import datetime

import numpy as np
from numpy.testing import assert_equal

import pandas as pd
from pandas import DataFrame, Series, Index, MultiIndex, Grouper
from pandas.tools.merge import concat
from pandas.tools.pivot import pivot_table, crosstab
from pandas.compat import range, u, product
import pandas.util.testing as tm


class TestPivotTable(tm.TestCase):

    _multiprocess_can_split_ = True

    def setUp(self):
        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:
            self.assertEqual(table.index.names, tuple(index))
        else:
            self.assertEqual(table.index.name, index[0])

        if len(columns) > 1:
            self.assertEqual(table.columns.names, columns)
        else:
            self.assertEqual(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_warnings(self):
        index = ['A', 'B']
        columns = 'C'
        with tm.assert_produces_warning(FutureWarning):
            table = pivot_table(self.data, values='D', rows=index,
                                cols=columns)

        with tm.assert_produces_warning(False):
            table2 = pivot_table(self.data, values='D', index=index,
                                 columns=columns)

        tm.assert_frame_equal(table, table2)

    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([(u('A'), u('a')),
                                    (u('A'), u('b')),
                                    (u('A'), u('c')),
                                    (u('A'), u('d')),
                                    (u('B'), u('a')),
                                    (u('B'), u('b')),
                                    (u('B'), u('c')),
                                    (u('B'), u('d')),
                                    (u('C'), u('a')),
                                    (u('C'), u('b')),
                                    (u('C'), u('c')),
                                    (u('C'), u('d'))])

        assert_equal(pv_col.columns.values, m.values)
        assert_equal(pv_ind.index.values, m.values)


    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']})
        self.assertEqual(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']})
        self.assertEqual(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)

    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, nan , 20]})
        result = df.pivot('a','b','c')
        expected = DataFrame([[nan,nan,nan,nan],[nan,10,nan,nan],
                              [nan,nan,nan,nan],[nan,nan,15,20]],
                             index = Index(['R1','R2',nan,'R4'],name='a'),
                             columns = Index(['C1','C2','C3','C4'],name='b'))
        tm.assert_frame_equal(result, expected)

    def test_pivot_with_tz(self):
        # GH 5878
        df = DataFrame({'dt1': [datetime.datetime(2013, 1, 1, 9, 0),
                                datetime.datetime(2013, 1, 2, 9, 0),
                                datetime.datetime(2013, 1, 1, 9, 0),
                                datetime.datetime(2013, 1, 2, 9, 0)],
                        'dt2': [datetime.datetime(2014, 1, 1, 9, 0),
                                datetime.datetime(2014, 1, 1, 9, 0),
                                datetime.datetime(2014, 1, 2, 9, 0),
                                datetime.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_margins(self):
        def _check_output(res, col, index=['A', 'B'], columns=['C']):
            cmarg = res['All'][:-1]
            exp = self.data.groupby(index)[col].mean()
            tm.assert_series_equal(cmarg, exp)

            res = res.sortlevel()
            rmarg = res.xs(('All', ''))[:-1]
            exp = self.data.groupby(columns)[col].mean()
            tm.assert_series_equal(rmarg, exp)

            gmarg = res['All']['All', '']
            exp = self.data[col].mean()
            self.assertEqual(gmarg, exp)

        # column specified
        table = self.data.pivot_table('D', index=['A', 'B'], columns='C',
                                      margins=True, aggfunc=np.mean)
        _check_output(table, 'D')

        # no column specified
        table = self.data.pivot_table(index=['A', 'B'], columns='C',
                                      margins=True, aggfunc=np.mean)
        for valcol in table.columns.levels[0]:
            _check_output(table[valcol], valcol)

        # 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 valcol in table.columns:
            gmarg = table[valcol]['All', '']
            self.assertEqual(gmarg, self.data[valcol].mean())

        # this is OK
        table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
                                      aggfunc='mean')

