Пример #1
0
    def test_join_multiindex(self):
        index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'],
                                         [1, 2, 3, 1, 2, 3]],
                                        names=['first', 'second'])

        index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'],
                                         [1, 2, 3, 1, 2, 3]],
                                        names=['first', 'second'])

        df1 = DataFrame(data=np.random.randn(6), index=index1,
                        columns=['var X'])
        df2 = DataFrame(data=np.random.randn(6), index=index2,
                        columns=['var Y'])

        df1 = df1.sortlevel(0)
        df2 = df2.sortlevel(0)

        joined = df1.join(df2, how='outer')
        ex_index = index1._tuple_index.union(index2._tuple_index)
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names
        assert_frame_equal(joined, expected)
        self.assertEqual(joined.index.names, index1.names)

        df1 = df1.sortlevel(1)
        df2 = df2.sortlevel(1)

        joined = df1.join(df2, how='outer').sortlevel(0)
        ex_index = index1._tuple_index.union(index2._tuple_index)
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names

        assert_frame_equal(joined, expected)
        self.assertEqual(joined.index.names, index1.names)
Пример #2
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    def test_sort(self):
        frame = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4],
                          columns=['A', 'B', 'C', 'D'])

        # see gh-9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sortlevel()
Пример #3
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    def test_sort(self):
        frame = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4],
                          columns=['A', 'B', 'C', 'D'])

        # see gh-9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sortlevel()
Пример #4
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def practice_five():
    data = Series(np.randomrandn(10),
                  index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],
                         [1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])
    data.index
    data['b']
    data['b':'c']
    data.ix[['b', 'd']]
    data[:, 2]
    data.unstack()
    data.unstack().stack()

    # 重排分级顺序
    frame = DataFrame(np.arange(12).reshape((4, 3)),
                      index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                      columns=[['O', 'O', 'C'], ['G', 'R', 'G']])
    frame.index.names = ['key1', 'key2']
    frame.columns.names = ['state', 'color']
    frame.swaplevel('key1', 'key2')
    frame.sortlevel(1)
    frame.swaplevel(0, 1).sortlevel(0)

    # 根据级别汇总统计
    frame.sum(level='key2')
    frame.sum(level='color', axis=1)

    pass
Пример #5
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    def test_sortlevel(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        res = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(df, res)

        res = df.sortlevel(['A', 'B'], sort_remaining=False)
        assert_frame_equal(df, res)
Пример #6
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    def test_sortlevel(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        res = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(df, res)

        res = df.sortlevel(['A', 'B'], sort_remaining=False)
        assert_frame_equal(df, res)
Пример #7
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    def test_sort_values(self):
        # API for 9816

        # sort_index
        frame = DataFrame(np.arange(16).reshape(4, 4),
                          index=[1, 2, 3, 4],
                          columns=['A', 'B', 'C', 'D'])

        # 9816 deprecated
        with tm.assert_produces_warning(FutureWarning):
            frame.sort(columns='A')
        with tm.assert_produces_warning(FutureWarning):
            frame.sort()

        unordered = frame.ix[[3, 2, 4, 1]]
        expected = unordered.sort_index()

        result = unordered.sort_index(axis=0)
        assert_frame_equal(result, expected)

        unordered = frame.ix[:, [2, 1, 3, 0]]
        expected = unordered.sort_index(axis=1)

        result = unordered.sort_index(axis=1)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, expected)

        # sortlevel
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)

        result = df.sort_index(level='A', sort_remaining=False)
        expected = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(result, expected)

        df = df.T
        result = df.sort_index(level='A', axis=1, sort_remaining=False)
        expected = df.sortlevel('A', axis=1, sort_remaining=False)
        assert_frame_equal(result, expected)

        # MI sort, but no by
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        result = df.sort_index(sort_remaining=False)
        expected = df.sort_index()
        assert_frame_equal(result, expected)
Пример #8
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    def test_sort_values(self):
        # API for 9816

        # sort_index
        frame = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4],
                          columns=['A', 'B', 'C', 'D'])

        # 9816 deprecated
        with tm.assert_produces_warning(FutureWarning):
            frame.sort(columns='A')
        with tm.assert_produces_warning(FutureWarning):
            frame.sort()

        unordered = frame.ix[[3, 2, 4, 1]]
        expected = unordered.sort_index()

        result = unordered.sort_index(axis=0)
        assert_frame_equal(result, expected)

        unordered = frame.ix[:, [2, 1, 3, 0]]
        expected = unordered.sort_index(axis=1)

        result = unordered.sort_index(axis=1)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, expected)

        # sortlevel
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)

        result = df.sort_index(level='A', sort_remaining=False)
        expected = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(result, expected)

        df = df.T
        result = df.sort_index(level='A', axis=1, sort_remaining=False)
        expected = df.sortlevel('A', axis=1, sort_remaining=False)
        assert_frame_equal(result, expected)

        # MI sort, but no by
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        result = df.sort_index(sort_remaining=False)
        expected = df.sort_index()
        assert_frame_equal(result, expected)
Пример #9
0
    def test_sort_index_multiindex(self):
        # GH13496

        # sort rows by specified level of multi-index
        mi = MultiIndex.from_tuples([[2, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)

        result = df.sort_index(level='A', sort_remaining=False)
        expected = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(result, expected)

        # sort columns by specified level of multi-index
        df = df.T
        result = df.sort_index(level='A', axis=1, sort_remaining=False)
        expected = df.sortlevel('A', axis=1, sort_remaining=False)
        assert_frame_equal(result, expected)

        # MI sort, but no level: sort_level has no effect
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        result = df.sort_index(sort_remaining=False)
        expected = df.sort_index()
        assert_frame_equal(result, expected)
Пример #10
0
    def test_sort_index_multiindex(self):
        # GH13496

        # sort rows by specified level of multi-index
        mi = MultiIndex.from_tuples([[2, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)

        result = df.sort_index(level='A', sort_remaining=False)
        expected = df.sortlevel('A', sort_remaining=False)
        assert_frame_equal(result, expected)

        # sort columns by specified level of multi-index
        df = df.T
        result = df.sort_index(level='A', axis=1, sort_remaining=False)
        expected = df.sortlevel('A', axis=1, sort_remaining=False)
        assert_frame_equal(result, expected)

