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)})

        self.df_mixed_floats = 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.array(np.random.randn(8),dtype='float32')})

        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):

        def checkit(dtype):
            data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)

            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())
            assert_series_equal(grouped.agg(np.sum),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)


        for dtype in ['int64','int32','float64','float32']:
            checkit(dtype)

    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, check_names=False)

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

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

        # 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_first_last_nth_dtypes(self):

        df = self.df_mixed_floats.copy()
        df['E'] = True
        df['F'] = 1

        # tests for first / last / nth
        grouped = df.groupby('A')
        first = grouped.first()
        expected = df.ix[[1, 0], ['B', 'C', 'D', 'E', 'F']]
        expected.index = ['bar', 'foo']
        assert_frame_equal(first, expected, check_names=False)

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

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

        # GH 2763, first/last shifting dtypes
        idx = range(10)
        idx.append(9)
        s = Series(data=range(11), index=idx, name='IntCol')
        self.assert_(s.dtype == 'int64')
        f = s.groupby(level=0).first()
        self.assert_(f.dtype == 'int64')

    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_bounds_check(self):
        import pandas as pd
        # groupby_X is code-generated, so if one variant
        # does, the rest probably do to
        a = np.array([1,2],dtype='object')
        b = np.array([1,2,3],dtype='object')
        self.assertRaises(AssertionError, pd.algos.groupby_object,a, b)

    def test_groupby_grouper_f_sanity_checked(self):
        import pandas as pd
        dates = pd.date_range('01-Jan-2013', periods=12, freq='MS')
        ts = pd.TimeSeries(np.random.randn(12), index=dates)

        # GH3035
        # index.map is used to apply grouper to the index
        # if it fails on the elements, map tries it on the entire index as
        # a sequence. That can yield invalid results that cause trouble
        # down the line.
        # the surprise comes from using key[0:6] rather then str(key)[0:6]
        # when the elements are Timestamp.
        # the result is Index[0:6], very confusing.

        self.assertRaises(AssertionError, ts.groupby,lambda key: key[0:6])

    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_groupby_return_type(self):

        # GH2893
        df1 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, 
                         {"val1":2, "val2": 27}, {"val1":2, "val2": 12}])

        def func(dataf):
            return dataf["val2"]  - dataf["val2"].mean()

        result = df1.groupby("val1").apply(func)
        self.assert_(isinstance(result,Series))

        df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, 
                         {"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
        def func(dataf):
            return dataf["val2"]  - dataf["val2"].mean()

        result = df2.groupby("val1").apply(func)
        self.assert_(isinstance(result,Series))

    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_period_index(self):
        from pandas import period_range, PeriodIndex
        prng = period_range('2012-1-1', freq='M', periods=3)
        df = DataFrame(np.random.randn(3, 2), index=prng)
        rs = df.groupby(level=0).sum()
        self.assert_(isinstance(rs.index, PeriodIndex))

    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, check_names=False)
        assert_frame_equal(grouped.agg(np.sum), exp_df, check_names=False)
        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')

        # API change in 0.11
        # 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_agg_item_by_item_raise_typeerror(self):
        from numpy.random import randint

        df = DataFrame(randint(10, size=(20, 10)))

        def raiseException(df):
          print '----------------------------------------'
          print df.to_string()
          raise TypeError

        self.assertRaises(TypeError, df.groupby(0).agg,
                          raiseException)

    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))

        for dtype in ['float64','float32','int64','int32','int16','int8']:
            values = Series(np.ones(10), index, dtype=dtype)
            labels = Series([nan, 'foo', 'bar', 'bar', nan, nan, 'bar',
                             'bar', nan, 'foo'], index=index)


            # this SHOULD be an int
            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))

            # explicity return a float from my function
            def f(x):
                return float(len(x))

            agged = grouped.agg(f)
            expected = Series([4, 2], index=['bar', 'foo'])

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

    def test_indices_concatenation_order(self):

        # GH 2808

        def f1(x):
            y = x[(x.b % 2) == 1]**2
            if y.empty:
                multiindex = MultiIndex(
                    levels = [[]]*2,
                    labels = [[]]*2,
                    names = ['b', 'c']
                    )
                res = DataFrame(None,
                                   columns=['a'],
                                   index=multiindex)
                return res
            else:
                y = y.set_index(['b','c'])
                return y

        def f2(x):
            y = x[(x.b % 2) == 1]**2
            if y.empty:
                return DataFrame()
            else:
                y = y.set_index(['b','c'])
                return y

        def f3(x):
            y = x[(x.b % 2) == 1]**2
            if y.empty:
                multiindex = MultiIndex(
                    levels = [[]]*2,
                    labels = [[]]*2,
                    names = ['foo', 'bar']
                    )
                res = DataFrame(None,
                                columns=['a','b'],
                                index=multiindex)
                return res
            else:
                return y

        df = DataFrame({'a':[1,2,2,2],
                        'b':range(4),
                        'c':range(5,9)})

        df2 = DataFrame({'a':[3,2,2,2],
                         'b':range(4),
                         'c':range(5,9)})


