/pandas/tests/test_groupby.py

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import nose
import unittest

from datetime import datetime
from numpy import nan

from pandas import bdate_range
from pandas.core.index import Index, MultiIndex
from pandas.core.common import rands
from pandas.core.api import Categorical, DataFrame
from pandas.core.groupby import GroupByError, SpecificationError, DataError
from pandas.core.series import Series
from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
                                 assert_series_equal, assert_almost_equal)
from pandas.core.panel import Panel
from pandas.tools.merge import concat
from collections import defaultdict
import pandas.core.common as com
import pandas.core.datetools as dt
import numpy as np
from numpy.testing import assert_equal

import pandas.core.nanops as nanops

import pandas.util.testing as tm

def commonSetUp(self):
    self.dateRange = bdate_range('1/1/2005', periods=250)
    self.stringIndex = Index([rands(8).upper() for x in xrange(250)])

    self.groupId = Series([x[0] for x in self.stringIndex],
                              index=self.stringIndex)
    self.groupDict = dict((k, v) for k, v in self.groupId.iteritems())

    self.columnIndex = Index(['A', 'B', 'C', 'D', 'E'])

    randMat = np.random.randn(250, 5)
    self.stringMatrix = DataFrame(randMat, columns=self.columnIndex,
                                  index=self.stringIndex)

    self.timeMatrix = DataFrame(randMat, columns=self.columnIndex,
                                index=self.dateRange)

class TestGroupBy(unittest.TestCase):

    _multiprocess_can_split_ = True

    def setUp(self):
        self.ts = tm.makeTimeSeries()

        self.seriesd = tm.getSeriesData()
        self.tsd = tm.getTimeSeriesData()
        self.frame = DataFrame(self.seriesd)
        self.tsframe = DataFrame(self.tsd)

        self.df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                                    'foo', 'bar', 'foo', 'foo'],
                             'B' : ['one', 'one', 'two', 'three',
                                    'two', 'two', 'one', 'three'],
                             'C' : np.random.randn(8),
                             'D' : np.random.randn(8)})

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                                   ['one', 'two', 'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.mframe = DataFrame(np.random.randn(10, 3), index=index,
                                columns=['A', 'B', 'C'])

        self.three_group = 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_basic(self):
        data = Series(np.arange(9) // 3, index=np.arange(9))

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

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

        for k, v in grouped:
            self.assertEqual(len(v), 3)

        agged = grouped.aggregate(np.mean)
        self.assertEqual(agged[1], 1)

        assert_series_equal(agged, grouped.agg(np.mean)) # shorthand
        assert_series_equal(agged, grouped.mean())

        # Cython only returning floating point for now...
        assert_series_equal(grouped.agg(np.sum).astype(float),
                            grouped.sum())

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

        value_grouped = data.groupby(data)
        assert_series_equal(value_grouped.aggregate(np.mean), agged)

        # complex agg
        agged = grouped.aggregate([np.mean, np.std])
        agged = grouped.aggregate({'one' : np.mean,
                                   'two' : np.std})

        group_constants = {
            0 : 10,
            1 : 20,
            2 : 30
        }
        agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
        self.assertEqual(agged[1], 21)

        # corner cases
        self.assertRaises(Exception, grouped.aggregate, lambda x: x * 2)

    def test_first_last_nth(self):
        # tests for first / last / nth
        grouped = self.df.groupby('A')
        first = grouped.first()
        expected = self.df.ix[[1, 0], ['B', 'C', 'D']]
        expected.index = ['bar', 'foo']
        assert_frame_equal(first, expected)

        last = grouped.last()
        expected = self.df.ix[[5, 7], ['B', 'C', 'D']]
        expected.index = ['bar', 'foo']
        assert_frame_equal(last, expected)

        nth = grouped.nth(1)
        expected = self.df.ix[[3, 2], ['B', 'C', 'D']]
        expected.index = ['bar', 'foo']
        assert_frame_equal(nth, expected)

        # it works!
        grouped['B'].first()
        grouped['B'].last()
        grouped['B'].nth(0)

        self.df['B'][self.df['A'] == 'foo'] = np.nan
        self.assert_(com.isnull(grouped['B'].first()['foo']))
        self.assert_(com.isnull(grouped['B'].last()['foo']))
        self.assert_(com.isnull(grouped['B'].nth(0)['foo']))

    def test_grouper_iter(self):
        self.assertEqual(sorted(self.df.groupby('A').grouper), ['bar', 'foo'])

    def test_empty_groups(self):
        # GH # 1048
        self.assertRaises(ValueError, self.df.groupby, [])

    def test_groupby_grouper(self):
        grouped = self.df.groupby('A')

        result = self.df.groupby(grouped.grouper).mean()
        expected = grouped.mean()
        assert_frame_equal(result, expected)

    def test_groupby_dict_mapping(self):
        # GH #679
        from pandas import Series
        s = Series({'T1': 5})
        result = s.groupby({'T1': 'T2'}).agg(sum)
        expected = s.groupby(['T2']).agg(sum)
        assert_series_equal(result, expected)

        s = Series([1., 2., 3., 4.], index=list('abcd'))
        mapping = {'a' : 0, 'b' : 0, 'c' : 1, 'd' : 1}

        result = s.groupby(mapping).mean()
        result2 = s.groupby(mapping).agg(np.mean)
        expected = s.groupby([0, 0, 1, 1]).mean()
        expected2 = s.groupby([0, 0, 1, 1]).mean()
        assert_series_equal(result, expected)
        assert_series_equal(result, result2)
        assert_series_equal(result, expected2)