        # MI sort, but no level: sort_level has no effect
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        df = DataFrame([[1, 2], [3, 4]], mi)
        result = df.sort_index(sort_remaining=False)
        expected = df.sort_index()
        assert_frame_equal(result, expected)
Пример #11
0
    def test_join_multiindex(self):
        index1 = MultiIndex.from_arrays(
            [['a', 'a', 'a', 'b', 'b', 'b'], [1, 2, 3, 1, 2, 3]],
            names=['first', 'second'])

        index2 = MultiIndex.from_arrays(
            [['b', 'b', 'b', 'c', 'c', 'c'], [1, 2, 3, 1, 2, 3]],
            names=['first', 'second'])

        df1 = DataFrame(data=np.random.randn(6),
                        index=index1,
                        columns=['var X'])
        df2 = DataFrame(data=np.random.randn(6),
                        index=index2,
                        columns=['var Y'])

        df1 = df1.sortlevel(0)
        df2 = df2.sortlevel(0)

        joined = df1.join(df2, how='outer')
        ex_index = index1._tuple_index.union(index2._tuple_index)
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names
        assert_frame_equal(joined, expected)
        self.assertEqual(joined.index.names, index1.names)

        df1 = df1.sortlevel(1)
        df2 = df2.sortlevel(1)

        joined = df1.join(df2, how='outer').sortlevel(0)
        ex_index = index1._tuple_index.union(index2._tuple_index)
        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
        expected.index.names = index1.names

        assert_frame_equal(joined, expected)
        self.assertEqual(joined.index.names, index1.names)
Пример #12
0
    def _read_panel_table(self, group, where=None):
        from pandas.core.common import _asarray_tuplesafe

        table = getattr(group, 'table')

        # create the selection
        sel = Selection(table, where)
        sel.select()
        fields = table._v_attrs.fields

        columns = _maybe_convert(sel.values['column'],
                                 table._v_attrs.columns_kind)
        index = _maybe_convert(sel.values['index'],
                               table._v_attrs.index_kind)
        # reconstruct
        long_index = MultiIndex.from_arrays([index, columns])
        lp = DataFrame(sel.values['values'], index=long_index,
                       columns=fields)

        if not long_index.has_duplicates:
            lp = lp.sortlevel(level=0)
            wp = lp.to_panel()
        else:
            if not self._quiet:  # pragma: no cover
                print ('Duplicate entries in table, taking most recently '
                       'appended')

            # need a better algorithm
            tuple_index = long_index.get_tuple_index()
            index_map = lib.map_indices_object(tuple_index)

            unique_tuples = lib.fast_unique(tuple_index)
            unique_tuples = _asarray_tuplesafe(unique_tuples)

            indexer = lib.merge_indexer_object(unique_tuples, index_map)

            new_index = long_index.take(indexer)
            new_values = lp.values.take(indexer, axis=0)

            lp = DataFrame(new_values, index=new_index, columns=lp.columns)
            wp = lp.to_panel()

        if sel.column_filter:
            new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
            wp = wp.reindex(minor=new_minor)
        return wp
Пример #13
0
    def _read_panel_table(self, group, where=None):
        from pandas.core.common import _asarray_tuplesafe

        table = getattr(group, 'table')

        # create the selection
        sel = Selection(table, where)
        sel.select()
        fields = table._v_attrs.fields

        columns = _maybe_convert(sel.values['column'],
                                 table._v_attrs.columns_kind)
        index = _maybe_convert(sel.values['index'], table._v_attrs.index_kind)
        # reconstruct
        long_index = MultiIndex.from_arrays([index, columns])
        lp = DataFrame(sel.values['values'], index=long_index, columns=fields)

        if not long_index.has_duplicates:
            lp = lp.sortlevel(level=0)
            wp = lp.to_panel()
        else:
            if not self._quiet:  # pragma: no cover
                print(
                    'Duplicate entries in table, taking most recently '
                    'appended')

            # need a better algorithm
            tuple_index = long_index.get_tuple_index()
            index_map = lib.map_indices_object(tuple_index)

            unique_tuples = lib.fast_unique(tuple_index)
            unique_tuples = _asarray_tuplesafe(unique_tuples)

            indexer = lib.merge_indexer_object(unique_tuples, index_map)

            new_index = long_index.take(indexer)
            new_values = lp.values.take(indexer, axis=0)

            lp = DataFrame(new_values, index=new_index, columns=lp.columns)
            wp = lp.to_panel()

        if sel.column_filter:
            new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
            wp = wp.reindex(minor=new_minor)
        return wp
Пример #14
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    def DBReqStockPool_Pandas(self, strWindCode):
        """
        @return:    df
                    df.empty
        #-------------------------
        @return format:
                                    STOCK_WINDCODE
        IN_DATE     OUT_DATE(if CUR_SING==True: OUT_DATE==Today)
        #-------------------------
        @return eg:
                                    STOCK_WINDCODE
        IN_DATE     OUT_DATE
        2007-01-15  2016-06-08      600997.SH
                    2007-06-29      000725.SZ
        ...               ...             ...
        2016-12-12  2017-01-19      600291.SH
                    2017-01-19      603866.SH
        """
        df = DataFrame()
        if (len(strWindCode) != 9):
            return df

        strSelect = "SELECT S_CON_WINDCODE as STOCK_WINDCODE, S_CON_INDATE as IN_DATE, S_CON_OUTDATE as OUT_DATE, CUR_SIGN FROM WindDB.dbo.AINDEXMEMBERS"
        strSelect += " where S_INFO_WINDCODE = '" + strWindCode + "' order by CUR_SIGN desc, IN_DATE asc"

        conn = pymssql.connect(host=self.__strHost,
                               user=self.__strUser,
                               password=self.__strPwd,
                               database=self.__strDB,
                               charset="utf8")
        df = pd.read_sql_query(strSelect,
                               conn,
                               parse_dates=['IN_DATE', 'OUT_DATE'])
        conn.close()

        dtToday = datetime.date.today()
        for index, row in df.iterrows():
            if (row['CUR_SIGN'] == 1.0):
                df['OUT_DATE'][index] = dtToday
            else:
                break
        del df['CUR_SIGN']
        df = df.set_index(['IN_DATE', 'OUT_DATE'])
        df = df.sortlevel([0, 1])
        return df
Пример #15
0
class TestMultiLevel(unittest.TestCase):
    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3),
                               index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       labels=[[0, 1, 2, 3]],
                                       names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby(
            [lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.levels = [
            lev.astype('i8') for lev in self.ymd.index.levels
        ]
        self.ymd.index.names = ['year', 'month', 'day']

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a['A'].append(b['A'])
        tm.assert_series_equal(result, self.frame['A'])

    def test_reindex_level(self):
        # axis=0
        month_sums = self.ymd.sum(level='month')
        result = month_sums.reindex(self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum)

        assert_frame_equal(result, expected)

        # Series
        result = month_sums['A'].reindex(self.ymd.index, level=1)
        expected = self.ymd['A'].groupby(level='month').transform(np.sum)
        assert_series_equal(result, expected)