        # correct result
        result1 = df.groupby('a').apply(f1)
        result2 = df2.groupby('a').apply(f1)
        assert_frame_equal(result1, result2)

        # should fail (not the same number of levels)
        self.assertRaises(AssertionError, df.groupby('a').apply, f2)
        self.assertRaises(AssertionError, df2.groupby('a').apply, f2)

        # should fail (incorrect shape)
        self.assertRaises(AssertionError, df.groupby('a').apply, f3)
        self.assertRaises(AssertionError, df2.groupby('a').apply, f3)

    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, check_names=False)

        # 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']],
                           check_names=False)  # TODO groupby get drops names

        # 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)
            expected.major_axis.name, expected.minor_axis.name = 'A', 'B'

            # 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})
            exp.index.name = 'A'
            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_max_min_non_numeric(self):
        # #2700
        aa = DataFrame({'nn':[11,11,22,22],'ii':[1,2,3,4],'ss':4*['mama']})

        result = aa.groupby('nn').max()
        self.assertTrue('ss' in result)

        result = aa.groupby('nn').min()
        self.assertTrue('ss' in result)

    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_with_nas(self):
        index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
                           labels=[[1, 1, 1, 1, 0, 0, 0, 0],
                                   [0, 1, 2, 3, 0, 1, 2, 3]])

        # factorizing doesn't confuse things
        s = Series(np.arange(8.), index=index)
        result = s.groupby(level=0).sum()
        expected = Series([22., 6.], index=[1, 0])
        assert_series_equal(result, expected)

        index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
                           labels=[[1, 1, 1, 1, -1, 0, 0, 0],
                                   [0, 1, 2, 3, 0, 1, 2, 3]])

        # factorizing doesn't confuse things
        s = Series(np.arange(8.), index=index)
        result = s.groupby(level=0).sum()
        expected = Series([18., 6.], index=[1, 0])
        assert_series_equal(result, expected)

    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()
        expected0.index.name, expected1.index.name = 'first', 'second'

        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_mutate_groups(self):

        # GH3380

        mydf = DataFrame({
                'cat1' : ['a'] * 8 + ['b'] * 6,
                'cat2' : ['c'] * 2 + ['d'] * 2 + ['e'] * 2 + ['f'] * 2 + ['c'] * 2 + ['d'] * 2 + ['e'] * 2,
                'cat3' : map(lambda x: 'g%s' % x, range(1,15)),
                'val' : np.random.randint(100, size=14),
                })

        def f_copy(x):
            x = x.copy()
            x['rank'] = x.val.rank(method='min')
            return x.groupby('cat2')['rank'].min()

        def f_no_copy(x):
            x['rank'] = x.val.rank(method='min')
            return x.groupby('cat2')['rank'].min()

        grpby_copy    = mydf.groupby('cat1').apply(f_copy)
        grpby_no_copy = mydf.groupby('cat1').apply(f_no_copy)
        assert_series_equal(grpby_copy,grpby_no_copy)

    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_apply_no_name_column_conflict(self):
        df = DataFrame({'name': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
                        'name2': [0, 0, 0, 1, 1, 1, 0, 0, 1, 1],
                        'value': range(10)[::-1]})

        # it works! #2605
        grouped = df.groupby(['name', 'name2'])
        grouped.apply(lambda x: x.sort('value'))

    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_grouped = self.tsframe.groupby(lambda x: x.month)
        agg_result = df_grouped.agg(scoreatpercentile, 80)
        apply_result = df_grouped.apply(DataFrame.quantile, .8)
        expected = df_grouped.quantile(.8)
        assert_frame_equal(apply_result, expected)
        assert_frame_equal(agg_result, expected)

        agg_result = df_grouped.agg(f, q=80)
        apply_result = df_grouped.apply(DataFrame.quantile, q=.8)
        assert_frame_equal(agg_result, expected)
        assert_frame_equal(apply_result, expected)

    # def test_cython_na_bug(self):
    #     values = np.random.randn(10)
    #     shape = (5, 5)
    #     label_list = [np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2], dtype=np.int32),
    # np.array([1, 2, 3, 4, 0, 1, 2, 3, 3, 4], dtype=np.int32)]