    def test_groupby_nonobject_dtype(self):
        key = self.mframe.index.labels[0]
        grouped = self.mframe.groupby(key)
        result = grouped.sum()

        expected = self.mframe.groupby(key.astype('O')).sum()
        assert_frame_equal(result, expected)

    def test_agg_regression1(self):
        grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month])
        result = grouped.agg(np.mean)
        expected = grouped.mean()
        assert_frame_equal(result, expected)

    def test_agg_datetimes_mixed(self):
        data = [[1, '2012-01-01', 1.0],
                [2, '2012-01-02', 2.0],
                [3, None, 3.0]]

        df1 = DataFrame({'key': [x[0] for x in data],
                         'date': [x[1] for x in data],
                         'value': [x[2] for x in data]})

        data = [[row[0], datetime.strptime(row[1], '%Y-%m-%d').date()
                if row[1] else None, row[2]] for row in data]

        df2 = DataFrame({'key': [x[0] for x in data],
                         'date': [x[1] for x in data],
                         'value': [x[2] for x in data]})

        df1['weights'] = df1['value']/df1['value'].sum()
        gb1 = df1.groupby('date').aggregate(np.sum)

        df2['weights'] = df1['value']/df1['value'].sum()
        gb2 = df2.groupby('date').aggregate(np.sum)

        assert(len(gb1) == len(gb2))

    def test_agg_must_agg(self):
        grouped = self.df.groupby('A')['C']
        self.assertRaises(Exception, grouped.agg, lambda x: x.describe())
        self.assertRaises(Exception, grouped.agg, lambda x: x.index[:2])

    def test_agg_ser_multi_key(self):
        ser = self.df.C
        f = lambda x: x.sum()
        results = self.df.C.groupby([self.df.A, self.df.B]).aggregate(f)
        expected = self.df.groupby(['A', 'B']).sum()['C']
        assert_series_equal(results, expected)

    def test_get_group(self):
        wp = tm.makePanel()
        grouped = wp.groupby(lambda x: x.month, axis='major')

        gp = grouped.get_group(1)
        expected = wp.reindex(major=[x for x in wp.major_axis if x.month == 1])
        assert_panel_equal(gp, expected)

    def test_agg_apply_corner(self):
        # nothing to group, all NA
        grouped = self.ts.groupby(self.ts * np.nan)

        assert_series_equal(grouped.sum(), Series([]))
        assert_series_equal(grouped.agg(np.sum), Series([]))
        assert_series_equal(grouped.apply(np.sum), Series([]))

        # DataFrame
        grouped = self.tsframe.groupby(self.tsframe['A'] * np.nan)
        exp_df = DataFrame(columns=self.tsframe.columns, dtype=float)
        assert_frame_equal(grouped.sum(), exp_df)
        assert_frame_equal(grouped.agg(np.sum), exp_df)
        assert_frame_equal(grouped.apply(np.sum), DataFrame({}, dtype=float))

    def test_agg_grouping_is_list_tuple(self):
        from pandas.core.groupby import Grouping

        df = tm.makeTimeDataFrame()

        grouped = df.groupby(lambda x: x.year)
        grouper = grouped.grouper.groupings[0].grouper
        grouped.grouper.groupings[0] = Grouping(self.ts.index, list(grouper))

        result = grouped.agg(np.mean)
        expected = grouped.mean()
        tm.assert_frame_equal(result, expected)

        grouped.grouper.groupings[0] = Grouping(self.ts.index, tuple(grouper))

        result = grouped.agg(np.mean)
        expected = grouped.mean()
        tm.assert_frame_equal(result, expected)

    def test_agg_python_multiindex(self):
        grouped = self.mframe.groupby(['A', 'B'])

        result = grouped.agg(np.mean)
        expected = grouped.mean()
        tm.assert_frame_equal(result, expected)

    def test_apply_describe_bug(self):
        grouped = self.mframe.groupby(level='first')
        result = grouped.describe() # it works!

    def test_len(self):
        df = tm.makeTimeDataFrame()
        grouped = df.groupby([lambda x: x.year,
                              lambda x: x.month,
                              lambda x: x.day])
        self.assertEquals(len(grouped), len(df))

        grouped = df.groupby([lambda x: x.year,
                              lambda x: x.month])
        expected = len(set([(x.year, x.month) for x in df.index]))
        self.assertEquals(len(grouped), expected)

    def test_groups(self):
        grouped = self.df.groupby(['A'])
        groups = grouped.groups
        self.assert_(groups is grouped.groups) # caching works

        for k, v in grouped.groups.iteritems():
            self.assert_((self.df.ix[v]['A'] == k).all())

        grouped = self.df.groupby(['A', 'B'])
        groups = grouped.groups
        self.assert_(groups is grouped.groups) # caching works
        for k, v in grouped.groups.iteritems():
            self.assert_((self.df.ix[v]['A'] == k[0]).all())
            self.assert_((self.df.ix[v]['B'] == k[1]).all())

    def test_aggregate_str_func(self):
        from pandas.util.compat import OrderedDict
        def _check_results(grouped):
            # single series
            result = grouped['A'].agg('std')
            expected = grouped['A'].std()
            assert_series_equal(result, expected)

            # group frame by function name
            result = grouped.aggregate('var')
            expected = grouped.var()
            assert_frame_equal(result, expected)