        # axis=1
        month_sums = self.ymd.T.sum(axis=1, level='month')
        result = month_sums.reindex(columns=self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum).T
        assert_frame_equal(result, expected)

    def test_binops_level(self):
        def _check_op(opname):
            op = getattr(DataFrame, opname)
            month_sums = self.ymd.sum(level='month')
            result = op(self.ymd, month_sums, level='month')
            broadcasted = self.ymd.groupby(level='month').transform(np.sum)
            expected = op(self.ymd, broadcasted)
            assert_frame_equal(result, expected)

            # Series
            op = getattr(Series, opname)
            result = op(self.ymd['A'], month_sums['A'], level='month')
            broadcasted = self.ymd['A'].groupby(level='month').transform(
                np.sum)
            expected = op(self.ymd['A'], broadcasted)
            assert_series_equal(result, expected)

        _check_op('sub')
        _check_op('add')
        _check_op('mul')
        _check_op('div')

    def test_pickle(self):
        import cPickle

        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df['foo', 'one']
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ('foo', 'four'))
        self.assertRaises(KeyError, df.__getitem__, 'foobar')

    def test_series_getitem(self):
        s = self.ymd['A']

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s.reindex(s.index[42:65])
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s.reindex(s.index[49:51])
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_getitem_corner(self):
        s = self.ymd['A']

        # don't segfault, GH #495
        # out of bounds access
        self.assertRaises(IndexError, s.__getitem__, len(self.ymd))

        # generator
        result = s[(x > 0 for x in s)]
        expected = s[s > 0]
        assert_series_equal(result, expected)

    def test_series_setitem(self):
        s = self.ymd['A']

        s[2000, 3] = np.nan
        self.assert_(isnull(s.values[42:65]).all())
        self.assert_(notnull(s.values[:42]).all())
        self.assert_(notnull(s.values[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_frame_getitem_setitem_slice(self):
        # getitem
        result = self.frame.ix[:4]
        expected = self.frame[:4]
        assert_frame_equal(result, expected)

        # setitem
        cp = self.frame.copy()
        cp.ix[:4] = 0

        self.assert_((cp.values[:4] == 0).all())
        self.assert_((cp.values[4:] != 0).all())

    def test_frame_getitem_setitem_multislice(self):
        levels = [['t1', 't2'], ['a', 'b', 'c']]
        labels = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 1]]
        midx = MultiIndex(labels=labels, levels=levels, names=[None, 'id'])
        df = DataFrame({'value': [1, 2, 3, 7, 8]}, index=midx)

        result = df.ix[:, 'value']
        assert_series_equal(df['value'], result)

        result = df.ix[1:3, 'value']
        assert_series_equal(df['value'][1:3], result)

        result = df.ix[:, :]
        assert_frame_equal(df, result)

        result = df
        df.ix[:, 'value'] = 10
        result['value'] = 10
        assert_frame_equal(df, result)

        df.ix[:, :] = 10
        assert_frame_equal(df, result)

    def test_getitem_tuple_plus_slice(self):
        # GH #671
        df = DataFrame({
            'a': range(10),
            'b': range(10),
            'c': np.random.randn(10),
            'd': np.random.randn(10)
        })

        idf = df.set_index(['a', 'b'])

        result = idf.ix[(0, 0), :]
        expected = idf.ix[0, 0]
        expected2 = idf.xs((0, 0))

        assert_series_equal(result, expected)
        assert_series_equal(result, expected2)

    def test_xs(self):
        xs = self.frame.xs(('bar', 'two'))
        xs2 = self.frame.ix[('bar', 'two')]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs('foo')
        result2 = self.frame.ix['foo']
        expected = self.frame.T['foo'].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_xs_level(self):
        result = self.frame.xs('two', level='second')
        expected = self.frame[self.frame.index.get_level_values(1) == 'two']
        expected.index = expected.index.droplevel(1)

        assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([('x', 'y', 'z'), ('a', 'b', 'c'),
                                        ('p', 'q', 'r')])
        df = DataFrame(np.random.randn(3, 5), index=index)
        result = df.xs('c', level=2)
        expected = df[1:2]
        expected.index = expected.index.droplevel(2)
        assert_frame_equal(result, expected)

    def test_xs_level_multiple(self):
        from pandas import read_table
        from StringIO import StringIO
        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep='\s+')

        result = df.xs(('a', 4), level=['one', 'four'])
        expected = df.xs('a').xs(4, level='four')
        assert_frame_equal(result, expected)

    def test_xs_level0(self):
        from pandas import read_table
        from StringIO import StringIO
        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep='\s+')

        result = df.xs('a', level=0)
        expected = df.xs('a')
        self.assertEqual(len(result), 2)
        assert_frame_equal(result, expected)

    def test_xs_level_series(self):
        s = self.frame['A']
        result = s[:, 'two']
        expected = self.frame.xs('two', level=1)['A']
        assert_series_equal(result, expected)

        s = self.ymd['A']
        result = s[2000, 5]
        expected = self.ymd.ix[2000, 5]['A']
        assert_series_equal(result, expected)

        # not implementing this for now

        self.assertRaises(TypeError, s.__getitem__, (2000, slice(3, 4)))

        # result = s[2000, 3:4]
        # lv =s.index.get_level_values(1)
        # expected = s[(lv == 3) | (lv == 4)]
        # expected.index = expected.index.droplevel(0)
        # assert_series_equal(result, expected)

        # can do this though

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)),
                   index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df['foo']
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df['bar']
        result2 = df.ix[:, 'bar']

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_setitem_slice_integers(self):
        index = MultiIndex(levels=[[0, 1, 2], [0, 2]],
                           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        frame = DataFrame(np.random.randn(len(index), 4),
                          index=index,
                          columns=['a', 'b', 'c', 'd'])
        res = frame.ix[1:2]
        exp = frame.reindex(frame.index[2:])
        assert_frame_equal(res, exp)

        frame.ix[1:2] = 7
        self.assert_((frame.ix[1:2] == 7).values.all())

        series = Series(np.random.randn(len(index)), index=index)

        res = series.ix[1:2]
        exp = series.reindex(series.index[2:])
        assert_series_equal(res, exp)

        series.ix[1:2] = 7
        self.assert_((series.ix[1:2] == 7).values.all())

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_getitem_slice_not_sorted(self):
        df = self.frame.sortlevel(1).T

        # buglet with int typechecking
        result = df.ix[:, :np.int32(3)]
        expected = df.reindex(columns=df.columns[:3])
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft['foo', 'two']
        dft['foo', 'two'] = s > s.median()
        assert_series_equal(dft['foo', 'two'], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[('foo', 'two')])
        assert_series_equal(reindexed['foo', 'two'], s > s.median())

    def test_frame_setitem_ix(self):
        self.frame.ix[('bar', 'two'), 'B'] = 5
        self.assertEquals(self.frame.ix[('bar', 'two'), 'B'], 5)