    #     lib.group_aggregate(values, label_list, shape)

    def test_size(self):
        grouped = self.df.groupby(['A', 'B'])
        result = grouped.size()
        for key, group in grouped:
            self.assertEquals(result[key], len(group))

        grouped = self.df.groupby('A')
        result = grouped.size()
        for key, group in grouped:
            self.assertEquals(result[key], len(group))

        grouped = self.df.groupby('B')
        result = grouped.size()
        for key, group in grouped:
            self.assertEquals(result[key], len(group))

    def test_grouping_ndarray(self):
        grouped = self.df.groupby(self.df['A'].values)

        result = grouped.sum()
        expected = self.df.groupby('A').sum()
        assert_frame_equal(result, expected, check_names=False)  # Note: no names when grouping by value

    def test_apply_typecast_fail(self):
        df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                        'c': np.tile(['a', 'b', 'c'], 2),
                        'v': np.arange(1., 7.)})

        def f(group):
            v = group['v']
            group['v2'] = (v - v.min()) / (v.max() - v.min())
            return group

        result = df.groupby('d').apply(f)

        expected = df.copy()
        expected['v2'] = np.tile([0., 0.5, 1], 2)

        assert_frame_equal(result, expected)

    def test_apply_multiindex_fail(self):
        index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
                                        [1, 2, 3, 1, 2, 3]])
        df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                        'c': np.tile(['a', 'b', 'c'], 2),
                        'v': np.arange(1., 7.)}, index=index)

        def f(group):
            v = group['v']
            group['v2'] = (v - v.min()) / (v.max() - v.min())
            return group

        result = df.groupby('d').apply(f)

        expected = df.copy()
        expected['v2'] = np.tile([0., 0.5, 1], 2)

        assert_frame_equal(result, expected)

    def test_apply_corner(self):
        result = self.tsframe.groupby(lambda x: x.year).apply(lambda x: x * 2)
        expected = self.tsframe * 2
        assert_frame_equal(result, expected)

    def test_apply_use_categorical_name(self):
        from pandas import qcut
        cats = qcut(self.df.C, 4)

        def get_stats(group):
            return {'min': group.min(), 'max': group.max(),
                    'count': group.count(), 'mean': group.mean()}

        result = self.df.groupby(cats).D.apply(get_stats)
        self.assertEquals(result.index.names[0], 'C')

    def test_apply_corner_cases(self):
        # #535, can't use sliding iterator

        N = 1000
        labels = np.random.randint(0, 100, size=N)
        df = DataFrame({'key': labels,
                        'value1': np.random.randn(N),
                        'value2': ['foo', 'bar', 'baz', 'qux'] * (N // 4)})

        grouped = df.groupby('key')

        def f(g):
            g['value3'] = g['value1'] * 2
            return g

        result = grouped.apply(f)
        self.assertTrue('value3' in result)

    def test_transform_mixed_type(self):
        index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
                                        [1, 2, 3, 1, 2, 3]])
        df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                        'c': np.tile(['a', 'b', 'c'], 2),
                        'v': np.arange(1., 7.)}, index=index)

        def f(group):
            group['g'] = group['d'] * 2
            return group[:1]

        grouped = df.groupby('c')
        result = grouped.apply(f)

        self.assert_(result['d'].dtype == np.float64)

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

    def test_groupby_wrong_multi_labels(self):
        from pandas import read_csv
        from pandas.util.py3compat import StringIO
        data = """index,foo,bar,baz,spam,data
0,foo1,bar1,baz1,spam2,20
1,foo1,bar2,baz1,spam3,30
2,foo2,bar2,baz1,spam2,40
3,foo1,bar1,baz2,spam1,50
4,foo3,bar1,baz2,spam1,60"""
        data = read_csv(StringIO(data), index_col=0)

        grouped = data.groupby(['foo', 'bar', 'baz', 'spam'])

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

    def test_groupby_series_with_name(self):
        result = self.df.groupby(self.df['A']).mean()
        result2 = self.df.groupby(self.df['A'], as_index=False).mean()
        self.assertEquals(result.index.name, 'A')
        self.assert_('A' in result2)

        result = self.df.groupby([self.df['A'], self.df['B']]).mean()
        result2 = self.df.groupby([self.df['A'], self.df['B']],
                                  as_index=False).mean()
        self.assertEquals(result.index.names, ['A', 'B'])
        self.assert_('A' in result2)
        self.assert_('B' in result2)