        # no rows
        rtable = self.data.pivot_table(columns=['AA', 'BB'], margins=True,
                                       aggfunc=np.mean)
        tm.assert_isinstance(rtable, Series)
        for item in ['DD', 'EE', 'FF']:
            gmarg = table[item]['All', '']
            self.assertEqual(gmarg, self.data[item].mean())

    def test_pivot_integer_columns(self):
        # caused by upstream bug in unstack

        d = datetime.date.min
        data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
                            [d + datetime.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(datetime.date(2000, 1, 1), datetime.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')

        self.assertTrue(pivoted.columns.is_monotonic)

    def test_pivot_complex_aggfunc(self):
        f = {'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()
        self.assertEqual(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)
        self.assertEqual(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)
        self.assertEqual(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)
        self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0])

    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.datetime(2013, 1, 1), datetime.datetime(2013, 1, 1),
                      datetime.datetime(2013, 10, 1), datetime.datetime(2013, 10, 2),
                      datetime.datetime(2013, 10, 1), datetime.datetime(2013, 10, 2),
                      datetime.datetime(2013, 12, 2), datetime.datetime(2013, 12, 2),]}).set_index('Date')

        expected = DataFrame(np.array([10, 18, 3],dtype='int64').reshape(1, 3),
                             index=[datetime.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.datetime(2013, 1, 1), datetime.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)

        self.assertRaises(KeyError, lambda : pivot_table(df, index=Grouper(freq='6MS', key='foo'),
                          columns='Buyer', values='Quantity', aggfunc=np.sum))
        self.assertRaises(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)

        self.assertRaises(ValueError, lambda : pivot_table(df, index=Grouper(freq='6MS', level='foo'),
                          columns='Buyer', values='Quantity', aggfunc=np.sum))
        self.assertRaises(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.datetime(2013,11,1,13,0), datetime.datetime(2013,9,1,13,5),
                      datetime.datetime(2013,10,1,20,0), datetime.datetime(2013,10,2,10,0),
                      datetime.datetime(2013,11,1,20,0), datetime.datetime(2013,10,2,10,0),
                      datetime.datetime(2013,10,2,12,0), datetime.datetime(2013,12,5,14,0)],
            'PayDay' : [datetime.datetime(2013,10,4,0,0), datetime.datetime(2013,10,15,13,5),
                        datetime.datetime(2013,9,5,20,0), datetime.datetime(2013,11,2,10,0),
                        datetime.datetime(2013,10,7,20,0), datetime.datetime(2013,9,5,10,0),
                        datetime.datetime(2013,12,30,12,0), datetime.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.datetime(2013, 9, 30), datetime.datetime(2013, 10, 31),
                                    datetime.datetime(2013, 11, 30), datetime.datetime(2013, 12, 31)],
                             columns=[datetime.datetime(2013, 9, 30), datetime.datetime(2013, 10, 31),
                                    datetime.datetime(2013, 11, 30), datetime.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.datetime(2013, 9, 30), datetime.datetime(2013, 10, 31)),
                  (datetime.datetime(2013, 10, 31), datetime.datetime(2013, 9, 30)),
                  (datetime.datetime(2013, 10, 31), datetime.datetime(2013, 11, 30)),
                  (datetime.datetime(2013, 10, 31), datetime.datetime(2013, 12, 31)),
                  (datetime.datetime(2013, 11, 30), datetime.datetime(2013, 10, 31)),
                  (datetime.datetime(2013, 12, 31), datetime.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)


class TestCrosstab(tm.TestCase):

    def setUp(self):
        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)
        self.assertEqual(result.index.name, 'row_0')
        self.assertEqual(result.columns.name, 'col_0')

    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)

        self.assertEqual(result.index.names, ('a',))
        self.assertEqual(result.columns.names, ['b', 'c'])

        all_cols = result['All', '']
        exp_cols = df.groupby(['a']).size().astype('i8')
        exp_cols = exp_cols.append(Series([len(df)], index=['All']))

        tm.assert_series_equal(all_cols, exp_cols)

        all_rows = result.ix['All']
        exp_rows = df.groupby(['b', 'c']).size().astype('i8')
        exp_rows = exp_rows.append(Series([len(df)], index=[('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_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 = crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'], dropna=False)
        m = MultiIndex.from_tuples([('one', 'dull'), ('one', 'shiny'),
                                    ('two', 'dull'), ('two', 'shiny')])
        assert_equal(res.columns.values, m.values)

if __name__ == '__main__':
    import nose
    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                   exit=False)