        # with integer labels
        df = self.frame.copy()
        df.columns = range(3)
        df.ix[('bar', 'two'), 1] = 7
        self.assertEquals(df.ix[('bar', 'two'), 1], 7)

    def test_fancy_slice_partial(self):
        result = self.frame.ix['bar':'baz']
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000, 2):(2000, 4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame['A'].sortlevel(0)
        self.assertRaises(Exception, self.frame.reset_index()['A'].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_delevel_infer_dtype(self):
        tuples = [
            tuple
            for tuple in cart_product(['foo', 'bar'], [10, 20], [1.0, 1.1])
        ]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3),
                       columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        self.assert_(com.is_integer_dtype(deleveled['prm1']))
        self.assert_(com.is_float_dtype(deleveled['prm2']))

    def test_reset_index_with_drop(self):
        deleveled = self.ymd.reset_index(drop=True)
        self.assertEquals(len(deleveled.columns), len(self.ymd.columns))

        deleveled = self.series.reset_index()
        self.assert_(isinstance(deleveled, DataFrame))
        self.assert_(
            len(deleveled.columns) == len(self.series.index.levels) + 1)

        deleveled = self.series.reset_index(drop=True)
        self.assert_(isinstance(deleveled, Series))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ['first', 'second']
        result = self.frame.sortlevel(level='second')
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df['foo'] = 'bar'
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(['foo'], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft['foo', 'three'] = 'bar'

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
                           sorted_after.drop([('foo', 'three')], axis=1))

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype('i8')
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame['D'] = 'foo'
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ['A', 'B', 'C'])

    def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'],
                                   ['one', 'two', 'three', 'four']],
                           labels=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame['A'][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0],
                             columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked['foo'], df['foo'].stack())
        self.assert_(stacked['bar'].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame({
            'state': ['naive', 'naive', 'naive', 'activ', 'activ', 'activ'],
            'exp': ['a', 'b', 'b', 'b', 'a', 'a'],
            'barcode': [1, 2, 3, 4, 1, 3],
            'v': ['hi', 'hi', 'bye', 'bye', 'bye', 'peace'],
            'extra':
            np.arange(6.)
        })

        result = df.groupby(['state', 'exp', 'barcode', 'v']).apply(len)

        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked,
                            result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, 'first')
        self.assertEquals(unstacked.columns.names, ['exp', 'second'])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack('second')
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack('second')
        result = unstacked.stack('exp')
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack('exp')
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(['year', 'month'])
        expected = self.ymd.unstack('year').unstack('month')
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names, expected.columns.names)

        # series
        s = self.ymd['A']
        s_unstacked = s.unstack(['year', 'month'])
        assert_frame_equal(s_unstacked, expected['A'])

        restacked = unstacked.stack(['year', 'month'])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1)
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1)
        assert_frame_equal(unstacked, expected)

    def test_groupby_transform(self):
        s = self.frame['A']
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_groupby_corner(self):
        midx = MultiIndex(levels=[['foo'], ['bar'], ['baz']],
                          labels=[[0], [0], [0]],
                          names=['one', 'two', 'three'])
        df = DataFrame([np.random.rand(4)],
                       columns=['a', 'b', 'c', 'd'],
                       index=midx)
        # should work
        df.groupby(level='three')

    def test_join(self):
        a = self.frame.ix[:5, ['A']]
        b = self.frame.ix[2:, ['B', 'C']]

        joined = a.join(b, how='outer').reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame['A'].swaplevel(0, 1)
        swapped2 = self.frame['A'].swaplevel('first', 'second')
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel('second', 'first')
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel('first', 'second', axis=1)
        exp = self.frame.swaplevel('first', 'second').T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({'ItemA': self.frame, 'ItemB': self.frame * 2})

        result = panel.swaplevel(0, 1, axis='major')
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_reorder_levels(self):
        result = self.ymd.reorder_levels(['month', 'day', 'year'])
        expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
        assert_frame_equal(result, expected)

        result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
        expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
        assert_series_equal(result, expected)

        result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
        expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
        assert_frame_equal(result, expected)

        self.assertRaises(Exception, self.ymd.index.reorder_levels, [1, 2, 3])

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        x = Series(data=[1, 2, 3],
                   index=MultiIndex.from_tuples([("A", 1), ("A", 2),
                                                 ("B", 3)]))

        y = Series(data=[4, 5, 6],
                   index=MultiIndex.from_tuples([("Z", 1), ("Z", 2),
                                                 ("B", 3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df['foo'].values[:] = 0
        self.assert_((df['foo'].values == 0).all())

        # but not if it's mixed-type
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(0, axis=1)
        df['foo']['one'] = 2
        self.assert_((df['foo', 'one'] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df['foo']
        result2 = df.ix[:, 'foo']
        expected = df.reindex(columns=df.columns[arrays[0] == 'foo'])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs('foo')
        result2 = df.ix['foo']
        expected = df.reindex(df.index[arrays[0] == 'foo'])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s['qux']
        result2 = s.ix['qux']
        expected = s[arrays[0] == 'qux']
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    AGG_FUNCTIONS = [
        'sum', 'prod', 'min', 'max', 'median', 'mean', 'skew', 'mad', 'std',
        'var'
    ]

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS, range(2),
                                              [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
                                                    range(2), range(2),
                                                    [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            aggf = lambda x: getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level,
                                           axis=axis,
                                           skipna=skipna)

            # for good measure, groupby detail
            level_index = frame._get_axis(axis).levels[level]

            self.assert_(leftside._get_axis(axis).equals(level_index))
            self.assert_(rightside._get_axis(axis).equals(level_index))

            assert_frame_equal(leftside, rightside)

    def test_frame_series_agg_multiple_levels(self):
        result = self.ymd.sum(level=['year', 'month'])
        expected = self.ymd.groupby(level=['year', 'month']).sum()
        assert_frame_equal(result, expected)

        result = self.ymd['A'].sum(level=['year', 'month'])
        expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
        assert_series_equal(result, expected)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'),
                                        ('bar', 'one'), ('bar', 'two')])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df['Totals', ''] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd['A'].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd['A'].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df['A'].ix[2000, 4] = 1
        exp['A'].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df['A'].ix[14] = 5
        self.assertEquals(df['A'][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd['E'] = 'foo'
        self.ymd['F'] = 2

        unstacked = self.ymd.unstack('month')
        self.assert_(unstacked['A', 1].dtype == np.float64)
        self.assert_(unstacked['E', 1].dtype == np.object_)
        self.assert_(unstacked['F', 1].dtype == np.float64)

    def test_getitem_lowerdim_corner(self):
        self.assertRaises(KeyError, self.frame.ix.__getitem__,
                          (('bar', 'three'), 'B'))

        self.assertRaises(KeyError, self.frame.ix.__setitem__,
                          (('bar', 'three'), 'B'), 0)

