['foo', 'one', 666, None, ['a', 'b']]:
              with pytest.raises(ValueError):
                  # multi-index index
                  pivot_table(self.data, values='D', index=['A', 'B'],
                              columns=['C'], margins=True,
                              margins_name=margin_name)
              with pytest.raises(ValueError):
                  # multi-index column
                  pivot_table(self.data, values='D', index=['C'],
                              columns=['A', 'B'], margins=True,
                              margins_name=margin_name)
              with pytest.raises(ValueError):
                  # non-multi-index index/column
                  pivot_table(self.data, values='D', index=['A'],
                              columns=['B'], margins=True,
                              margins_name=margin_name)
  
      def test_pivot_timegrouper(self):
          df = DataFrame({
              'Branch': 'A A A A A A A B'.split(),
              'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
              'Quantity': [1, 3, 5, 1, 8, 1, 9, 3],
              'Date': [datetime(2013, 1, 1),
                       datetime(2013, 1, 1),
                       datetime(2013, 10, 1),
                       datetime(2013, 10, 2),
                       datetime(2013, 10, 1),
                       datetime(2013, 10, 2),
                       datetime(2013, 12, 2),
                       datetime(2013, 12, 2), ]}).set_index('Date')
  
          expected = DataFrame(np.array([10, 18, 3], dtype='int64')
                               .reshape(1, 3),
                               index=[datetime(2013, 12, 31)],
                               columns='Carl Joe Mark'.split())
          expected.index.name = 'Date'
          expected.columns.name = 'Buyer'
  
          result = pivot_table(df, index=Grouper(freq='A'), columns='Buyer',
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index='Buyer', columns=Grouper(freq='A'),
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
          expected = DataFrame(np.array([1, np.nan, 3, 9, 18, np.nan])
                               .reshape(2, 3),
                               index=[datetime(2013, 1, 1),
                                      datetime(2013, 7, 1)],
                               columns='Carl Joe Mark'.split())
          expected.index.name = 'Date'
          expected.columns.name = 'Buyer'
  
          result = pivot_table(df, index=Grouper(freq='6MS'), columns='Buyer',
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index='Buyer', columns=Grouper(freq='6MS'),
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
          # passing the name
          df = df.reset_index()
          result = pivot_table(df, index=Grouper(freq='6MS', key='Date'),
                               columns='Buyer',
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index='Buyer',
                               columns=Grouper(freq='6MS', key='Date'),
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
          pytest.raises(KeyError, lambda: pivot_table(
              df, index=Grouper(freq='6MS', key='foo'),
              columns='Buyer', values='Quantity', aggfunc=np.sum))
          pytest.raises(KeyError, lambda: pivot_table(
              df, index='Buyer',
              columns=Grouper(freq='6MS', key='foo'),
              values='Quantity', aggfunc=np.sum))
  
          # passing the level
          df = df.set_index('Date')
          result = pivot_table(df, index=Grouper(freq='6MS', level='Date'),
                               columns='Buyer', values='Quantity',
                               aggfunc=np.sum)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index='Buyer',
                               columns=Grouper(freq='6MS', level='Date'),
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
          pytest.raises(ValueError, lambda: pivot_table(
              df, index=Grouper(freq='6MS', level='foo'),
              columns='Buyer', values='Quantity', aggfunc=np.sum))
          pytest.raises(ValueError, lambda: pivot_table(
              df, index='Buyer',
              columns=Grouper(freq='6MS', level='foo'),
              values='Quantity', aggfunc=np.sum))
  
          # double grouper
          df = DataFrame({
              'Branch': 'A A A A A A A B'.split(),
              'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
              'Quantity': [1, 3, 5, 1, 8, 1, 9, 3],
              'Date': [datetime(2013, 11, 1, 13, 0), datetime(2013, 9, 1, 13, 5),
                       datetime(2013, 10, 1, 20, 0),
                       datetime(2013, 10, 2, 10, 0),
                       datetime(2013, 11, 1, 20, 0),
                       datetime(2013, 10, 2, 10, 0),
                       datetime(2013, 10, 2, 12, 0),
                       datetime(2013, 12, 5, 14, 0)],
              'PayDay': [datetime(2013, 10, 4, 0, 0),
                         datetime(2013, 10, 15, 13, 5),
                         datetime(2013, 9, 5, 20, 0),
                         datetime(2013, 11, 2, 10, 0),
                         datetime(2013, 10, 7, 20, 0),
                         datetime(2013, 9, 5, 10, 0),
                         datetime(2013, 12, 30, 12, 0),
                         datetime(2013, 11, 20, 14, 0), ]})
  
          result = pivot_table(df, index=Grouper(freq='M', key='Date'),
                               columns=Grouper(freq='M', key='PayDay'),
                               values='Quantity', aggfunc=np.sum)
          expected = DataFrame(np.array([np.nan, 3, np.nan, np.nan,
                                         6, np.nan, 1, 9,
                                         np.nan, 9, np.nan, np.nan, np.nan,
                                         np.nan, 3, np.nan]).reshape(4, 4),
                               index=[datetime(2013, 9, 30),
                                      datetime(2013, 10, 31),
                                      datetime(2013, 11, 30),
                                      datetime(2013, 12, 31)],
                               columns=[datetime(2013, 9, 30),
                                        datetime(2013, 10, 31),
                                        datetime(2013, 11, 30),
                                        datetime(2013, 12, 31)])
          expected.index.name = 'Date'
          expected.columns.name = 'PayDay'
  