    def test_groupby_nonstring_columns(self):
        df = DataFrame([np.arange(10) for x in range(10)])
        grouped = df.groupby(0)
        result = grouped.mean()
        expected = df.groupby(df[0]).mean()
        del expected[0]
        assert_frame_equal(result, expected)

    def test_cython_grouper_series_bug_noncontig(self):
        arr = np.empty((100, 100))
        arr.fill(np.nan)
        obj = Series(arr[:, 0], index=range(100))
        inds = np.tile(range(10), 10)

        result = obj.groupby(inds).agg(Series.median)
        self.assert_(result.isnull().all())

    def test_series_grouper_noncontig_index(self):
        index = Index([tm.rands(10) for _ in xrange(100)])

        values = Series(np.random.randn(50), index=index[::2])
        labels = np.random.randint(0, 5, 50)

        # it works!
        grouped = values.groupby(labels)

        # accessing the index elements causes segfault
        f = lambda x: len(set(map(id, x.index)))
        grouped.agg(f)

    def test_convert_objects_leave_decimal_alone(self):

        from decimal import Decimal

        s = Series(range(5))
        labels = np.array(['a', 'b', 'c', 'd', 'e'], dtype='O')

        def convert_fast(x):
            return Decimal(str(x.mean()))

        def convert_force_pure(x):
            # base will be length 0
            assert(len(x.base) > 0)
            return Decimal(str(x.mean()))

        grouped = s.groupby(labels)

        result = grouped.agg(convert_fast)
        self.assert_(result.dtype == np.object_)
        self.assert_(isinstance(result[0], Decimal))

        result = grouped.agg(convert_force_pure)
        self.assert_(result.dtype == np.object_)
        self.assert_(isinstance(result[0], Decimal))

    def test_groupby_list_infer_array_like(self):
        result = self.df.groupby(list(self.df['A'])).mean()
        expected = self.df.groupby(self.df['A']).mean()
        assert_frame_equal(result, expected, check_names=False)

        self.assertRaises(Exception, self.df.groupby, list(self.df['A'][:-1]))

        # pathological case of ambiguity
        df = DataFrame({'foo': [0, 1], 'bar': [3, 4],
                        'val': np.random.randn(2)})

        result = df.groupby(['foo', 'bar']).mean()
        expected = df.groupby([df['foo'], df['bar']]).mean()[['val']]

    def test_dictify(self):
        dict(iter(self.df.groupby('A')))
        dict(iter(self.df.groupby(['A', 'B'])))
        dict(iter(self.df['C'].groupby(self.df['A'])))
        dict(iter(self.df['C'].groupby([self.df['A'], self.df['B']])))
        dict(iter(self.df.groupby('A')['C']))
        dict(iter(self.df.groupby(['A', 'B'])['C']))

    def test_sparse_friendly(self):
        sdf = self.df[['C', 'D']].to_sparse()
        panel = tm.makePanel()
        tm.add_nans(panel)

        def _check_work(gp):
            gp.mean()
            gp.agg(np.mean)
            dict(iter(gp))

        # it works!
        _check_work(sdf.groupby(lambda x: x // 2))
        _check_work(sdf['C'].groupby(lambda x: x // 2))
        _check_work(sdf.groupby(self.df['A']))

        # do this someday
        # _check_work(panel.groupby(lambda x: x.month, axis=1))

    def test_panel_groupby(self):
        self.panel = tm.makePanel()
        tm.add_nans(self.panel)
        grouped = self.panel.groupby({'ItemA': 0, 'ItemB': 0, 'ItemC': 1},
                                     axis='items')
        agged = grouped.mean()
        agged2 = grouped.agg(lambda x: x.mean('items'))

        tm.assert_panel_equal(agged, agged2)

        self.assert_(np.array_equal(agged.items, [0, 1]))

        grouped = self.panel.groupby(lambda x: x.month, axis='major')
        agged = grouped.mean()

        self.assert_(np.array_equal(agged.major_axis, [1, 2]))

        grouped = self.panel.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
                                     axis='minor')
        agged = grouped.mean()
        self.assert_(np.array_equal(agged.minor_axis, [0, 1]))

    def test_numpy_groupby(self):
        from pandas.core.groupby import numpy_groupby

        data = np.random.randn(100, 100)
        labels = np.random.randint(0, 10, size=100)

        df = DataFrame(data)

        result = df.groupby(labels).sum().values
        expected = numpy_groupby(data, labels)
        assert_almost_equal(result, expected)

        result = df.groupby(labels, axis=1).sum().values
        expected = numpy_groupby(data, labels, axis=1)
        assert_almost_equal(result, expected)