            # group frame by function dict
            result = grouped.agg(OrderedDict([['A' , 'var'], ['B' , 'std'], ['C' , 'mean']]))
            expected = DataFrame(OrderedDict([['A', grouped['A'].var()],
                                  ['B', grouped['B'].std()],
                                  ['C', grouped['C'].mean()]]))
            assert_frame_equal(result, expected)

        by_weekday = self.tsframe.groupby(lambda x: x.weekday())
        _check_results(by_weekday)

        by_mwkday = self.tsframe.groupby([lambda x: x.month,
                                          lambda x: x.weekday()])
        _check_results(by_mwkday)

    def test_aggregate_item_by_item(self):

        df = self.df.copy()
        df['E'] = ['a'] * len(self.df)
        grouped = self.df.groupby('A')
        def aggfun(ser):
            return len(ser + 'a')
        result = grouped.agg(aggfun)
        self.assertEqual(len(result.columns), 1)

        aggfun = lambda ser: ser.size
        result = grouped.agg(aggfun)
        foo = (self.df.A == 'foo').sum()
        bar = (self.df.A == 'bar').sum()
        K = len(result.columns)
        assert_almost_equal(result.xs('foo'), [foo] * K)
        assert_almost_equal(result.xs('bar'), [bar] * K)

        def aggfun(ser):
            return ser.size
        result = DataFrame().groupby(self.df.A).agg(aggfun)
        self.assert_(isinstance(result, DataFrame))
        self.assertEqual(len(result), 0)

    def test_basic_regression(self):
        # regression
        T = [1.0*x for x in range(1,10) *10][:1095]
        result = Series(T, range(0, len(T)))

        groupings = np.random.random((1100,))
        groupings = Series(groupings, range(0, len(groupings))) * 10.

        grouped = result.groupby(groupings)
        grouped.mean()

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

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

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

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

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

        self.assert_(result.index.equals(self.ts.index))
        for _, gp in grouped:
            assert_fp_equal(result.reindex(gp.index), gp.mean())

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

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

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

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

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

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

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

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

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

        assert_frame_equal(result, expected)

    def test_transform_exclude_nuisance(self):
        expected = {}
        grouped = self.df.groupby('A')
        expected['C'] = grouped['C'].transform(np.mean)
        expected['D'] = grouped['D'].transform(np.mean)
        expected = DataFrame(expected)

        result = self.df.groupby('A').transform(np.mean)

        assert_frame_equal(result, expected)

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

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

    def test_with_na(self):
        index = Index(np.arange(10))
        values = Series(np.ones(10), index)
        labels = Series([nan, 'foo', 'bar', 'bar', nan, nan, 'bar',
                         'bar', nan, 'foo'], index=index)

        grouped = values.groupby(labels)
        agged = grouped.agg(len)
        expected = Series([4, 2], index=['bar', 'foo'])

        assert_series_equal(agged, expected, check_dtype=False)
        self.assert_(issubclass(agged.dtype.type, np.integer))

    def test_attr_wrapper(self):
        grouped = self.ts.groupby(lambda x: x.weekday())

        result = grouped.std()
        expected = grouped.agg(lambda x: np.std(x, ddof=1))
        assert_series_equal(result, expected)

        # this is pretty cool
        result = grouped.describe()
        expected = {}
        for name, gp in grouped:
            expected[name] = gp.describe()
        expected = DataFrame(expected).T
        assert_frame_equal(result.unstack(), expected)

        # get attribute
        result = grouped.dtype
        expected = grouped.agg(lambda x: x.dtype)

        # make sure raises error
        self.assertRaises(AttributeError, getattr, grouped, 'foo')

    def test_series_describe_multikey(self):
        ts = tm.makeTimeSeries()
        grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
        result = grouped.describe().unstack()
        assert_series_equal(result['mean'], grouped.mean())
        assert_series_equal(result['std'], grouped.std())
        assert_series_equal(result['min'], grouped.min())

    def test_series_describe_single(self):
        ts = tm.makeTimeSeries()
        grouped = ts.groupby(lambda x: x.month)
        result = grouped.apply(lambda x: x.describe())
        expected = grouped.describe()
        assert_series_equal(result, expected)

    def test_series_agg_multikey(self):
        ts = tm.makeTimeSeries()
        grouped = ts.groupby([lambda x: x.year, lambda x: x.month])

        result = grouped.agg(np.sum)
        expected = grouped.sum()
        assert_series_equal(result, expected)

    def test_series_agg_multi_pure_python(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 bad(x):
            assert(len(x.base) > 0)
            return 'foo'

        result = data.groupby(['A', 'B']).agg(bad)
        expected = data.groupby(['A', 'B']).agg(lambda x: 'foo')
        assert_frame_equal(result, expected)

    def test_series_index_name(self):
        grouped = self.df.ix[:, ['C']].groupby(self.df['A'])
        result = grouped.agg(lambda x: x.mean())
        self.assertEqual(result.index.name, 'A')

    def test_frame_describe_multikey(self):
        grouped = self.tsframe.groupby([lambda x: x.year,
                                        lambda x: x.month])
        result = grouped.describe()

        for col in self.tsframe:
            expected = grouped[col].describe()
            assert_series_equal(result[col], expected)

        groupedT = self.tsframe.groupby({'A' : 0, 'B' : 0,
                                         'C' : 1, 'D' : 1}, axis=1)
        result = groupedT.describe()

        for name, group in groupedT:
            assert_frame_equal(result[name], group.describe())

    def test_frame_groupby(self):
        grouped = self.tsframe.groupby(lambda x: x.weekday())

        # aggregate
        aggregated = grouped.aggregate(np.mean)
        self.assertEqual(len(aggregated), 5)
        self.assertEqual(len(aggregated.columns), 4)

        # by string
        tscopy = self.tsframe.copy()
        tscopy['weekday'] = [x.weekday() for x in tscopy.index]
        stragged = tscopy.groupby('weekday').aggregate(np.mean)
        assert_frame_equal(stragged, aggregated)