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index=Grouper(freq='M', key='PayDay'),
                               columns=Grouper(freq='M', key='Date'),
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
          tuples = [(datetime(2013, 9, 30), datetime(2013, 10, 31)),
                    (datetime(2013, 10, 31),
                     datetime(2013, 9, 30)),
                    (datetime(2013, 10, 31),
                     datetime(2013, 11, 30)),
                    (datetime(2013, 10, 31),
                     datetime(2013, 12, 31)),
                    (datetime(2013, 11, 30),
                     datetime(2013, 10, 31)),
                    (datetime(2013, 12, 31), datetime(2013, 11, 30)), ]
          idx = MultiIndex.from_tuples(tuples, names=['Date', 'PayDay'])
          expected = DataFrame(np.array([3, np.nan, 6, np.nan, 1, np.nan,
                                         9, np.nan, 9, np.nan,
                                         np.nan, 3]).reshape(6, 2),
                               index=idx, columns=['A', 'B'])
          expected.columns.name = 'Branch'
  
          result = pivot_table(
              df, index=[Grouper(freq='M', key='Date'),
                         Grouper(freq='M', key='PayDay')], columns=['Branch'],
              values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index=['Branch'],
                               columns=[Grouper(freq='M', key='Date'),
                                        Grouper(freq='M', key='PayDay')],
                               values='Quantity', aggfunc=np.sum)
          tm.assert_frame_equal(result, expected.T)
  
      def test_pivot_datetime_tz(self):
          dates1 = ['2011-07-19 07:00:00', '2011-07-19 08:00:00',
                    '2011-07-19 09:00:00',
                    '2011-07-19 07:00:00', '2011-07-19 08:00:00',
                    '2011-07-19 09:00:00']
          dates2 = ['2013-01-01 15:00:00', '2013-01-01 15:00:00',
                    '2013-01-01 15:00:00',
                    '2013-02-01 15:00:00', '2013-02-01 15:00:00',
                    '2013-02-01 15:00:00']
          df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'],
                          'dt1': dates1, 'dt2': dates2,
                          'value1': np.arange(6, dtype='int64'),
                          'value2': [1, 2] * 3})
          df['dt1'] = df['dt1'].apply(lambda d: pd.Timestamp(d, tz='US/Pacific'))
          df['dt2'] = df['dt2'].apply(lambda d: pd.Timestamp(d, tz='Asia/Tokyo'))
  
          exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00',
                                      '2011-07-19 08:00:00',
                                      '2011-07-19 09:00:00'],
                                     tz='US/Pacific', name='dt1')
          exp_col1 = Index(['value1', 'value1'])
          exp_col2 = Index(['a', 'b'], name='label')
          exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
          expected = DataFrame([[0, 3], [1, 4], [2, 5]],
                               index=exp_idx, columns=exp_col)
          result = pivot_table(df, index=['dt1'], columns=[
                               'label'], values=['value1'])
          tm.assert_frame_equal(result, expected)
  
          exp_col1 = Index(['sum', 'sum', 'sum', 'sum',
                            'mean', 'mean', 'mean', 'mean'])
          exp_col2 = Index(['value1', 'value1', 'value2', 'value2'] * 2)
          exp_col3 = pd.DatetimeIndex(['2013-01-01 15:00:00',
                                       '2013-02-01 15:00:00'] * 4,
                                      tz='Asia/Tokyo', name='dt2')
          exp_col = MultiIndex.from_arrays([exp_col1, exp_col2, exp_col3])
          expected = DataFrame(np.array([[0, 3, 1, 2, 0, 3, 1, 2],
                                         [1, 4, 2, 1, 1, 4, 2, 1],
                                         [2, 5, 1, 2, 2, 5, 1, 2]],
                                        dtype='int64'),
                               index=exp_idx,
                               columns=exp_col)
  
          result = pivot_table(df, index=['dt1'], columns=['dt2'],
                               values=['value1', 'value2'],
                               aggfunc=[np.sum, np.mean])
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_dtaccessor(self):
          # GH 8103
          dates1 = ['2011-07-19 07:00:00', '2011-07-19 08:00:00',
                    '2011-07-19 09:00:00',
                    '2011-07-19 07:00:00', '2011-07-19 08:00:00',
                    '2011-07-19 09:00:00']
          dates2 = ['2013-01-01 15:00:00', '2013-01-01 15:00:00',
                    '2013-01-01 15:00:00',
                    '2013-02-01 15:00:00', '2013-02-01 15:00:00',
                    '2013-02-01 15:00:00']
          df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'],
                          'dt1': dates1, 'dt2': dates2,
                          'value1': np.arange(6, dtype='int64'),
                          'value2': [1, 2] * 3})
          df['dt1'] = df['dt1'].apply(lambda d: pd.Timestamp(d))
          df['dt2'] = df['dt2'].apply(lambda d: pd.Timestamp(d))
  
          result = pivot_table(df, index='label', columns=df['dt1'].dt.hour,
                               values='value1')
  
          exp_idx = Index(['a', 'b'], name='label')
          expected = DataFrame({7: [0, 3], 8: [1, 4], 9: [2, 5]},
                               index=exp_idx,
                               columns=Index([7, 8, 9], name='dt1'))
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index=df['dt2'].dt.month,
                               columns=df['dt1'].dt.hour,
                               values='value1')
  