    def test_groupby_2d_malformed(self):
        d = DataFrame(index=range(2))
        d['group'] = ['g1', 'g2']
        d['zeros'] = [0, 0]
        d['ones'] = [1, 1]
        d['label'] = ['l1', 'l2']
        tmp = d.groupby(['group']).mean()
        res_values = np.array([[0., 1.], [0., 1.]])
        self.assert_(np.array_equal(tmp.columns, ['zeros', 'ones']))
        self.assert_(np.array_equal(tmp.values, res_values))

    def test_int32_overflow(self):
        B = np.concatenate((np.arange(10000), np.arange(10000),
                            np.arange(5000)))
        A = np.arange(25000)
        df = DataFrame({'A': A, 'B': B,
                        'C': A, 'D': B,
                        'E': np.random.randn(25000)})

        left = df.groupby(['A', 'B', 'C', 'D']).sum()
        right = df.groupby(['D', 'C', 'B', 'A']).sum()
        self.assert_(len(left) == len(right))

    def test_int64_overflow(self):
        B = np.concatenate((np.arange(1000), np.arange(1000),
                            np.arange(500)))
        A = np.arange(2500)
        df = DataFrame({'A': A, 'B': B,
                        'C': A, 'D': B,
                        'E': A, 'F': B,
                        'G': A, 'H': B,
                        'values': np.random.randn(2500)})

        lg = df.groupby(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
        rg = df.groupby(['H', 'G', 'F', 'E', 'D', 'C', 'B', 'A'])

        left = lg.sum()['values']
        right = rg.sum()['values']

        exp_index, _ = left.index.sortlevel(0)
        self.assert_(left.index.equals(exp_index))

        exp_index, _ = right.index.sortlevel(0)
        self.assert_(right.index.equals(exp_index))

        tups = map(tuple, df[['A', 'B', 'C', 'D',
                              'E', 'F', 'G', 'H']].values)
        tups = com._asarray_tuplesafe(tups)
        expected = df.groupby(tups).sum()['values']

        for k, v in expected.iteritems():
            self.assert_(left[k] == right[k[::-1]] == v)
        self.assert_(len(left) == len(right))

    def test_groupby_sort_multi(self):
        df = DataFrame({'a': ['foo', 'bar', 'baz'],
                        'b': [3, 2, 1],
                        'c': [0, 1, 2],
                        'd': np.random.randn(3)})

        tups = map(tuple, df[['a', 'b', 'c']].values)
        tups = com._asarray_tuplesafe(tups)
        result = df.groupby(['a', 'b', 'c'], sort=True).sum()
        self.assert_(np.array_equal(result.index.values,
                                    tups[[1, 2, 0]]))

        tups = map(tuple, df[['c', 'a', 'b']].values)
        tups = com._asarray_tuplesafe(tups)
        result = df.groupby(['c', 'a', 'b'], sort=True).sum()
        self.assert_(np.array_equal(result.index.values, tups))

        tups = map(tuple, df[['b', 'c', 'a']].values)
        tups = com._asarray_tuplesafe(tups)
        result = df.groupby(['b', 'c', 'a'], sort=True).sum()
        self.assert_(np.array_equal(result.index.values,
                                    tups[[2, 1, 0]]))

        df = DataFrame({'a': [0, 1, 2, 0, 1, 2],
                        'b': [0, 0, 0, 1, 1, 1],
                        'd': np.random.randn(6)})
        grouped = df.groupby(['a', 'b'])['d']
        result = grouped.sum()
        _check_groupby(df, result, ['a', 'b'], 'd')

    def test_intercept_builtin_sum(self):
        import __builtin__
        s = Series([1., 2., np.nan, 3.])
        grouped = s.groupby([0, 1, 2, 2])

        result = grouped.agg(__builtin__.sum)
        result2 = grouped.apply(__builtin__.sum)
        expected = grouped.sum()
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    def test_column_select_via_attr(self):
        result = self.df.groupby('A').C.sum()
        expected = self.df.groupby('A')['C'].sum()
        assert_series_equal(result, expected)

        self.df['mean'] = 1.5
        result = self.df.groupby('A').mean()
        expected = self.df.groupby('A').agg(np.mean)
        assert_frame_equal(result, expected)

    def test_rank_apply(self):
        lev1 = np.array([rands(10) for _ in xrange(100)], dtype=object)
        lev2 = np.array([rands(10) for _ in xrange(130)], dtype=object)
        lab1 = np.random.randint(0, 100, size=500)
        lab2 = np.random.randint(0, 130, size=500)

        df = DataFrame({'value': np.random.randn(500),
                        'key1': lev1.take(lab1),
                        'key2': lev2.take(lab2)})