        # transform
        transformed = grouped.transform(lambda x: x - x.mean())
        self.assertEqual(len(transformed), 30)
        self.assertEqual(len(transformed.columns), 4)

        # transform propagate
        transformed = grouped.transform(lambda x: x.mean())
        for name, group in grouped:
            mean = group.mean()
            for idx in group.index:
                assert_almost_equal(transformed.xs(idx), mean)

        # iterate
        for weekday, group in grouped:
            self.assert_(group.index[0].weekday() == weekday)

        # groups / group_indices
        groups = grouped.groups
        indices = grouped.indices

        for k, v in groups.iteritems():
            samething = self.tsframe.index.take(indices[k])
            self.assertTrue((samething == v).all())

    def test_grouping_is_iterable(self):
        # this code path isn't used anywhere else
        # not sure it's useful
        grouped = self.tsframe.groupby([lambda x: x.weekday(),
                                        lambda x: x.year])

        # test it works
        for g in grouped.grouper.groupings[0]:
            pass

    def test_frame_groupby_columns(self):
        mapping = {
            'A' : 0, 'B' : 0, 'C' : 1, 'D' : 1
        }
        grouped = self.tsframe.groupby(mapping, axis=1)

        # aggregate
        aggregated = grouped.aggregate(np.mean)
        self.assertEqual(len(aggregated), len(self.tsframe))
        self.assertEqual(len(aggregated.columns), 2)

        # transform
        tf = lambda x: x - x.mean()
        groupedT = self.tsframe.T.groupby(mapping, axis=0)
        assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))

        # iterate
        for k, v in grouped:
            self.assertEqual(len(v.columns), 2)

    def test_frame_set_name_single(self):
        grouped = self.df.groupby('A')

        result = grouped.mean()
        self.assert_(result.index.name == 'A')

        result = self.df.groupby('A', as_index=False).mean()
        self.assert_(result.index.name != 'A')

        result = grouped.agg(np.mean)
        self.assert_(result.index.name == 'A')

        result = grouped.agg({'C' : np.mean, 'D' : np.std})
        self.assert_(result.index.name == 'A')

        result = grouped['C'].mean()
        self.assert_(result.index.name == 'A')
        result = grouped['C'].agg(np.mean)
        self.assert_(result.index.name == 'A')
        result = grouped['C'].agg([np.mean, np.std])
        self.assert_(result.index.name == 'A')

        result = grouped['C'].agg({'foo' : np.mean, 'bar' : np.std})
        self.assert_(result.index.name == 'A')

    def test_multi_iter(self):
        s = Series(np.arange(6))
        k1 = np.array(['a', 'a', 'a', 'b', 'b', 'b'])
        k2 = np.array(['1', '2', '1', '2', '1', '2'])

        grouped = s.groupby([k1, k2])

        iterated = list(grouped)
        expected = [('a', '1', s[[0, 2]]),
                    ('a', '2', s[[1]]),
                    ('b', '1', s[[4]]),
                    ('b', '2', s[[3, 5]])]
        for i, ((one, two), three) in enumerate(iterated):
            e1, e2, e3 = expected[i]
            self.assert_(e1 == one)
            self.assert_(e2 == two)
            assert_series_equal(three, e3)

    def test_multi_iter_frame(self):
        k1 = np.array(['b', 'b', 'b', 'a', 'a', 'a'])
        k2 = np.array(['1', '2', '1', '2', '1', '2'])
        df = DataFrame({'v1' : np.random.randn(6),
                        'v2' : np.random.randn(6),
                        'k1' : k1, 'k2' : k2},
                       index=['one', 'two', 'three', 'four', 'five', 'six'])

        grouped = df.groupby(['k1', 'k2'])

        # things get sorted!
        iterated = list(grouped)
        idx = df.index
        expected = [('a', '1', df.ix[idx[[4]]]),
                    ('a', '2', df.ix[idx[[3, 5]]]),
                    ('b', '1', df.ix[idx[[0, 2]]]),
                    ('b', '2', df.ix[idx[[1]]])]
        for i, ((one, two), three) in enumerate(iterated):
            e1, e2, e3 = expected[i]
            self.assert_(e1 == one)
            self.assert_(e2 == two)
            assert_frame_equal(three, e3)

        # don't iterate through groups with no data
        df['k1'] = np.array(['b', 'b', 'b', 'a', 'a', 'a'])
        df['k2'] = np.array(['1', '1', '1', '2', '2', '2'])
        grouped = df.groupby(['k1', 'k2'])
        groups = {}
        for key, gp in grouped:
            groups[key] = gp
        self.assertEquals(len(groups), 2)

        # axis = 1
        three_levels = self.three_group.groupby(['A', 'B', 'C']).mean()
        grouped = three_levels.T.groupby(axis=1, level=(1, 2))
        for key, group in grouped:
            pass

    def test_multi_iter_panel(self):
        wp = tm.makePanel()
        grouped = wp.groupby([lambda x: x.month, lambda x: x.weekday()],
                             axis=1)

        for (month, wd), group in grouped:
            exp_axis = [x for x in wp.major_axis
                        if x.month == month and x.weekday() == wd]
            expected = wp.reindex(major=exp_axis)
            assert_panel_equal(group, expected)

    def test_multi_func(self):
        col1 = self.df['A']
        col2 = self.df['B']

        grouped = self.df.groupby([col1.get, col2.get])
        agged = grouped.mean()
        expected = self.df.groupby(['A', 'B']).mean()
        assert_frame_equal(agged.ix[:, ['C', 'D']],
                           expected.ix[:, ['C', 'D']])