          expected = DataFrame({7: [0, 3], 8: [1, 4], 9: [2, 5]},
                               index=Index([1, 2], name='dt2'),
                               columns=Index([7, 8, 9], name='dt1'))
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index=df['dt2'].dt.year.values,
                               columns=[df['dt1'].dt.hour, df['dt2'].dt.month],
                               values='value1')
  
          exp_col = MultiIndex.from_arrays(
              [[7, 7, 8, 8, 9, 9], [1, 2] * 3], names=['dt1', 'dt2'])
          expected = DataFrame(np.array([[0, 3, 1, 4, 2, 5]], dtype='int64'),
                               index=[2013], columns=exp_col)
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(df, index=np.array(['X', 'X', 'X',
                                                   'X', 'Y', 'Y']),
                               columns=[df['dt1'].dt.hour, df['dt2'].dt.month],
                               values='value1')
          expected = DataFrame(np.array([[0, 3, 1, np.nan, 2, np.nan],
                                         [np.nan, np.nan, np.nan,
                                          4, np.nan, 5]]),
                               index=['X', 'Y'], columns=exp_col)
          tm.assert_frame_equal(result, expected)
  
      def test_daily(self):
          rng = date_range('1/1/2000', '12/31/2004', freq='D')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          annual = pivot_table(DataFrame(ts), index=ts.index.year,
                               columns=ts.index.dayofyear)
          annual.columns = annual.columns.droplevel(0)
  
          doy = np.asarray(ts.index.dayofyear)
  
          for i in range(1, 367):
              subset = ts[doy == i]
              subset.index = subset.index.year
  
              result = annual[i].dropna()
              tm.assert_series_equal(result, subset, check_names=False)
              assert result.name == i
  
      def test_monthly(self):
          rng = date_range('1/1/2000', '12/31/2004', freq='M')
          ts = Series(np.random.randn(len(rng)), index=rng)
  
          annual = pivot_table(pd.DataFrame(ts), index=ts.index.year,
                               columns=ts.index.month)
          annual.columns = annual.columns.droplevel(0)
  
          month = ts.index.month
          for i in range(1, 13):
              subset = ts[month == i]
              subset.index = subset.index.year
              result = annual[i].dropna()
              tm.assert_series_equal(result, subset, check_names=False)
              assert result.name == i
  
      def test_pivot_table_with_iterator_values(self):
          # GH 12017
          aggs = {'D': 'sum', 'E': 'mean'}
  
          pivot_values_list = pd.pivot_table(
              self.data, index=['A'], values=list(aggs.keys()), aggfunc=aggs,
          )
  
          pivot_values_keys = pd.pivot_table(
              self.data, index=['A'], values=aggs.keys(), aggfunc=aggs,
          )
          tm.assert_frame_equal(pivot_values_keys, pivot_values_list)
  
          agg_values_gen = (value for value in aggs.keys())
          pivot_values_gen = pd.pivot_table(
              self.data, index=['A'], values=agg_values_gen, aggfunc=aggs,
          )
          tm.assert_frame_equal(pivot_values_gen, pivot_values_list)
  
      def test_pivot_table_margins_name_with_aggfunc_list(self):
          # GH 13354
          margins_name = 'Weekly'
          costs = pd.DataFrame(
              {'item': ['bacon', 'cheese', 'bacon', 'cheese'],
               'cost': [2.5, 4.5, 3.2, 3.3],
               'day': ['M', 'M', 'T', 'T']}
          )
          table = costs.pivot_table(
              index="item", columns="day", margins=True,
              margins_name=margins_name, aggfunc=[np.mean, max]
          )
          ix = pd.Index(
              ['bacon', 'cheese', margins_name], dtype='object', name='item'
          )
          tups = [('mean', 'cost', 'M'), ('mean', 'cost', 'T'),
                  ('mean', 'cost', margins_name), ('max', 'cost', 'M'),
                  ('max', 'cost', 'T'), ('max', 'cost', margins_name)]
          cols = pd.MultiIndex.from_tuples(tups, names=[None, None, 'day'])
          expected = pd.DataFrame(table.values, index=ix, columns=cols)
          tm.assert_frame_equal(table, expected)
  
      @pytest.mark.xfail(reason='GH 17035 (np.mean of ints is casted back to '
                                'ints)')
      def test_categorical_margins(self, observed):
          # GH 10989
          df = pd.DataFrame({'x': np.arange(8),
                             'y': np.arange(8) // 4,
                             'z': np.arange(8) % 2})
  
          expected = pd.DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]])
          expected.index = Index([0, 1, 'All'], name='y')
          expected.columns = Index([0, 1, 'All'], name='z')
  
          table = df.pivot_table('x', 'y', 'z', dropna=observed, margins=True)
          tm.assert_frame_equal(table, expected)
  
