test_pivot.py

# -*- coding: utf-8 -*-

from collections import OrderedDict
from datetime import date, datetime, timedelta

import numpy as np
import pytest

from pandas.compat import product, range

import pandas as pd
from pandas import (
    Categorical, DataFrame, Grouper, Index, MultiIndex, Series, concat,
    date_range)
from pandas.api.types import CategoricalDtype as CDT
from pandas.core.reshape.pivot import crosstab, pivot_table
import pandas.util.testing as tm


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


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)

    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_pivot_with_non_observable_dropna(self, dropna):
        # gh-21133
        df = pd.DataFrame(
            {'A': pd.Categorical([np.nan, 'low', 'high', 'low', 'high'],
                                 categories=['low', 'high'],
                                 ordered=True),
             'B': range(5)})

        result = df.pivot_table(index='A', values='B', dropna=dropna)
        expected = pd.DataFrame(
            {'B': [2, 3]},
            index=pd.Index(
                pd.Categorical.from_codes([0, 1],
                                          categories=['low', 'high'],
                                          ordered=True),
                name='A'))

        tm.assert_frame_equal(result, expected)

        # gh-21378
        df = pd.DataFrame(
            {'A': pd.Categorical(['left', 'low', 'high', 'low', 'high'],
                                 categories=['low', 'high', 'left'],
                                 ordered=True),
             'B': range(5)})

        result = df.pivot_table(index='A', values='B', dropna=dropna)
        expected = pd.DataFrame(
            {'B': [2, 3, 0]},
            index=pd.Index(
                pd.Categorical.from_codes([0, 1, 2],
                                          categories=['low', 'high', 'left'],
                                          ordered=True),
                name='A'))

        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)

    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_index_with_nan(self, method):
        # GH 3588
        nan = np.nan
        df = DataFrame({'a': ['R1', 'R2', nan, 'R4'],
                        'b': ['C1', 'C2', 'C3', 'C4'],
                        'c': [10, 15, 17, 20]})
        if method:
            result = df.pivot('a', 'b', 'c')
        else:
            result = pd.pivot(df, '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

        if method:
            pv = df.pivot('a', 'b', 'c')
        else:
            pv = pd.pivot(df, 'a', 'b', 'c')
        assert pv.notna().values.sum() == len(df)

        for _, row in df.iterrows():
            assert pv.loc[row['a'], row['b']] == row['c']

        if method:
            result = df.pivot('b', 'a', 'c')
        else:
            result = pd.pivot(df, 'b', 'a', 'c')
        tm.assert_frame_equal(result, pv.T)

    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_with_tz(self, method):
        # 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)

        if method:
            pv = df.pivot(index='dt1', columns='dt2')
        else:
            pv = pd.pivot(df, 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'))

        if method:
            pv = df.pivot(index='dt1', columns='dt2', values='data1')
        else:
            pv = pd.pivot(df, index='dt1', columns='dt2', values='data1')
        tm.assert_frame_equal(pv, expected)

    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_periods(self, method):
        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)
        if method:
            pv = df.pivot(index='p1', columns='p2')
        else:
            pv = pd.pivot(df, 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'))
        if method:
            pv = df.pivot(index='p1', columns='p2', values='data1')
        else:
            pv = pd.pivot(df, 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'])
    ])
    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_with_list_like_values(self, values, method):
        # 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']})

        if method:
            result = df.pivot(index='foo', columns='bar', values=values)
        else:
            result = pd.pivot(df, 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']],
                             codes=[[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'])
    ])
    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_with_list_like_values_nans(self, values, method):
        # 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']})

        if method:
            result = df.pivot(index='zoo', columns='foo', values=values)
        else:
            result = pd.pivot(df, 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']],
                             codes=[[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 GH#19966')
    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_with_multiindex(self, method):
        # 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']],
                             codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
        df = DataFrame(data=data, index=index, columns=columns, dtype='object')
        if method:
            result = df.pivot(index=('bar', 'first'),
                              columns=('bar', 'second'),
                              values=('baz', 'first'))
        else:
            result = pd.pivot(df,
                              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)

    @pytest.mark.parametrize('method', [True, False])
    def test_pivot_with_tuple_of_values(self, method):
        # 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, match=r"^\('bar', 'baz'\)$"):
            # tuple is seen as a single column name
            if method:
                df.pivot(index='zoo', columns='foo', values=('bar', 'baz'))
            else:
                pd.pivot(df, 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()

    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]

    @pytest.mark.parametrize(
        'margin_name', ['foo', 'one', 666, None, ['a', 'b']])
    def test_pivot_table_with_margins_set_margin_name(self, margin_name):
        # see gh-3335
        msg = (r'Conflicting name "{}" in margins|'
               "margins_name argument must be a string").format(margin_name)
        with pytest.raises(ValueError, match=msg):
            # multi-index index
            pivot_table(self.data, values='D', index=['A', 'B'],
                        columns=['C'], margins=True,
                        margins_name=margin_name)
        with pytest.raises(ValueError, match=msg):
            # multi-index column
            pivot_table(self.data, values='D', index=['C'],
                        columns=['A', 'B'], margins=True,
                        margins_name=margin_name)
        with pytest.raises(ValueError, match=msg):
            # 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)

        msg = "'The grouper name foo is not found'"
        with pytest.raises(KeyError, match=msg):
            pivot_table(df, index=Grouper(freq='6MS', key='foo'),
                        columns='Buyer', values='Quantity', aggfunc=np.sum)
        with pytest.raises(KeyError, match=msg):
            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)

        msg = "The level foo is not valid"
        with pytest.raises(ValueError, match=msg):
            pivot_table(df, index=Grouper(freq='6MS', level='foo'),
                        columns='Buyer', values='Quantity', aggfunc=np.sum)
        with pytest.raises(ValueError, match=msg):
            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']],
                        codes=[[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']],
                        codes=[[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)

    @pytest.mark.slow
    def test_pivot_number_of_levels_larger_than_int32(self):
        # GH 20601
        df = DataFrame({'ind1': np.arange(2 ** 16),
                        'ind2': np.arange(2 ** 16),
                        'count': 0})

        msg = "Unstacked DataFrame is too big, causing int32 overflow"
        with pytest.raises(ValueError, match=msg):
            df.pivot_table(index='ind1', columns='ind2',
                           values='count', aggfunc='count')


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)

        msg = "margins_name argument must be a string"
        for margins_name in [666, None, ['a', 'b']]:
            with pytest.raises(ValueError, match=msg):
                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 pytest.raises(ValueError, match=error):
            pd.crosstab(df.a, df.b, values=df.c)

        error = 'aggfunc cannot be used without values'
        with pytest.raises(ValueError, match=error):
            pd.crosstab(df.a, df.b, aggfunc=np.mean)

        error = 'Not a valid normalize argument'
        with pytest.raises(ValueError, match=error):
            pd.crosstab(df.a, df.b, normalize='42')

        with pytest.raises(ValueError, match=error):
            pd.crosstab(df.a, df.b, normalize=42)

        error = 'Not a valid margins argument'
        with pytest.raises(ValueError, match=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']],
                                       codes=[[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
        s = pd.Series(range(3), name='foo')

        result = pd.crosstab(s, s)
        expected_index = pd.Index(range(3), name='foo')
        expected = pd.DataFrame(np.eye(3, dtype=np.int64),
                                index=expected_index,
                                columns=expected_index)
        tm.assert_frame_equal(result, expected)

    @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)