        result = df.groupby(['key1', 'key2']).value.rank()

        expected = []
        for key, piece in df.groupby(['key1', 'key2']):
            expected.append(piece.value.rank())
        expected = concat(expected, axis=0)
        expected = expected.reindex(result.index)

        assert_series_equal(result, expected)

    def test_dont_clobber_name_column(self):
        df = DataFrame({'key': ['a', 'a', 'a', 'b', 'b', 'b'],
                        'name': ['foo', 'bar', 'baz'] * 2})

        result = df.groupby('key').apply(lambda x: x)
        assert_frame_equal(result, df)

    def test_skip_group_keys(self):
        from pandas import concat

        tsf = tm.makeTimeDataFrame()

        grouped = tsf.groupby(lambda x: x.month, group_keys=False)
        result = grouped.apply(lambda x: x.sort_index(by='A')[:3])

        pieces = []
        for key, group in grouped:
            pieces.append(group.sort_index(by='A')[:3])

        expected = concat(pieces)
        assert_frame_equal(result, expected)

        grouped = tsf['A'].groupby(lambda x: x.month, group_keys=False)
        result = grouped.apply(lambda x: x.order()[:3])

        pieces = []
        for key, group in grouped:
            pieces.append(group.order()[:3])

        expected = concat(pieces)
        assert_series_equal(result, expected)

    def test_no_nonsense_name(self):
        # GH #995
        s = self.frame['C'].copy()
        s.name = None

        result = s.groupby(self.frame['A']).agg(np.sum)
        self.assert_(result.name is None)

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

        def func(ser):
            if ser.dtype == np.object:
                raise TypeError
            else:
                return ser.sum()
        result = grouped.aggregate(func)
        exp_grouped = self.three_group.ix[:, self.three_group.columns != 'C']
        expected = exp_grouped.groupby(['A', 'B']).aggregate(func)
        assert_frame_equal(result, expected)

    def test_multifunc_sum_bug(self):
        # GH #1065
        x = DataFrame(np.arange(9).reshape(3, 3))
        x['test'] = 0
        x['fl'] = [1.3, 1.5, 1.6]

        grouped = x.groupby('test')
        result = grouped.agg({'fl': 'sum', 2: 'size'})
        self.assert_(result['fl'].dtype == np.float64)

    def test_handle_dict_return_value(self):
        def f(group):
            return {'min': group.min(), 'max': group.max()}

        def g(group):
            return Series({'min': group.min(), 'max': group.max()})

        result = self.df.groupby('A')['C'].apply(f)
        expected = self.df.groupby('A')['C'].apply(g)

        self.assert_(isinstance(result, Series))
        assert_series_equal(result, expected)

    def test_getitem_list_of_columns(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),
                        'E': np.random.randn(8)})

        result = df.groupby('A')[['C', 'D']].mean()
        result2 = df.groupby('A')['C', 'D'].mean()
        result3 = df.groupby('A')[df.columns[2:4]].mean()

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

        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)
        assert_frame_equal(result3, expected)

    def test_agg_multiple_functions_maintain_order(self):
        # GH #610
        funcs = [('mean', np.mean), ('max', np.max), ('min', np.min)]
        result = self.df.groupby('A')['C'].agg(funcs)
        exp_cols = ['mean', 'max', 'min']

        self.assert_(np.array_equal(result.columns, exp_cols))

    def test_multiple_functions_tuples_and_non_tuples(self):
        # #1359

        funcs = [('foo', 'mean'), 'std']
        ex_funcs = [('foo', 'mean'), ('std', 'std')]

        result = self.df.groupby('A')['C'].agg(funcs)
        expected = self.df.groupby('A')['C'].agg(ex_funcs)
        assert_frame_equal(result, expected)

        result = self.df.groupby('A').agg(funcs)
        expected = self.df.groupby('A').agg(ex_funcs)
        assert_frame_equal(result, expected)

    def test_agg_multiple_functions_too_many_lambdas(self):
        grouped = self.df.groupby('A')
        funcs = ['mean', lambda x: x.mean(), lambda x: x.std()]

        self.assertRaises(SpecificationError, grouped.agg, funcs)

    def test_more_flexible_frame_multi_function(self):
        from pandas import concat
        from pandas.util.compat import OrderedDict

        grouped = self.df.groupby('A')

        exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]]))
        exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))

        expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
        expected = expected.swaplevel(0, 1, axis=1).sortlevel(0, axis=1)

        d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
        result = grouped.aggregate(d)

        assert_frame_equal(result, expected)