      @pytest.mark.xfail(reason='GH 17035 (np.mean of ints is casted back to '
                                'ints)')
      def test_categorical_margins_category(self, observed):
          df = pd.DataFrame({'x': np.arange(8),
                             'y': np.arange(8) // 4,
                             'z': np.arange(8) % 2})
  
          expected = pd.DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]])
          expected.index = Index([0, 1, 'All'], name='y')
          expected.columns = Index([0, 1, 'All'], name='z')
  
          df.y = df.y.astype('category')
          df.z = df.z.astype('category')
          table = df.pivot_table('x', 'y', 'z', dropna=observed, margins=True)
          tm.assert_frame_equal(table, expected)
  
      def test_categorical_aggfunc(self, observed):
          # GH 9534
          df = pd.DataFrame({"C1": ["A", "B", "C", "C"],
                             "C2": ["a", "a", "b", "b"],
                             "V": [1, 2, 3, 4]})
          df["C1"] = df["C1"].astype("category")
          result = df.pivot_table("V", index="C1", columns="C2",
                                  dropna=observed, aggfunc="count")
  
          expected_index = pd.CategoricalIndex(['A', 'B', 'C'],
                                               categories=['A', 'B', 'C'],
                                               ordered=False,
                                               name='C1')
          expected_columns = pd.Index(['a', 'b'], name='C2')
          expected_data = np.array([[1., np.nan],
                                    [1., np.nan],
                                    [np.nan, 2.]])
          expected = pd.DataFrame(expected_data,
                                  index=expected_index,
                                  columns=expected_columns)
          tm.assert_frame_equal(result, expected)
  
      def test_categorical_pivot_index_ordering(self, observed):
          # GH 8731
          df = pd.DataFrame({'Sales': [100, 120, 220],
                             'Month': ['January', 'January', 'January'],
                             'Year': [2013, 2014, 2013]})
          months = ['January', 'February', 'March', 'April', 'May', 'June',
                    'July', 'August', 'September', 'October', 'November',
                    'December']
          df['Month'] = df['Month'].astype('category').cat.set_categories(months)
          result = df.pivot_table(values='Sales',
                                  index='Month',
                                  columns='Year',
                                  dropna=observed,
                                  aggfunc='sum')
          expected_columns = pd.Int64Index([2013, 2014], name='Year')
          expected_index = pd.CategoricalIndex(['January'],
                                               categories=months,
                                               ordered=False,
                                               name='Month')
          expected = pd.DataFrame([[320, 120]],
                                  index=expected_index,
                                  columns=expected_columns)
          if not observed:
              result = result.dropna().astype(np.int64)
  
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_table_not_series(self):
          # GH 4386
          # pivot_table always returns a DataFrame
          # when values is not list like and columns is None
          # and aggfunc is not instance of list
          df = DataFrame({'col1': [3, 4, 5],
                          'col2': ['C', 'D', 'E'],
                          'col3': [1, 3, 9]})
  
          result = df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum)
          m = MultiIndex.from_arrays([[1, 3, 9],
                                      ['C', 'D', 'E']],
                                     names=['col3', 'col2'])
          expected = DataFrame([3, 4, 5],
                               index=m, columns=['col1'])
  
          tm.assert_frame_equal(result, expected)
  
          result = df.pivot_table(
              'col1', index='col3', columns='col2', aggfunc=np.sum
          )
          expected = DataFrame([[3, np.NaN, np.NaN],
                                [np.NaN, 4, np.NaN],
                                [np.NaN, np.NaN, 5]],
                               index=Index([1, 3, 9], name='col3'),
                               columns=Index(['C', 'D', 'E'], name='col2'))
  
          tm.assert_frame_equal(result, expected)
  
          result = df.pivot_table('col1', index='col3', aggfunc=[np.sum])
          m = MultiIndex.from_arrays([['sum'],
                                      ['col1']])
          expected = DataFrame([3, 4, 5],
                               index=Index([1, 3, 9], name='col3'),
                               columns=m)
  
          tm.assert_frame_equal(result, expected)
  
      def test_pivot_margins_name_unicode(self):
          # issue #13292
          greek = u'\u0394\u03bf\u03ba\u03b9\u03bc\u03ae'
          frame = pd.DataFrame({'foo': [1, 2, 3]})
          table = pd.pivot_table(frame, index=['foo'], aggfunc=len, margins=True,
                                 margins_name=greek)
          index = pd.Index([1, 2, 3, greek], dtype='object', name='foo')
          expected = pd.DataFrame(index=index)
          tm.assert_frame_equal(table, expected)
  
      def test_pivot_string_as_func(self):
          # GH #18713
          # for correctness purposes
          data = DataFrame({'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar',
                                  'bar', 'bar', 'foo', 'foo', 'foo'],
                            'B': ['one', 'one', 'one', 'two', 'one', 'one',
                                  'one', 'two', 'two', 'two', 'one'],
                            'C': range(11)})
  
          result = pivot_table(data, index='A', columns='B', aggfunc='sum')
          mi = MultiIndex(levels=[['C'], ['one', 'two']],
                          labels=[[0, 0], [0, 1]], names=[None, 'B'])
          expected = DataFrame({('C', 'one'): {'bar': 15, 'foo': 13},
                                ('C', 'two'): {'bar': 7, 'foo': 20}},
                               columns=mi).rename_axis('A')
          tm.assert_frame_equal(result, expected)
  