    #----------------------------------------------------------------------
    # AMBIGUOUS CASES!

    def test_partial_ix_missing(self):
        raise nose.SkipTest

        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]['A']
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    def test_fancy_2d(self):
        raise nose.SkipTest

        result = self.frame.ix['foo', 'B']
        expected = self.frame.xs('foo')['B']
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix['B', 'foo']
        expected = ft.xs('B')['foo']
        assert_series_equal(result, expected)

    #----------------------------------------------------------------------

    def test_to_html(self):
        self.ymd.columns.name = 'foo'
        self.ymd.to_html()
        self.ymd.T.to_html()

    def test_level_with_tuples(self):
        index = MultiIndex(levels=[[('foo', 'bar', 0), ('foo', 'baz', 0),
                                    ('foo', 'qux', 0)], [0, 1]],
                           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar', 0)]
        result2 = series.ix[('foo', 'bar', 0)]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        self.assertRaises(KeyError, series.__getitem__, (('foo', 'bar', 0), 2))

        result = frame.ix[('foo', 'bar', 0)]
        result2 = frame.xs(('foo', 'bar', 0))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        index = MultiIndex(levels=[[('foo', 'bar'), ('foo', 'baz'),
                                    ('foo', 'qux')], [0, 1]],
                           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar')]
        result2 = series.ix[('foo', 'bar')]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        result = frame.ix[('foo', 'bar')]
        result2 = frame.xs(('foo', 'bar'))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_int_series_slicing(self):
        s = self.ymd['A']
        result = s[5:]
        expected = s.reindex(s.index[5:])
        assert_series_equal(result, expected)

        exp = self.ymd['A'].copy()
        s[5:] = 0
        exp.values[5:] = 0
        self.assert_(np.array_equal(s.values, exp.values))

        result = self.ymd[5:]
        expected = self.ymd.reindex(s.index[5:])
        assert_frame_equal(result, expected)

    def test_mixed_depth_get(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df['a']
        expected = df['a', '', '']
        assert_series_equal(result, expected)
        self.assertEquals(result.name, 'a')

        result = df['routine1', 'result1']
        expected = df['routine1', 'result1', '']
        assert_series_equal(result, expected)
        self.assertEquals(result.name, ('routine1', 'result1'))

    def test_mixed_depth_insert(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df.copy()
        expected = df.copy()
        result['b'] = [1, 2, 3, 4]
        expected['b', '', ''] = [1, 2, 3, 4]
        assert_frame_equal(result, expected)

    def test_mixed_depth_drop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df.drop('a', axis=1)
        expected = df.drop([('a', '', '')], axis=1)
        assert_frame_equal(expected, result)

        result = df.drop(['top'], axis=1)
        expected = df.drop([('top', 'OD', 'wx')], axis=1)
        expected = expected.drop([('top', 'OD', 'wy')], axis=1)
        assert_frame_equal(expected, result)

    def test_mixed_depth_pop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop('a')
        expected = df2.pop(('a', '', ''))
        assert_series_equal(expected, result)
        assert_frame_equal(df1, df2)
        self.assertEquals(result.name, 'a')

        expected = df1['top']
        df1 = df1.drop(['top'], axis=1)
        result = df2.pop('top')
        assert_frame_equal(expected, result)
        assert_frame_equal(df1, df2)
Пример #16
0
print ser
print ser[1]  # calling upper index level
print ser[:, 'a']  # calling lower index levels

# Convert multiple index series to dataframe
dframe = ser.unstack()
print dframe

# Dataframes with multiple level indexes (like higher level column and row labels)
dframe2 = DataFrame(np.arange(16).reshape(4, 4),
                    index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                    columns=[['NYC', 'NYC', "LA", 'SF'],
                             ['cold', 'hot', 'hot', 'cold']])
print dframe2

# Giving index names
dframe2.index.names = ['IND1', 'IND2']
dframe2.columns.names = ['CITIES', 'TEMP']
print dframe2

# Swap index levels
dframe3 = dframe2.swaplevel('CITIES', 'TEMP', axis=1)
print dframe3

# Sort index
print dframe2.sortlevel(1)  # sort by lower level index

# Perform operations on specific level
print dframe2.sum(level='TEMP',
                  axis=1)  # add on temp level, ignoring/lumping cities
Пример #17
0
index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],
[1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])
import numpy as np
data = Series(np.random.randn(10),
index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],
[1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])
data
data.index
data[b]
data['b']
data['b': 'c']
data.ix[['b', 'd']]
# selection in an inner level
data[:, 2]
data.unstack()
data.unstack().stack()
from pandas import DataFrame
frame = DataFrame(np.arange(12).reshape((4, 3)),
index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
columns=[['Ohio', 'Ohio', 'Colorado'],
['Green', 'Red', 'Green']])
frame
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame
# Reordering and Sorting Levels
frame.swaplevel('key1', 'key2')
frame.sortlevel(1)
# Summary Ststistics by Level
frame.sum(level='key2')
Пример #18
0
class TestMultiLevel(unittest.TestCase):

    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       labels=[[0, 1, 2, 3]],
                                       names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.levels = [lev.astype('i8')
                                 for lev in self.ymd.index.levels]
        self.ymd.index.names = ['year', 'month', 'day']

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a['A'].append(b['A'])
        tm.assert_series_equal(result, self.frame['A'])

    def test_reindex_level(self):
        # axis=0
        month_sums = self.ymd.sum(level='month')
        result = month_sums.reindex(self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum)

        assert_frame_equal(result, expected)

        # Series
        result = month_sums['A'].reindex(self.ymd.index, level=1)
        expected = self.ymd['A'].groupby(level='month').transform(np.sum)
        assert_series_equal(result, expected)

        # axis=1
        month_sums = self.ymd.T.sum(axis=1, level='month')
        result = month_sums.reindex(columns=self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum).T
        assert_frame_equal(result, expected)

    def test_binops_level(self):
        def _check_op(opname):
            op = getattr(DataFrame, opname)
            month_sums = self.ymd.sum(level='month')
            result = op(self.ymd, month_sums, level='month')
            broadcasted = self.ymd.groupby(level='month').transform(np.sum)
            expected = op(self.ymd, broadcasted)
            assert_frame_equal(result, expected)

            # Series
            op = getattr(Series, opname)
            result = op(self.ymd['A'], month_sums['A'], level='month')
            broadcasted = self.ymd['A'].groupby(level='month').transform(np.sum)
            expected = op(self.ymd['A'], broadcasted)
            assert_series_equal(result, expected)

        _check_op('sub')
        _check_op('add')
        _check_op('mul')
        _check_op('div')

    def test_pickle(self):
        import cPickle
        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df['foo', 'one']
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ('foo', 'four'))
        self.assertRaises(KeyError, df.__getitem__, 'foobar')

    def test_series_getitem(self):
        s = self.ymd['A']