        # be careful
        result = grouped.aggregate(OrderedDict([['C', np.mean],
                                                ['D', [np.mean, np.std]]]))
        expected = grouped.aggregate(OrderedDict([['C', np.mean],
                                                  ['D', [np.mean, np.std]]]))
        assert_frame_equal(result, expected)

        def foo(x):
            return np.mean(x)

        def bar(x):
            return np.std(x, ddof=1)
        d = OrderedDict([['C', np.mean],
                         ['D', OrderedDict([['foo', np.mean],
                                            ['bar', np.std]])]])
        result = grouped.aggregate(d)

        d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]])
        expected = grouped.aggregate(d)

        assert_frame_equal(result, expected)

    def test_multi_function_flexible_mix(self):
        # GH #1268
        from pandas.util.compat import OrderedDict
        grouped = self.df.groupby('A')

        d = OrderedDict([['C', OrderedDict([['foo', 'mean'],
                                            [
                                            'bar', 'std']])],
                         ['D', 'sum']])
        result = grouped.aggregate(d)
        d2 = OrderedDict([['C', OrderedDict([['foo', 'mean'],
                                             [
                                             'bar', 'std']])],
                          ['D', ['sum']]])
        result2 = grouped.aggregate(d2)

        d3 = OrderedDict([['C', OrderedDict([['foo', 'mean'],
                                             [
                                             'bar', 'std']])],
                          ['D', {'sum': 'sum'}]])
        expected = grouped.aggregate(d3)

        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_set_group_name(self):
        def f(group):
            assert group.name is not None
            return group

        def freduce(group):
            assert group.name is not None
            return group.sum()

        def foo(x):
            return freduce(x)

        def _check_all(grouped):
            # make sure all these work
            grouped.apply(f)
            grouped.aggregate(freduce)
            grouped.aggregate({'C': freduce, 'D': freduce})
            grouped.transform(f)

            grouped['C'].apply(f)
            grouped['C'].aggregate(freduce)
            grouped['C'].aggregate([freduce, foo])
            grouped['C'].transform(f)

        _check_all(self.df.groupby('A'))
        _check_all(self.df.groupby(['A', 'B']))

    def test_no_dummy_key_names(self):
        # GH #1291

        result = self.df.groupby(self.df['A'].values).sum()
        self.assert_(result.index.name is None)

        result = self.df.groupby([self.df['A'].values,
                                  self.df['B'].values]).sum()
        self.assert_(result.index.names == [None, None])

    def test_groupby_categorical(self):
        levels = ['foo', 'bar', 'baz', 'qux']
        labels = np.random.randint(0, 4, size=100)

        cats = Categorical(labels, levels, name='myfactor')

        data = DataFrame(np.random.randn(100, 4))

        result = data.groupby(cats).mean()

        expected = data.groupby(np.asarray(cats)).mean()
        expected = expected.reindex(levels)
        expected.index.name = 'myfactor'

        assert_frame_equal(result, expected)
        self.assert_(result.index.name == cats.name)

        grouped = data.groupby(cats)
        desc_result = grouped.describe()

        idx = cats.labels.argsort()
        ord_labels = np.asarray(cats).take(idx)
        ord_data = data.take(idx)
        expected = ord_data.groupby(ord_labels, sort=False).describe()
        expected.index.names = ['myfactor', None]
        assert_frame_equal(desc_result, expected)

    def test_groupby_groups_datetimeindex(self):
        # #1430
        from pandas.tseries.api import DatetimeIndex
        periods = 1000
        ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
        df = DataFrame({'high': np.arange(periods),
                        'low': np.arange(periods)}, index=ind)
        grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))

        # it works!
        groups = grouped.groups
        self.assert_(isinstance(groups.keys()[0], datetime))

    def test_groupby_reindex_inside_function(self):
        from pandas.tseries.api import DatetimeIndex

        periods = 1000
        ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
        df = DataFrame({'high': np.arange(
            periods), 'low': np.arange(periods)}, index=ind)

        def agg_before(hour, func, fix=False):
            """
                Run an aggregate func on the subset of data.
            """
            def _func(data):
                d = data.select(lambda x: x.hour < 11).dropna()
                if fix:
                    data[data.index[0]]
                if len(d) == 0:
                    return None
                return func(d)
            return _func

        def afunc(data):
            d = data.select(lambda x: x.hour < 11).dropna()
            return np.max(d)

        grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
        closure_bad = grouped.agg({'high': agg_before(11, np.max)})
        closure_good = grouped.agg({'high': agg_before(11, np.max, True)})

        assert_frame_equal(closure_bad, closure_good)

    def test_multiindex_columns_empty_level(self):
        l = [['count', 'values'], ['to filter', '']]
        midx = MultiIndex.from_tuples(l)