          result = pivot_table(data, index='A', columns='B',
                               aggfunc=['sum', 'mean'])
          mi = MultiIndex(levels=[['sum', 'mean'], ['C'], ['one', 'two']],
                          labels=[[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 1]],
                          names=[None, None, 'B'])
          expected = DataFrame({('mean', 'C', 'one'): {'bar': 5.0, 'foo': 3.25},
                                ('mean', 'C', 'two'): {'bar': 7.0,
                                                       'foo': 6.666666666666667},
                                ('sum', 'C', 'one'): {'bar': 15, 'foo': 13},
                                ('sum', 'C', 'two'): {'bar': 7, 'foo': 20}},
                               columns=mi).rename_axis('A')
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize('f, f_numpy',
                               [('sum', np.sum),
                                ('mean', np.mean),
                                ('std', np.std),
                                (['sum', 'mean'], [np.sum, np.mean]),
                                (['sum', 'std'], [np.sum, np.std]),
                                (['std', 'mean'], [np.std, np.mean])])
      def test_pivot_string_func_vs_func(self, f, f_numpy):
          # GH #18713
          # for consistency purposes
          result = pivot_table(self.data, index='A', columns='B', aggfunc=f)
          expected = pivot_table(self.data, index='A', columns='B',
                                 aggfunc=f_numpy)
          tm.assert_frame_equal(result, expected)
  
  
  class TestCrosstab(object):
  
      def setup_method(self, method):
          df = DataFrame({'A': ['foo', 'foo', 'foo', 'foo',
                                'bar', 'bar', 'bar', 'bar',
                                'foo', 'foo', 'foo'],
                          'B': ['one', 'one', 'one', 'two',
                                'one', 'one', 'one', 'two',
                                'two', 'two', 'one'],
                          'C': ['dull', 'dull', 'shiny', 'dull',
                                'dull', 'shiny', 'shiny', 'dull',
                                'shiny', 'shiny', 'shiny'],
                          'D': np.random.randn(11),
                          'E': np.random.randn(11),
                          'F': np.random.randn(11)})
  
          self.df = df.append(df, ignore_index=True)
  
      def test_crosstab_single(self):
          df = self.df
          result = crosstab(df['A'], df['C'])
          expected = df.groupby(['A', 'C']).size().unstack()
          tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
  
      def test_crosstab_multiple(self):
          df = self.df
  
          result = crosstab(df['A'], [df['B'], df['C']])
          expected = df.groupby(['A', 'B', 'C']).size()
          expected = expected.unstack(
              'B').unstack('C').fillna(0).astype(np.int64)
          tm.assert_frame_equal(result, expected)
  
          result = crosstab([df['B'], df['C']], df['A'])
          expected = df.groupby(['B', 'C', 'A']).size()
          expected = expected.unstack('A').fillna(0).astype(np.int64)
          tm.assert_frame_equal(result, expected)
  
      def test_crosstab_ndarray(self):
          a = np.random.randint(0, 5, size=100)
          b = np.random.randint(0, 3, size=100)
          c = np.random.randint(0, 10, size=100)
  
          df = DataFrame({'a': a, 'b': b, 'c': c})
  
          result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'))
          expected = crosstab(df['a'], [df['b'], df['c']])
          tm.assert_frame_equal(result, expected)
  
          result = crosstab([b, c], a, colnames=['a'], rownames=('b', 'c'))
          expected = crosstab([df['b'], df['c']], df['a'])
          tm.assert_frame_equal(result, expected)
  
          # assign arbitrary names
          result = crosstab(self.df['A'].values, self.df['C'].values)
          assert result.index.name == 'row_0'
          assert result.columns.name == 'col_0'
  
      def test_crosstab_non_aligned(self):
          # GH 17005
          a = pd.Series([0, 1, 1], index=['a', 'b', 'c'])
          b = pd.Series([3, 4, 3, 4, 3], index=['a', 'b', 'c', 'd', 'f'])
          c = np.array([3, 4, 3])
  
          expected = pd.DataFrame([[1, 0], [1, 1]],
                                  index=Index([0, 1], name='row_0'),
                                  columns=Index([3, 4], name='col_0'))
  
          result = crosstab(a, b)
          tm.assert_frame_equal(result, expected)
  
          result = crosstab(a, c)
          tm.assert_frame_equal(result, expected)
  
      def test_crosstab_margins(self):
          a = np.random.randint(0, 7, size=100)
          b = np.random.randint(0, 3, size=100)
          c = np.random.randint(0, 5, size=100)
  
          df = DataFrame({'a': a, 'b': b, 'c': c})
  
          result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
                            margins=True)
  
          assert result.index.names == ('a',)
          assert result.columns.names == ['b', 'c']
  
          all_cols = result['All', '']
          exp_cols = df.groupby(['a']).size().astype('i8')
          # to keep index.name
          exp_margin = Series([len(df)], index=Index(['All'], name='a'))
          exp_cols = exp_cols.append(exp_margin)
          exp_cols.name = ('All', '')
  
          tm.assert_series_equal(all_cols, exp_cols)
  
          all_rows = result.loc['All']
          exp_rows = df.groupby(['b', 'c']).size().astype('i8')
          exp_rows = exp_rows.append(Series([len(df)], index=[('All', '')]))
          exp_rows.name = 'All'
  
          exp_rows = exp_rows.reindex(all_rows.index)
          exp_rows = exp_rows.fillna(0).astype(np.int64)
          tm.assert_series_equal(all_rows, exp_rows)
  
      def test_crosstab_margins_set_margin_name(self):
          # GH 15972
          a = np.random.randint(0, 7, size=100)
          b = np.random.randint(0, 3, size=100)
          c = np.random.randint(0, 5, size=100)
  
          df = DataFrame({'a': a, 'b': b, 'c': c})
  
          result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
                            margins=True, margins_name='TOTAL')
  
          assert result.index.names == ('a',)
          assert result.columns.names == ['b', 'c']
  