        # some "groups" with no data
        df = DataFrame({'v1' : np.random.randn(6),
                        'v2' : np.random.randn(6),
                        'k1' : np.array(['b', 'b', 'b', 'a', 'a', 'a']),
                        'k2' : np.array(['1', '1', '1', '2', '2', '2'])},
                       index=['one', 'two', 'three', 'four', 'five', 'six'])
        # only verify that it works for now
        grouped = df.groupby(['k1', 'k2'])
        grouped.agg(np.sum)

    def test_multi_key_multiple_functions(self):
        grouped = self.df.groupby(['A', 'B'])['C']

        agged = grouped.agg([np.mean, np.std])
        expected = DataFrame({'mean' : grouped.agg(np.mean),
                              'std' : grouped.agg(np.std)})
        assert_frame_equal(agged, expected)

    def test_frame_multi_key_function_list(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)})

        grouped = data.groupby(['A', 'B'])
        funcs = [np.mean, np.std]
        agged = grouped.agg(funcs)
        expected = concat([grouped['D'].agg(funcs), grouped['E'].agg(funcs),
                           grouped['F'].agg(funcs)],
                          keys=['D', 'E', 'F'], axis=1)
        assert(isinstance(agged.index, MultiIndex))
        assert(isinstance(expected.index, MultiIndex))
        assert_frame_equal(agged, expected)

    def test_groupby_multiple_columns(self):
        data = self.df
        grouped = data.groupby(['A', 'B'])

        def _check_op(op):

            result1 = op(grouped)

            expected = defaultdict(dict)
            for n1, gp1 in data.groupby('A'):
                for n2, gp2 in gp1.groupby('B'):
                    expected[n1][n2] = op(gp2.ix[:, ['C', 'D']])
            expected = dict((k, DataFrame(v)) for k, v in expected.iteritems())
            expected = Panel.fromDict(expected).swapaxes(0, 1)

            # a little bit crude
            for col in ['C', 'D']:
                result_col = op(grouped[col])
                exp = expected[col]
                pivoted = result1[col].unstack()
                pivoted2 = result_col.unstack()
                assert_frame_equal(pivoted.reindex_like(exp), exp)
                assert_frame_equal(pivoted2.reindex_like(exp), exp)

        _check_op(lambda x: x.sum())
        _check_op(lambda x: x.mean())

        # test single series works the same
        result = data['C'].groupby([data['A'], data['B']]).mean()
        expected = data.groupby(['A', 'B']).mean()['C']

        assert_series_equal(result, expected)

    def test_groupby_as_index_agg(self):
        from pandas.util.compat import OrderedDict
        grouped = self.df.groupby('A', as_index=False)

        # single-key

        result = grouped.agg(np.mean)
        expected = grouped.mean()
        assert_frame_equal(result, expected)

        result2 = grouped.agg(OrderedDict([['C' , np.mean], ['D' , np.sum]]))
        expected2 = grouped.mean()
        expected2['D'] = grouped.sum()['D']
        assert_frame_equal(result2, expected2)

        grouped = self.df.groupby('A', as_index=True)
        expected3 = grouped['C'].sum()
        expected3 = DataFrame(expected3).rename(columns={'C' : 'Q'})
        result3 = grouped['C'].agg({'Q' : np.sum})
        assert_frame_equal(result3, expected3)

        # multi-key

        grouped = self.df.groupby(['A', 'B'], as_index=False)

        result = grouped.agg(np.mean)
        expected = grouped.mean()
        assert_frame_equal(result, expected)

        result2 = grouped.agg(OrderedDict([['C' , np.mean], ['D' , np.sum]]))
        expected2 = grouped.mean()
        expected2['D'] = grouped.sum()['D']
        assert_frame_equal(result2, expected2)

        expected3 = grouped['C'].sum()
        expected3 = DataFrame(expected3).rename(columns={'C' : 'Q'})
        result3 = grouped['C'].agg({'Q' : np.sum})
        assert_frame_equal(result3, expected3)

    def test_multifunc_select_col_integer_cols(self):
        df = self.df
        df.columns = np.arange(len(df.columns))

        # it works!
        result = df.groupby(1, as_index=False)[2].agg({'Q' : np.mean})

    def test_as_index_series_return_frame(self):
        grouped = self.df.groupby('A', as_index=False)
        grouped2 = self.df.groupby(['A', 'B'], as_index=False)

        result = grouped['C'].agg(np.sum)
        expected = grouped.agg(np.sum).ix[:, ['A', 'C']]
        self.assert_(isinstance(result, DataFrame))
        assert_frame_equal(result, expected)

        result2 = grouped2['C'].agg(np.sum)
        expected2 = grouped2.agg(np.sum).ix[:, ['A', 'B', 'C']]
        self.assert_(isinstance(result2, DataFrame))
        assert_frame_equal(result2, expected2)

        result = grouped['C'].sum()
        expected = grouped.sum().ix[:, ['A', 'C']]
        self.assert_(isinstance(result, DataFrame))
        assert_frame_equal(result, expected)

        result2 = grouped2['C'].sum()
        expected2 = grouped2.sum().ix[:, ['A', 'B', 'C']]
        self.assert_(isinstance(result2, DataFrame))
        assert_frame_equal(result2, expected2)

        # corner case
        self.assertRaises(Exception, grouped['C'].__getitem__,
                          'D')

    def test_groupby_as_index_cython(self):
        data = self.df

        # single-key
        grouped = data.groupby('A', as_index=False)
        result = grouped.mean()
        expected = data.groupby(['A']).mean()
        expected.insert(0, 'A', expected.index)
        expected.index = np.arange(len(expected))
        assert_frame_equal(result, expected)