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s[42:65]
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s[49:51]
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_setitem(self):
        s = self.ymd['A']

        s[2000, 3] = np.nan
        self.assert_(isnull(s[42:65]).all())
        self.assert_(notnull(s[:42]).all())
        self.assert_(notnull(s[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_xs(self):
        xs = self.frame.xs(('bar', 'two'))
        xs2 = self.frame.ix[('bar', 'two')]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs('foo')
        result2 = self.frame.ix['foo']
        expected = self.frame.T['foo'].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_fancy_2d(self):
        result = self.frame.ix['foo', 'B']
        expected = self.frame.xs('foo')['B']
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix['B', 'foo']
        expected = ft.xs('B')['foo']
        assert_series_equal(result, expected)

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)),
                   index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df['foo']
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df['bar']
        result2 = df.ix[:, 'bar']

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_slice_integers(self):
        index = MultiIndex(levels=[[0, 1, 2], [0, 2]],
                           labels=[[0, 0, 1, 1, 2, 2],
                                   [0, 1, 0, 1, 0, 1]])

        frame =  DataFrame(np.random.randn(len(index), 4), index=index,
                           columns=['a', 'b', 'c', 'd'])
        res = frame.ix[1:2]
        exp = frame[2:]
        assert_frame_equal(res, exp)

        series =  Series(np.random.randn(len(index)), index=index)

        res = series.ix[1:2]
        exp = series[2:]
        assert_series_equal(res, exp)

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_getitem_slice_not_sorted(self):
        df = self.frame.sortlevel(1).T

        # buglet with int typechecking
        result = df.ix[:, :np.int32(3)]
        expected = df.reindex(columns=df.columns[:3])
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft['foo', 'two']
        dft['foo', 'two'] = s > s.median()
        assert_series_equal(dft['foo', 'two'], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[('foo', 'two')])
        assert_series_equal(reindexed['foo', 'two'], s > s.median())

    def test_frame_setitem_ix(self):
        self.frame.ix[('bar', 'two'), 'B'] = 5
        self.assertEquals(self.frame.ix[('bar', 'two'), 'B'], 5)

        # with integer labels
        df = self.frame.copy()
        df.columns = range(3)
        df.ix[('bar', 'two'), 1] = 7
        self.assertEquals(df.ix[('bar', 'two'), 1], 7)

    def test_fancy_slice_partial(self):
        result = self.frame.ix['bar':'baz']
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000,2):(2000,4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame['A'].sortlevel(0)
        self.assertRaises(Exception,
                          self.frame.reset_index()['A'].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_delevel_infer_dtype(self):
        tuples = [tuple for tuple in cart_product(['foo', 'bar'],
                                                  [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples,
                                       names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8,3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        self.assert_(com.is_integer_dtype(deleveled['prm1']))
        self.assert_(com.is_float_dtype(deleveled['prm2']))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ['first', 'second']
        result = self.frame.sortlevel(level='second')
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df['foo'] = 'bar'
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(['foo'], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft['foo', 'three'] = 'bar'

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
                           sorted_after.drop([('foo', 'three')], axis=1))

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype('i8')
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame['D'] = 'foo'
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ['A', 'B', 'C'])

    def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'],
                                   ['one', 'two', 'three', 'four']],
                           labels=[[0, 0, 0, 2, 2],
                                   [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame['A'][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0],
                             columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked['foo'], df['foo'].stack())
        self.assert_(stacked['bar'].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame({'state': ['naive','naive','naive',
                                  'activ','activ','activ'],
                        'exp':['a','b','b','b','a','a'],
                        'barcode':[1,2,3,4,1,3],
                        'v':['hi','hi','bye','bye','bye','peace'],
                        'extra': np.arange(6.)})

        result = df.groupby(['state','exp','barcode','v']).apply(len)
        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked,
                            result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, 'first')
        self.assertEquals(unstacked.columns.names, ['exp', 'second'])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack('second')
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack('second')
        result = unstacked.stack('exp')
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack('exp')
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(['year', 'month'])
        expected = self.ymd.unstack('year').unstack('month')
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names,
                          expected.columns.names)

        # series
        s = self.ymd['A']
        s_unstacked = s.unstack(['year', 'month'])
        assert_frame_equal(s_unstacked, expected['A'])

        restacked = unstacked.stack(['year', 'month'])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1)
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1)
        assert_frame_equal(unstacked, expected)

    def test_groupby_transform(self):
        s = self.frame['A']
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_join(self):
        a = self.frame.ix[:5, ['A']]
        b = self.frame.ix[2:, ['B', 'C']]

        joined = a.join(b, how='outer').reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame['A'].swaplevel(0, 1)
        swapped2 = self.frame['A'].swaplevel('first', 'second')
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel('second', 'first')
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel('first', 'second', axis=1)
        exp = self.frame.swaplevel('first', 'second').T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({'ItemA' : self.frame,
                       'ItemB' : self.frame * 2})

        result = panel.swaplevel(0, 1, axis='major')
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_reorder_levels(self):
        result = self.ymd.reorder_levels(['month', 'day', 'year'])
        expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
        assert_frame_equal(result, expected)

        result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
        expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
        assert_series_equal(result, expected)

        result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
        expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
        assert_frame_equal(result, expected)

        self.assertRaises(Exception, self.ymd.index.reorder_levels,
                          [1, 2, 3])

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        x = Series(data=[1,2,3],
                   index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B",3)]))

        y = Series(data=[4,5,6],
                   index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B",3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 1, 0, 1, 1],
                                   [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df['foo'].values[:] = 0
        self.assert_((df['foo'].values == 0).all())

        # but not if it's mixed-type
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(0, axis=1)
        df['foo']['one'] = 2
        self.assert_((df['foo', 'one'] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df['foo']
        result2 = df.ix[:, 'foo']
        expected = df.reindex(columns=df.columns[arrays[0] == 'foo'])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs('foo')
        result2 = df.ix['foo']
        expected = df.reindex(df.index[arrays[0] == 'foo'])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
        ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s['qux']
        result2 = s.ix['qux']
        expected = s[arrays[0] == 'qux']
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
                     'mad', 'std', 'var']

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS,
                                              range(2),
                                              [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
                                                    range(2), range(2),
                                                    [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            aggf = lambda x: getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level, axis=axis,
                                           skipna=skipna)