          all_cols = result['TOTAL', '']
          exp_cols = df.groupby(['a']).size().astype('i8')
          # to keep index.name
          exp_margin = Series([len(df)], index=Index(['TOTAL'], name='a'))
          exp_cols = exp_cols.append(exp_margin)
          exp_cols.name = ('TOTAL', '')
  
          tm.assert_series_equal(all_cols, exp_cols)
  
          all_rows = result.loc['TOTAL']
          exp_rows = df.groupby(['b', 'c']).size().astype('i8')
          exp_rows = exp_rows.append(Series([len(df)], index=[('TOTAL', '')]))
          exp_rows.name = 'TOTAL'
  
          exp_rows = exp_rows.reindex(all_rows.index)
          exp_rows = exp_rows.fillna(0).astype(np.int64)
          tm.assert_series_equal(all_rows, exp_rows)
  
          for margins_name in [666, None, ['a', 'b']]:
              with pytest.raises(ValueError):
                  crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
                           margins=True, margins_name=margins_name)
  
      def test_crosstab_pass_values(self):
          a = np.random.randint(0, 7, size=100)
          b = np.random.randint(0, 3, size=100)
          c = np.random.randint(0, 5, size=100)
          values = np.random.randn(100)
  
          table = crosstab([a, b], c, values, aggfunc=np.sum,
                           rownames=['foo', 'bar'], colnames=['baz'])
  
          df = DataFrame({'foo': a, 'bar': b, 'baz': c, 'values': values})
  
          expected = df.pivot_table('values', index=['foo', 'bar'],
                                    columns='baz', aggfunc=np.sum)
          tm.assert_frame_equal(table, expected)
  
      def test_crosstab_dropna(self):
          # GH 3820
          a = np.array(['foo', 'foo', 'foo', 'bar',
                        'bar', 'foo', 'foo'], dtype=object)
          b = np.array(['one', 'one', 'two', 'one',
                        'two', 'two', 'two'], dtype=object)
          c = np.array(['dull', 'dull', 'dull', 'dull',
                        'dull', 'shiny', 'shiny'], dtype=object)
          res = pd.crosstab(a, [b, c], rownames=['a'],
                            colnames=['b', 'c'], dropna=False)
          m = MultiIndex.from_tuples([('one', 'dull'), ('one', 'shiny'),
                                      ('two', 'dull'), ('two', 'shiny')],
                                     names=['b', 'c'])
          tm.assert_index_equal(res.columns, m)
  
      def test_crosstab_no_overlap(self):
          # GS 10291
  
          s1 = pd.Series([1, 2, 3], index=[1, 2, 3])
          s2 = pd.Series([4, 5, 6], index=[4, 5, 6])
  
          actual = crosstab(s1, s2)
          expected = pd.DataFrame()
  
          tm.assert_frame_equal(actual, expected)
  
      def test_margin_dropna(self):
          # GH 12577
          # pivot_table counts null into margin ('All')
          # when margins=true and dropna=true
  
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan],
                             'b': [3, 3, 4, 4, 4, 4]})
          actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
          expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]])
          expected.index = Index([1.0, 2.0, 'All'], name='a')
          expected.columns = Index([3, 4, 'All'], name='b')
          tm.assert_frame_equal(actual, expected)
  
          df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan],
                          'b': [3, np.nan, 4, 4, 4, 4]})
          actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
          expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
          expected.index = Index([1.0, 2.0, 'All'], name='a')
          expected.columns = Index([3.0, 4.0, 'All'], name='b')
          tm.assert_frame_equal(actual, expected)
  
          df = DataFrame({'a': [1, np.nan, np.nan, np.nan, np.nan, 2],
                          'b': [3, 3, 4, 4, 4, 4]})
          actual = pd.crosstab(df.a, df.b, margins=True, dropna=True)
          expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
          expected.index = Index([1.0, 2.0, 'All'], name='a')
          expected.columns = Index([3, 4, 'All'], name='b')
          tm.assert_frame_equal(actual, expected)
  
          # GH 12642
          # _add_margins raises KeyError: Level None not found
          # when margins=True and dropna=False
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan],
                             'b': [3, 3, 4, 4, 4, 4]})
          actual = pd.crosstab(df.a, df.b, margins=True, dropna=False)
          expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]])
          expected.index = Index([1.0, 2.0, 'All'], name='a')
          expected.columns = Index([3, 4, 'All'], name='b')
          tm.assert_frame_equal(actual, expected)
  
          df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan],
                          'b': [3, np.nan, 4, 4, 4, 4]})
          actual = pd.crosstab(df.a, df.b, margins=True, dropna=False)
          expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]])
          expected.index = Index([1.0, 2.0, 'All'], name='a')
          expected.columns = Index([3.0, 4.0, 'All'], name='b')
          tm.assert_frame_equal(actual, expected)
  
          a = np.array(['foo', 'foo', 'foo', 'bar',
                        'bar', 'foo', 'foo'], dtype=object)
          b = np.array(['one', 'one', 'two', 'one',
                        'two', np.nan, 'two'], dtype=object)
          c = np.array(['dull', 'dull', 'dull', 'dull',
                        'dull', 'shiny', 'shiny'], dtype=object)
  