        # multi-key
        grouped = data.groupby(['A', 'B'], as_index=False)
        result = grouped.mean()
        expected = data.groupby(['A', 'B']).mean()

        arrays = zip(*expected.index._tuple_index)
        expected.insert(0, 'A', arrays[0])
        expected.insert(1, 'B', arrays[1])
        expected.index = np.arange(len(expected))
        assert_frame_equal(result, expected)

    def test_groupby_as_index_series_scalar(self):
        grouped = self.df.groupby(['A', 'B'], as_index=False)

        # GH #421

        result = grouped['C'].agg(len)
        expected = grouped.agg(len).ix[:, ['A', 'B', 'C']]
        assert_frame_equal(result, expected)

    def test_groupby_as_index_corner(self):
        self.assertRaises(TypeError, self.ts.groupby,
                          lambda x: x.weekday(), as_index=False)

        self.assertRaises(ValueError, self.df.groupby,
                          lambda x: x.lower(), as_index=False, axis=1)

    def test_groupby_multiple_key(self):
        df = tm.makeTimeDataFrame()
        grouped = df.groupby([lambda x: x.year,
                              lambda x: x.month,
                              lambda x: x.day])
        agged = grouped.sum()
        assert_almost_equal(df.values, agged.values)

        grouped = df.T.groupby([lambda x: x.year,
                                lambda x: x.month,
                                lambda x: x.day], axis=1)

        agged = grouped.agg(lambda x: x.sum(1))
        self.assert_(agged.index.equals(df.columns))
        assert_almost_equal(df.T.values, agged.values)

        agged = grouped.agg(lambda x: x.sum(1))
        assert_almost_equal(df.T.values, agged.values)

    def test_groupby_multi_corner(self):
        # test that having an all-NA column doesn't mess you up
        df = self.df.copy()
        df['bad'] = np.nan
        agged = df.groupby(['A', 'B']).mean()

        expected = self.df.groupby(['A', 'B']).mean()
        expected['bad'] = np.nan

        assert_frame_equal(agged, expected)

    def test_omit_nuisance(self):
        grouped = self.df.groupby('A')

        result = grouped.mean()
        expected = self.df.ix[:, ['A', 'C', 'D']].groupby('A').mean()
        assert_frame_equal(result, expected)

        agged = grouped.agg(np.mean)
        exp = grouped.mean()
        assert_frame_equal(agged, exp)

        df = self.df.ix[:, ['A', 'C', 'D']]
        df['E'] = datetime.now()
        grouped = df.groupby('A')
        result = grouped.agg(np.sum)
        expected = grouped.sum()
        assert_frame_equal(result, expected)

        # won't work with axis = 1
        grouped = df.groupby({'A' : 0, 'C' : 0, 'D' : 1, 'E' : 1}, axis=1)
        result = self.assertRaises(TypeError, grouped.agg,
                                   lambda x: x.sum(1, numeric_only=False))

    def test_omit_nuisance_python_multiple(self):
        grouped = self.three_group.groupby(['A', 'B'])

        agged = grouped.agg(np.mean)
        exp = grouped.mean()
        assert_frame_equal(agged, exp)

    def test_empty_groups_corner(self):
        # handle empty groups
        df = DataFrame({'k1' : np.array(['b', 'b', 'b', 'a', 'a', 'a']),
                        'k2' : np.array(['1', '1', '1', '2', '2', '2']),
                        'k3' : ['foo', 'bar'] * 3,
                        'v1' : np.random.randn(6),
                        'v2' : np.random.randn(6)})

        grouped = df.groupby(['k1', 'k2'])
        result = grouped.agg(np.mean)
        expected = grouped.mean()
        assert_frame_equal(result, expected)

        grouped = self.mframe[3:5].groupby(level=0)
        agged = grouped.apply(lambda x: x.mean())
        agged_A = grouped['A'].apply(np.mean)
        assert_series_equal(agged['A'], agged_A)
        self.assertEquals(agged.index.name, 'first')

    def test_apply_concat_preserve_names(self):
        grouped = self.three_group.groupby(['A', 'B'])

        def desc(group):
            result = group.describe()
            result.index.name = 'stat'
            return result

        def desc2(group):
            result = group.describe()
            result.index.name = 'stat'
            result = result[:len(group)]
            # weirdo
            return result

        def desc3(group):
            result = group.describe()

            # names are different
            result.index.name = 'stat_%d' % len(group)

            result = result[:len(group)]
            # weirdo
            return result

        result = grouped.apply(desc)
        self.assertEquals(result.index.names, ['A', 'B', 'stat'])

        result2 = grouped.apply(desc2)
        self.assertEquals(result2.index.names, ['A', 'B', 'stat'])

        result3 = grouped.apply(desc3)
        self.assertEquals(result3.index.names, ['A', 'B', None])

    def test_nonsense_func(self):
        df = DataFrame([0])
        self.assertRaises(Exception, df.groupby, lambda x: x + 'foo')

    def test_cythonized_aggers(self):
        data = {'A' : [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan],
                'B' : ['A', 'B'] * 6,
                'C' : np.random.randn(12)}
        df = DataFrame(data)
        df['C'][2:10:2] = nan

        def _testit(op):
            # single column
            grouped = df.drop(['B'], axis=1).groupby('A')
            exp = {}
            for cat, group in grouped:
                exp[cat] = op(group['C'])
            exp = DataFrame({'C' : exp})
            result = op(grouped)
            assert_frame_equal(result, exp)