            # for good measure, groupby detail
            level_index = frame._get_axis(axis).levels[level]

            self.assert_(leftside._get_axis(axis).equals(level_index))
            self.assert_(rightside._get_axis(axis).equals(level_index))

            assert_frame_equal(leftside, rightside)

    def test_frame_series_agg_multiple_levels(self):
        result = self.ymd.sum(level=['year', 'month'])
        expected = self.ymd.groupby(level=['year', 'month']).sum()
        assert_frame_equal(result, expected)

        result = self.ymd['A'].sum(level=['year', 'month'])
        expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
        assert_series_equal(result, expected)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'),
                                        ('bar', 'one'), ('bar', 'two')])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df['Totals', ''] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd['A'].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd['A'].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df['A'].ix[2000, 4] = 1
        exp['A'].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df['A'].ix[14] = 5
        self.assertEquals(df['A'][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd['E'] = 'foo'
        self.ymd['F'] = 2

        unstacked = self.ymd.unstack('month')
        self.assert_(unstacked['A', 1].dtype == np.float64)
        self.assert_(unstacked['E', 1].dtype == np.object_)
        self.assert_(unstacked['F', 1].dtype == np.float64)

    def test_partial_ix_missing(self):
        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]['A']
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    def test_to_html(self):
        self.ymd.columns.name = 'foo'
        self.ymd.to_html()
        self.ymd.T.to_html()
Пример #19
0
class TestMultiLevel(unittest.TestCase):
    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3), index=index, columns=["A", "B", "C"])

        self.single_level = MultiIndex(levels=[["foo", "bar", "baz", "qux"]], labels=[[0, 1, 2, 3]], names=["first"])

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a["A"].append(b["A"])
        tm.assert_series_equal(result, self.frame["A"])

    def test_pickle(self):
        import cPickle

        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[("foo", "one"), ("bar", "one")]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df["foo", "one"]
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ("foo", "four"))
        self.assertRaises(KeyError, df.__getitem__, "foobar")

    def test_series_getitem(self):
        s = self.ymd["A"]

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s[42:65]
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s[49:51]
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_setitem(self):
        s = self.ymd["A"]

        s[2000, 3] = np.nan
        self.assert_(isnull(s[42:65]).all())
        self.assert_(notnull(s[:42]).all())
        self.assert_(notnull(s[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_xs(self):
        xs = self.frame.xs(("bar", "two"))
        xs2 = self.frame.ix[("bar", "two")]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs("foo")
        result2 = self.frame.ix["foo"]
        expected = self.frame.T["foo"].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_fancy_2d(self):
        result = self.frame.ix["foo", "B"]
        expected = self.frame.xs("foo")["B"]
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix["B", "foo"]
        expected = ft.xs("B")["foo"]
        assert_series_equal(result, expected)

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)), index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df["foo"]
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df["bar"]
        result2 = df.ix[:, "bar"]

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft["foo", "two"]
        dft["foo", "two"] = s > s.median()
        assert_series_equal(dft["foo", "two"], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[("foo", "two")])
        assert_series_equal(reindexed["foo", "two"], s > s.median())

    def test_fancy_slice_partial(self):
        result = self.frame.ix["bar":"baz"]
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000, 2):(2000, 4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame["A"].sortlevel(0)
        self.assertRaises(Exception, self.frame.delevel()["A"].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_sortlevel_by_name(self):
        self.frame.index.names = ["first", "second"]
        result = self.frame.sortlevel(level="second")
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df["foo"] = "bar"
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft["foo", "three"] = "bar"

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(
            sorted_before.drop([("foo", "three")], axis=1), sorted_after.drop([("foo", "three")], axis=1)
        )

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

    def test_count_level_corner(self):
        s = self.frame["A"][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0], columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df["foo", "four"] = "foo"
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked["foo"], df["foo"].stack())
        self.assert_(stacked["bar"].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame(
            {
                "state": ["naive", "naive", "naive", "activ", "activ", "activ"],
                "exp": ["a", "b", "b", "b", "a", "a"],
                "barcode": [1, 2, 3, 4, 1, 3],
                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
                "extra": np.arange(6.0),
            }
        )

        result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked, result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, "first")
        self.assertEquals(unstacked.columns.names, [None, "second"])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack("second")
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_groupby_transform(self):
        s = self.frame["A"]
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_join(self):
        a = self.frame.ix[:5, ["A"]]
        b = self.frame.ix[2:, ["B", "C"]]

        joined = a.join(b, how="outer").reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame["A"].swaplevel(0, 1)
        self.assert_(not swapped.index.equals(self.frame.index))

        back = swapped.swaplevel(0, 1)
        self.assert_(back.index.equals(self.frame.index))

    def test_swaplevel_panel(self):
        panel = Panel({"ItemA": self.frame, "ItemB": self.frame * 2})

        result = panel.swaplevel(0, 1, axis="major")
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        pass

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df["foo"].values[:] = 0
        self.assert_((df["foo"].values == 0).all())

        # but not if it's mixed-type
        df["foo", "four"] = "foo"
        df = df.sortlevel(0, axis=1)
        df["foo"]["one"] = 2
        self.assert_((df["foo", "one"] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df["foo", "four"] = "foo"

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df["foo"]
        result2 = df.ix[:, "foo"]
        expected = df.reindex(columns=df.columns[arrays[0] == "foo"])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs("foo")
        result2 = df.ix["foo"]
        expected = df.reindex(df.index[arrays[0] == "foo"])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [
            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
            ["one", "two", "one", "two", "one", "two", "one", "two"],
        ]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s["qux"]
        result2 = s.ix["qux"]
        expected = s[arrays[0] == "qux"]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)
Пример #20
0
from numpy.random import randn

ser = Series(randn(6),index = [[1,1,1,2,2,2],['a','b','c','a','b','c']]) # 2 lists of indexes
ser
ser.index
ser[1]
ser[2]

ser[:,'a']

df=ser.unstack()
df

df2 = DataFrame(np.arange(16).reshape(4,4),index = [['a','a','b','b'],[1,2,1,2]],
                    columns = [['NY','NY','LA','SF'],['cold','hot','cold','hot']])
df2

#naming indices
df2.index.names = ['Index_1','Index_2']
df2.columns.names = ['Cities','Temp']
df2

df2.swaplevel('Cities','Temp',axis = 1) #interchange the indices

df2.sortlevel(1)
df2.sortlevel(0)
df2
df2.sortlevel(0,axis=1)

df2.sum(axis=1)
df2.sum(level = 'Temp',axis =1) #provide the sum for each level of the index separately
Пример #21
0

'''

print('索引层级交换')
frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'],
                           ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2']
frame_swapped = frame.swaplevel('key1', 'key2')
print(frame_swapped)
print(frame_swapped.swaplevel(0, 1))

print('根据索引排序')
print(frame.sortlevel('key2'))
print(frame.swaplevel(0, 1).sortlevel(0))
'''