          actual = pd.crosstab(a, [b, c], rownames=['a'],
                               colnames=['b', 'c'], margins=True, dropna=False)
          m = MultiIndex.from_arrays([['one', 'one', 'two', 'two', 'All'],
                                      ['dull', 'shiny', 'dull', 'shiny', '']],
                                     names=['b', 'c'])
          expected = DataFrame([[1, 0, 1, 0, 2], [2, 0, 1, 1, 5],
                                [3, 0, 2, 1, 7]], columns=m)
          expected.index = Index(['bar', 'foo', 'All'], name='a')
          tm.assert_frame_equal(actual, expected)
  
          actual = pd.crosstab([a, b], c, rownames=['a', 'b'],
                               colnames=['c'], margins=True, dropna=False)
          m = MultiIndex.from_arrays([['bar', 'bar', 'foo', 'foo', 'All'],
                                      ['one', 'two', 'one', 'two', '']],
                                     names=['a', 'b'])
          expected = DataFrame([[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2],
                                [5, 2, 7]], index=m)
          expected.columns = Index(['dull', 'shiny', 'All'], name='c')
          tm.assert_frame_equal(actual, expected)
  
          actual = pd.crosstab([a, b], c, rownames=['a', 'b'],
                               colnames=['c'], margins=True, dropna=True)
          m = MultiIndex.from_arrays([['bar', 'bar', 'foo', 'foo', 'All'],
                                      ['one', 'two', 'one', 'two', '']],
                                     names=['a', 'b'])
          expected = DataFrame([[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2],
                                [5, 1, 6]], index=m)
          expected.columns = Index(['dull', 'shiny', 'All'], name='c')
          tm.assert_frame_equal(actual, expected)
  
      def test_crosstab_normalize(self):
          # Issue 12578
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
                             'c': [1, 1, np.nan, 1, 1]})
  
          rindex = pd.Index([1, 2], name='a')
          cindex = pd.Index([3, 4], name='b')
          full_normal = pd.DataFrame([[0.2, 0], [0.2, 0.6]],
                                     index=rindex, columns=cindex)
          row_normal = pd.DataFrame([[1.0, 0], [0.25, 0.75]],
                                    index=rindex, columns=cindex)
          col_normal = pd.DataFrame([[0.5, 0], [0.5, 1.0]],
                                    index=rindex, columns=cindex)
  
          # Check all normalize args
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='all'),
                                full_normal)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=True),
                                full_normal)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index'),
                                row_normal)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns'),
                                col_normal)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=1),
                                pd.crosstab(df.a, df.b, normalize='columns'))
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=0),
                                pd.crosstab(df.a, df.b, normalize='index'))
  
          row_normal_margins = pd.DataFrame([[1.0, 0],
                                             [0.25, 0.75],
                                             [0.4, 0.6]],
                                            index=pd.Index([1, 2, 'All'],
                                                           name='a',
                                                           dtype='object'),
                                            columns=pd.Index([3, 4], name='b',
                                                             dtype='object'))
          col_normal_margins = pd.DataFrame([[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
                                            index=pd.Index([1, 2], name='a',
                                                           dtype='object'),
                                            columns=pd.Index([3, 4, 'All'],
                                                             name='b',
                                                             dtype='object'))
  
          all_normal_margins = pd.DataFrame([[0.2, 0, 0.2],
                                             [0.2, 0.6, 0.8],
                                             [0.4, 0.6, 1]],
                                            index=pd.Index([1, 2, 'All'],
                                                           name='a',
                                                           dtype='object'),
                                            columns=pd.Index([3, 4, 'All'],
                                                             name='b',
                                                             dtype='object'))
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index',
                                            margins=True), row_normal_margins)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns',
                                            margins=True), col_normal_margins)
          tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=True,
                                            margins=True), all_normal_margins)
  
          # Test arrays
          pd.crosstab([np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])],
                      np.array([1, 2, 1, 2]))
  
          # Test with aggfunc
          norm_counts = pd.DataFrame([[0.25, 0, 0.25],
                                      [0.25, 0.5, 0.75],
                                      [0.5, 0.5, 1]],
                                     index=pd.Index([1, 2, 'All'],
                                                    name='a',
                                                    dtype='object'),
                                     columns=pd.Index([3, 4, 'All'],
                                                      name='b'))
          test_case = pd.crosstab(df.a, df.b, df.c, aggfunc='count',
                                  normalize='all',
                                  margins=True)
          tm.assert_frame_equal(test_case, norm_counts)
  
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
                             'c': [0, 4, np.nan, 3, 3]})
  
          norm_sum = pd.DataFrame([[0, 0, 0.],
                                   [0.4, 0.6, 1],
                                   [0.4, 0.6, 1]],
                                  index=pd.Index([1, 2, 'All'],
                                                 name='a',
                                                 dtype='object'),
                                  columns=pd.Index([3, 4, 'All'],
                                                   name='b',
                                                   dtype='object'))
          test_case = pd.crosstab(df.a, df.b, df.c, aggfunc=np.sum,
                                  normalize='all',
                                  margins=True)
          tm.assert_frame_equal(test_case, norm_sum)
  
      def test_crosstab_with_empties(self):
          # Check handling of empties
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
                             'c': [np.nan, np.nan, np.nan, np.nan, np.nan]})
  
          empty = pd.DataFrame([[0.0, 0.0], [0.0, 0.0]],
                               index=pd.Index([1, 2],
                                              name='a',
                                              dtype='int64'),
                               columns=pd.Index([3, 4], name='b'))
  