        df = DataFrame([[1L, 'A']], columns=midx)

        grouped = df.groupby('to filter').groups
        self.assert_(np.array_equal(grouped['A'], [0]))

        grouped = df.groupby([('to filter', '')]).groups
        self.assert_(np.array_equal(grouped['A'], [0]))

        df = DataFrame([[1L, 'A'], [2L, 'B']], columns=midx)

        expected = df.groupby('to filter').groups
        result = df.groupby([('to filter', '')]).groups
        self.assertEquals(result, expected)

        df = DataFrame([[1L, 'A'], [2L, 'A']], columns=midx)

        expected = df.groupby('to filter').groups
        result = df.groupby([('to filter', '')]).groups
        self.assertEquals(result, expected)

    def test_cython_median(self):
        df = DataFrame(np.random.randn(1000))
        df.values[::2] = np.nan

        labels = np.random.randint(0, 50, size=1000).astype(float)
        labels[::17] = np.nan

        result = df.groupby(labels).median()
        exp = df.groupby(labels).agg(nanops.nanmedian)
        assert_frame_equal(result, exp)

        df = DataFrame(np.random.randn(1000, 5))
        rs = df.groupby(labels).agg(np.median)
        xp = df.groupby(labels).median()
        assert_frame_equal(rs, xp)

    def test_groupby_categorical_no_compress(self):
        data = Series(np.random.randn(9))

        labels = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
        cats = Categorical(labels, [0, 1, 2])

        result = data.groupby(cats).mean()
        exp = data.groupby(labels).mean()
        assert_series_equal(result, exp)

        labels = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3])
        cats = Categorical(labels, [0, 1, 2, 3])

        result = data.groupby(cats).mean()
        exp = data.groupby(labels).mean().reindex(cats.levels)
        assert_series_equal(result, exp)

    def test_groupby_first_datetime64(self):
        df = DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)])
        df[1] = df[1].view('M8[ns]')

        self.assert_(issubclass(df[1].dtype.type, np.datetime64))

        result = df.groupby(level=0).first()
        got_dt = result[1].dtype
        self.assert_(issubclass(got_dt.type, np.datetime64))

        result = df[1].groupby(level=0).first()
        got_dt = result.dtype
        self.assert_(issubclass(got_dt.type, np.datetime64))

    def test_groupby_categorical_unequal_len(self):
        import pandas as pd
        #GH3011
        series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
        bins =  pd.cut(series.dropna(), 4)

        # len(bins) != len(series) here
        self.assertRaises(AssertionError,lambda : series.groupby(bins).mean())

    def test_gb_apply_list_of_unequal_len_arrays(self):

        # GH1738
        df = DataFrame({'group1': ['a','a','a','b','b','b','a','a','a','b','b','b'],
                               'group2': ['c','c','d','d','d','e','c','c','d','d','d','e'],
                               'weight': [1.1,2,3,4,5,6,2,4,6,8,1,2],
                               'value': [7.1,8,9,10,11,12,8,7,6,5,4,3]
        })
        df = df.set_index(['group1', 'group2'])
        df_grouped = df.groupby(level=['group1','group2'], sort=True)

        def noddy(value, weight):
            out = np.array( value * weight ).repeat(3)
            return out

        # the kernel function returns arrays of unequal length
        # pandas sniffs the first one, sees it's an array and not
        # a list, and assumed the rest are of equal length
        # and so tries a vstack

        # don't die
        no_toes = df_grouped.apply(lambda x: noddy(x.value, x.weight ))

def assert_fp_equal(a, b):
    assert((np.abs(a - b) < 1e-12).all())


def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
    tups = map(tuple, df[keys].values)
    tups = com._asarray_tuplesafe(tups)
    expected = f(df.groupby(tups)[field])
    for k, v in expected.iteritems():
        assert(result[k] == v)


def test_decons():
    from pandas.core.groupby import decons_group_index, get_group_index

    def testit(label_list, shape):
        group_index = get_group_index(label_list, shape)
        label_list2 = decons_group_index(group_index, shape)

        for a, b in zip(label_list, label_list2):
            assert(np.array_equal(a, b))

    shape = (4, 5, 6)
    label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100),
                  np.tile([0, 2, 4, 3, 0, 1, 2, 3], 100),
                  np.tile([5, 1, 0, 2, 3, 0, 5, 4], 100)]
    testit(label_list, shape)

    shape = (10000, 10000)
    label_list = [np.tile(np.arange(10000), 5),
                  np.tile(np.arange(10000), 5)]
    testit(label_list, shape)


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