            # multiple columns
            grouped = df.groupby(['A', 'B'])
            expd = {}
            for (cat1, cat2), group in grouped:
                expd.setdefault(cat1, {})[cat2] = op(group['C'])
            exp = DataFrame(expd).T.stack(dropna=False)
            result = op(grouped)['C']
            assert_series_equal(result, exp)

        _testit(lambda x: x.sum())
        _testit(lambda x: x.mean())
        _testit(lambda x: x.prod())
        _testit(lambda x: x.min())
        _testit(lambda x: x.max())

    def test_cython_agg_boolean(self):
        frame = DataFrame({'a': np.random.randint(0, 5, 50),
                           'b': np.random.randint(0, 2, 50).astype('bool')})
        result = frame.groupby('a')['b'].mean()
        expected = frame.groupby('a')['b'].agg(np.mean)

        assert_series_equal(result, expected)

    def test_cython_agg_nothing_to_agg(self):
        frame = DataFrame({'a': np.random.randint(0, 5, 50),
                           'b': ['foo', 'bar'] * 25})
        self.assertRaises(DataError, frame.groupby('a')['b'].mean)

        frame = DataFrame({'a': np.random.randint(0, 5, 50),
                           'b': ['foo', 'bar'] * 25})
        self.assertRaises(DataError, frame[['b']].groupby(frame['a']).mean)

    def test_cython_agg_frame_columns(self):
        # #2113
        df = DataFrame({'x': [1,2,3], 'y': [3,4,5]})

        result = df.groupby(level=0, axis='columns').mean()
        result = df.groupby(level=0, axis='columns').mean()
        result = df.groupby(level=0, axis='columns').mean()
        _ = df.groupby(level=0, axis='columns').mean()

    def test_wrap_aggregated_output_multindex(self):
        df = self.mframe.T
        df['baz', 'two'] = 'peekaboo'

        keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
        agged = df.groupby(keys).agg(np.mean)
        self.assert_(isinstance(agged.columns, MultiIndex))

        def aggfun(ser):
            if ser.name == ('foo', 'one'):
                raise TypeError
            else:
                return ser.sum()
        agged2 = df.groupby(keys).aggregate(aggfun)
        self.assertEqual(len(agged2.columns) + 1, len(df.columns))

    def test_groupby_level(self):
        frame = self.mframe
        deleveled = frame.reset_index()

        result0 = frame.groupby(level=0).sum()
        result1 = frame.groupby(level=1).sum()

        expected0 = frame.groupby(deleveled['first'].values).sum()
        expected1 = frame.groupby(deleveled['second'].values).sum()

        expected0 = expected0.reindex(frame.index.levels[0])
        expected1 = expected1.reindex(frame.index.levels[1])

        self.assert_(result0.index.name == 'first')
        self.assert_(result1.index.name == 'second')

        assert_frame_equal(result0, expected0)
        assert_frame_equal(result1, expected1)
        self.assertEquals(result0.index.name, frame.index.names[0])
        self.assertEquals(result1.index.name, frame.index.names[1])

        # groupby level name
        result0 = frame.groupby(level='first').sum()
        result1 = frame.groupby(level='second').sum()
        assert_frame_equal(result0, expected0)
        assert_frame_equal(result1, expected1)

        # axis=1

        result0 = frame.T.groupby(level=0, axis=1).sum()
        result1 = frame.T.groupby(level=1, axis=1).sum()
        assert_frame_equal(result0, expected0.T)
        assert_frame_equal(result1, expected1.T)

        # raise exception for non-MultiIndex
        self.assertRaises(ValueError, self.df.groupby, level=1)

    def test_groupby_level_apply(self):
        frame = self.mframe

        result = frame.groupby(level=0).count()
        self.assert_(result.index.name == 'first')
        result = frame.groupby(level=1).count()
        self.assert_(result.index.name == 'second')

        result = frame['A'].groupby(level=0).count()
        self.assert_(result.index.name == 'first')

    def test_groupby_level_mapper(self):
        frame = self.mframe
        deleveled = frame.reset_index()

        mapper0 = {'foo' : 0, 'bar' : 0,
                   'baz' : 1, 'qux' : 1}
        mapper1 = {'one' : 0, 'two' : 0, 'three' : 1}

        result0 = frame.groupby(mapper0, level=0).sum()
        result1 = frame.groupby(mapper1, level=1).sum()

        mapped_level0 = np.array([mapper0.get(x) for x in deleveled['first']])
        mapped_level1 = np.array([mapper1.get(x) for x in deleveled['second']])
        expected0 = frame.groupby(mapped_level0).sum()
        expected1 = frame.groupby(mapped_level1).sum()

        assert_frame_equal(result0, expected0)
        assert_frame_equal(result1, expected1)

    def test_groupby_level_0_nonmulti(self):
        # #1313
        a = Series([1,2,3,10,4,5,20,6], Index([1,2,3,1,4,5,2,6], name='foo'))

        result = a.groupby(level=0).sum()
        self.assertEquals(result.index.name, a.index.name)

    def test_level_preserve_order(self):
        grouped = self.mframe.groupby(level=0)
        exp_labels = np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3])
        assert_almost_equal(grouped.grouper.labels[0], exp_labels)

    def test_grouping_labels(self):
        grouped = self.mframe.groupby(self.mframe.index.get_level_values(0))
        exp_labels = np.array([2, 2, 2, 0, 0, 1, 1, 3, 3, 3])
        assert_almost_equal(grouped.grouper.labels[0], exp_labels)