          for i in [True, 'index', 'columns']:
              calculated = pd.crosstab(df.a, df.b, values=df.c, aggfunc='count',
                                       normalize=i)
              tm.assert_frame_equal(empty, calculated)
  
          nans = pd.DataFrame([[0.0, np.nan], [0.0, 0.0]],
                              index=pd.Index([1, 2],
                                             name='a',
                                             dtype='int64'),
                              columns=pd.Index([3, 4], name='b'))
  
          calculated = pd.crosstab(df.a, df.b, values=df.c, aggfunc='count',
                                   normalize=False)
          tm.assert_frame_equal(nans, calculated)
  
      def test_crosstab_errors(self):
          # Issue 12578
  
          df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
                             'c': [1, 1, np.nan, 1, 1]})
  
          error = 'values cannot be used without an aggfunc.'
          with tm.assert_raises_regex(ValueError, error):
              pd.crosstab(df.a, df.b, values=df.c)
  
          error = 'aggfunc cannot be used without values'
          with tm.assert_raises_regex(ValueError, error):
              pd.crosstab(df.a, df.b, aggfunc=np.mean)
  
          error = 'Not a valid normalize argument'
          with tm.assert_raises_regex(ValueError, error):
              pd.crosstab(df.a, df.b, normalize='42')
  
          with tm.assert_raises_regex(ValueError, error):
              pd.crosstab(df.a, df.b, normalize=42)
  
          error = 'Not a valid margins argument'
          with tm.assert_raises_regex(ValueError, error):
              pd.crosstab(df.a, df.b, normalize='all', margins=42)
  
      def test_crosstab_with_categorial_columns(self):
          # GH 8860
          df = pd.DataFrame({'MAKE': ['Honda', 'Acura', 'Tesla',
                                      'Honda', 'Honda', 'Acura'],
                             'MODEL': ['Sedan', 'Sedan', 'Electric',
                                       'Pickup', 'Sedan', 'Sedan']})
          categories = ['Sedan', 'Electric', 'Pickup']
          df['MODEL'] = (df['MODEL'].astype('category')
                                    .cat.set_categories(categories))
          result = pd.crosstab(df['MAKE'], df['MODEL'])
  
          expected_index = pd.Index(['Acura', 'Honda', 'Tesla'], name='MAKE')
          expected_columns = pd.CategoricalIndex(categories,
                                                 categories=categories,
                                                 ordered=False,
                                                 name='MODEL')
          expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
          expected = pd.DataFrame(expected_data,
                                  index=expected_index,
                                  columns=expected_columns)
          tm.assert_frame_equal(result, expected)
  
      def test_crosstab_with_numpy_size(self):
          # GH 4003
          df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 6,
                             'B': ['A', 'B', 'C'] * 8,
                             'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
                             'D': np.random.randn(24),
                             'E': np.random.randn(24)})
          result = pd.crosstab(index=[df['A'], df['B']],
                               columns=[df['C']],
                               margins=True,
                               aggfunc=np.size,
                               values=df['D'])
          expected_index = pd.MultiIndex(levels=[['All', 'one', 'three', 'two'],
                                                 ['', 'A', 'B', 'C']],
                                         labels=[[1, 1, 1, 2, 2, 2, 3, 3, 3, 0],
                                                 [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]],
                                         names=['A', 'B'])
          expected_column = pd.Index(['bar', 'foo', 'All'],
                                     dtype='object',
                                     name='C')
          expected_data = np.array([[2., 2., 4.],
                                    [2., 2., 4.],
                                    [2., 2., 4.],
                                    [2., np.nan, 2.],
                                    [np.nan, 2., 2.],
                                    [2., np.nan, 2.],
                                    [np.nan, 2., 2.],
                                    [2., np.nan, 2.],
                                    [np.nan, 2., 2.],
                                    [12., 12., 24.]])
          expected = pd.DataFrame(expected_data,
                                  index=expected_index,
                                  columns=expected_column)
          tm.assert_frame_equal(result, expected)
  
      def test_crosstab_dup_index_names(self):
          # GH 13279, GH 18872
          s = pd.Series(range(3), name='foo')
          pytest.raises(ValueError, pd.crosstab, s, s)
  
      @pytest.mark.parametrize("names", [['a', ('b', 'c')],
                                         [('a', 'b'), 'c']])
      def test_crosstab_tuple_name(self, names):
          s1 = pd.Series(range(3), name=names[0])
          s2 = pd.Series(range(1, 4), name=names[1])
  
          mi = pd.MultiIndex.from_arrays([range(3), range(1, 4)], names=names)
          expected = pd.Series(1, index=mi).unstack(1, fill_value=0)
  
          result = pd.crosstab(s1, s2)
          tm.assert_frame_equal(result, expected)
  
      def test_crosstab_unsorted_order(self):
          df = pd.DataFrame({"b": [3, 1, 2], 'a': [5, 4, 6]},
                            index=['C', 'A', 'B'])
          result = pd.crosstab(df.index, [df.b, df.a])
          e_idx = pd.Index(['A', 'B', 'C'], name='row_0')
          e_columns = pd.MultiIndex.from_tuples([(1, 4), (2, 6), (3, 5)],
                                                names=['b', 'a'])
          expected = pd.DataFrame([[1, 0, 0], [0, 1, 0], [0, 0, 1]],
                                  index=e_idx,
                                  columns=e_columns)
          tm.assert_frame_equal(result, expected)
  