    def test_cython_fail_agg(self):
        dr = bdate_range('1/1/2000', periods=50)
        ts = Series(['A', 'B', 'C', 'D', 'E'] * 10, index=dr)

        grouped = ts.groupby(lambda x: x.month)
        summed = grouped.sum()
        expected = grouped.agg(np.sum)
        assert_series_equal(summed, expected)

    def test_apply_series_to_frame(self):
        def f(piece):
            return DataFrame({'value' : piece,
                              'demeaned' : piece - piece.mean(),
                              'logged' : np.log(piece)})

        dr = bdate_range('1/1/2000', periods=100)
        ts = Series(np.random.randn(100), index=dr)

        grouped = ts.groupby(lambda x: x.month)
        result = grouped.apply(f)

        self.assert_(isinstance(result, DataFrame))
        self.assert_(result.index.equals(ts.index))

    def test_apply_series_yield_constant(self):
        result = self.df.groupby(['A', 'B'])['C'].apply(len)
        self.assertEquals(result.index.names[:2], ['A', 'B'])

    def test_apply_frame_to_series(self):
        grouped = self.df.groupby(['A', 'B'])
        result = grouped.apply(len)
        expected = grouped.count()['C']
        self.assert_(result.index.equals(expected.index))
        self.assert_(np.array_equal(result.values, expected.values))

    def test_apply_frame_concat_series(self):
        def trans(group):
            return group.groupby('B')['C'].sum().order()[:2]

        def trans2(group):
            grouped = group.groupby(df.reindex(group.index)['B'])
            return grouped.sum().order()[:2]

        df = DataFrame({'A': np.random.randint(0, 5, 1000),
                        'B': np.random.randint(0, 5, 1000),
                        'C': np.random.randn(1000)})

        result = df.groupby('A').apply(trans)
        exp = df.groupby('A')['C'].apply(trans2)
        assert_series_equal(result, exp)

    def test_apply_transform(self):
        grouped = self.ts.groupby(lambda x: x.month)
        result = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(result, expected)

    def test_apply_multikey_corner(self):
        grouped = self.tsframe.groupby([lambda x: x.year,
                                        lambda x: x.month])

        def f(group):
            return group.sort('A')[-5:]

        result = grouped.apply(f)
        for key, group in grouped:
            assert_frame_equal(result.ix[key], f(group))

    def test_apply_chunk_view(self):
        # Low level tinkering could be unsafe, make sure not
        df = DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3],
                        'value': range(9)})

        # return view
        f = lambda x: x[:2]

        result = df.groupby('key', group_keys=False).apply(f)
        expected = df.take([0, 1, 3, 4, 6, 7])
        assert_frame_equal(result, expected)

    def test_groupby_series_indexed_differently(self):
        s1 = Series([5.0,-9.0,4.0,100.,-5.,55.,6.7],
                    index=Index(['a','b','c','d','e','f','g']))
        s2 = Series([1.0,1.0,4.0,5.0,5.0,7.0],
                    index=Index(['a','b','d','f','g','h']))

        grouped = s1.groupby(s2)
        agged = grouped.mean()
        exp = s1.groupby(s2.reindex(s1.index).get).mean()
        assert_series_equal(agged, exp)

    def test_groupby_with_hier_columns(self):
        tuples = zip(*[['bar', 'bar', 'baz', 'baz',
                        'foo', 'foo', 'qux', 'qux'],
                       ['one', 'two', 'one', 'two',
                        'one', 'two', 'one', 'two']])
        index = MultiIndex.from_tuples(tuples)
        columns = MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),
                                          ('B', 'cat'), ('A', 'dog')])
        df = DataFrame(np.random.randn(8, 4), index=index,
                       columns=columns)

        result = df.groupby(level=0).mean()
        self.assert_(result.columns.equals(columns))

        result = df.groupby(level=0, axis=1).mean()
        self.assert_(result.index.equals(df.index))

        result = df.groupby(level=0).agg(np.mean)
        self.assert_(result.columns.equals(columns))

        result = df.groupby(level=0).apply(lambda x: x.mean())
        self.assert_(result.columns.equals(columns))

        result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
        self.assert_(result.columns.equals(Index(['A', 'B'])))
        self.assert_(result.index.equals(df.index))

        # add a nuisance column
        sorted_columns, _ = columns.sortlevel(0)
        df['A', 'foo'] = 'bar'
        result = df.groupby(level=0).mean()
        self.assert_(result.columns.equals(df.columns[:-1]))

    def test_pass_args_kwargs(self):
        from pandas.compat.scipy import scoreatpercentile

        def f(x, q=None):
            return scoreatpercentile(x, q)
        g = lambda x: scoreatpercentile(x, 80)

        # Series
        ts_grouped = self.ts.groupby(lambda x: x.month)
        agg_result = ts_grouped.agg(scoreatpercentile, 80)
        apply_result = ts_grouped.apply(scoreatpercentile, 80)
        trans_result = ts_grouped.transform(scoreatpercentile, 80)

        agg_expected = ts_grouped.quantile(.8)
        trans_expected = ts_grouped.transform(g)

        assert_series_equal(apply_result, agg_expected)
        assert_series_equal(agg_result, agg_expected)
        assert_series_equal(trans_result, trans_expected)

        agg_result = ts_grouped.agg(f, q=80)
        apply_result = ts_grouped.apply(f, q=80)
        trans_result = ts_grouped.transform(f, q=80)
        assert_series_equal(agg_result, agg_expected)
        assert_series_equal(apply_result, agg_expected)
        assert_series_equal(trans_result, trans_expected)

        # DataFrame
        df_groupe…