	层次化索引 根据级别汇总统计
	• 指定索引级别和轴


	层次化索引 使用DataFrame的列
	• 将指定列变为索引
	• 移除或保留对象
	• reset_index恢复

 	整数索引
	• 歧义的产生
	• 可靠的,不考虑索引类型的,基于位置的索引。
Пример #22
0
# b    1         6    7         8
#      2         9   10        11
#互换级别
print frame.swaplevel('key1', 'key2')
# state       Ohio       Colorado
# color      Green  Red     Green
# key2 key1
# 1    a         0    1         2
# 2    a         3    4         5
# 1    b         6    7         8
# 2    b         9   10        11

#sortlevel根据单个级别中的值对数据进行排序。

#按key2排序
print frame.sortlevel(1)
# state       Ohio       Colorado
# color      Green  Red     Green
# key1 key2
# a    1         0    1         2
# b    1         6    7         8
# a    2         3    4         5
# b    2         9   10        11
print frame.swaplevel(0, 1).sortlevel()
# state       Ohio       Colorado
# color      Green  Red     Green
# key2 key1
# 1    a         0    1         2
#      b         6    7         8
# 2    a         3    4         5
#      b         9   10        11
Пример #23
0
df2 = DataFrame(np.arange(16).reshape(4,4), columns = hier_cols)
df2

df2.columns.values # output column names
df2.index.names = ['INDEX_1','INDEX_2'] # name or rename an index
df2.columns.names = ['Cities','Temp'] # name or rename column indexes 

# other ways to rename indexes or columns
df.rename(index = {'Old_Name': 'New_Name'},
         columns = {'Old_Col_Name': 'New_Col_Name'},
         inplace = True)

df2.swaplevel('Cities','Temp', axis = 1) # swamp index relationships. (axis =1 is for columns)
df2.swaplevel('Cities','Temp', axis = 1) # swamp index relationships. (axis =1 is for columns)

df2.sortlevel(0) #sorts according to index. df2.sortlevel(1) will sort according to secondary index

df2.sum(level = 'Temp', axis = 1) # sum across 'columnar' index = "Temp" (columnar because of axis = 1)

df.reset_index(level=0, inplace = True) # converts index into a column. 
# This is useful if index has relevant information like date. In which case reset_index(level = 0) converts that date into a column

# Using loc (location) function
df['a_column'].loc[df['a_column'] > 0] = 'I am not zero' # loc(ate) rows where df['a_column'] > 0, and set the value of those rows to 'I am not zero'
df['a_column'].loc[df['a_column'] == 0] = 'I am zero'

###############################################################
###															###
###															###
###			   			DATAFRAME MERGING					###
###															###
Пример #24
0
print(frame)

frame.index.names = ['key1', 'key2']

frame.columns.names = ['state', 'color']

print(frame)

print(frame['Ohio'])

#Reordering and Sorting Levels

print(frame.swaplevel('key1', 'key2'))

print(frame.sortlevel(1))

print(frame.swaplevel(0, 1).sortlevel(0))

#Summary Statistics by Level

print(frame.sum(level='key2'))
print(frame.sum(level='color', axis=1))

#Using a DataFrame`s Columns

frame = DataFrame({
    'a': range(7),
    'b': range(7, 0, -1),
    'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],
    'd': [0, 1, 2, 0, 1, 2, 3]
Пример #25
0
df9 = df5.fillna(value = 0, axis = 1)                # .fillna() scans columns for NAs, replaces them with 0           
df10 = df5.fillna({0:0,1:1,2:2,3:3})                 # use a dictionary to fill diff values for diff columns
df5.fillna('NA', inplace= True )                     # use inplace=True for permanent processing


#############################################################################################################
# 5. Index Hierarchies: pandas allows you to create multiple levels of indexes
#############################################################################################################
city = DataFrame(np.arange(16).reshape(4,4),
                    index=[['a','a','b','b'],[1,2,1,2]],
                    columns=[['NY','NY','LA','SF'],['cold','hot','hot','cold']])

city.index.names = ['Level-0','Level-1']            # name the indexes
city.columns.names = ['Cities','Temp']              # name the columns
city2 = city.swaplevel('Cities','Temp',axis=1)      # swap level for columns
city.sortlevel(1)                                   # sort by Level-1
city3 = city.sum(level='Temp', axis=1)              # math operations by levels - sums the rows
city4 = city.sum(level='Level-0', axis=0)           # math operations by levels - sums on columns
                                                 
#############################################################################################################
# 6. Importing Data
#############################################################################################################
# Getting data intp Python like SAS's CARDS system
                                                 
from StringIO import StringIO
data ="""\
Sample Type Intelligence
1 Dog Smart
2 Dog Smart
3 Cat Dumb
4 Cat Dumb
data.unstack()
data.unstack().stack()
frame = DataFrame(np.arange(12).reshape(4, 3),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2'] 
frame.columns.names = ['state', 'color'] 
frame['Ohio']
new_columns=pd.MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']],
                       names=['state', 'color'])

"""
Reordering and Sorting Levels
"""
frame.swaplevel('key1', 'key2')
frame.sortlevel(1) #What is the difference from DataFrame.sort_index
frame.swaplevel(0, 1).sortlevel(1) 

"""
Summary Statistics by Level
"""
frame.sum(level='key2')
frame.sum(level='color', axis=1)

"""
Using a DataFrame's columns
"""
frame = DataFrame({'a': range(7), 'b': range(7, 0, -1),
                   'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],
                   'd': [0, 1, 2, 0, 1, 2, 3]})
frame.set_index(['c', 'd'])
Пример #27
0
def fun8():
    df = DataFrame(np.arange(10).reshape((5, 2)),
                   columns=['c1', 'c2'],
                   index=[list('aabbc'), list('12122')])
    # 指定索引、列名标签
    df.columns.name = 'columns'
    df.index.names = ['key0', 'key1']
    """
    columns    c1  c2
    key0 key1        
    a    1      0   1
         2      2   3
    b    1      4   5
         2      6   7
    c    2      8   9
    """

    # 使用0级索引提取DataFrame
    a1 = df.loc['a']
    """
    columns  c1  c2
    key1           
    1         0   1
    2         2   3
    """

    # 使用1级索引提取一行series, 以下结果一致
    a2 = df.loc['a', '1']
    a3 = df.loc['a'].loc['1']
    """
    columns
    c1    0
    c2    1
    """

    # 提取一列series, 以下结果一致
    b1 = df.loc['a']['c1']
    b2 = df.loc['a', 'c1']
    b3 = df['c1']['a']
    """
    key1
    1    0
    2    2
    """

    # 使用单个元素, 以下结果一致
    n1 = df.loc['a', '1']['c1']
    n2 = df.loc['a', 'c1']['1']
    n3 = df.loc['a'].loc['1', 'c1']
    """
    0
    """

    # 由第二层索引(level=1或'key1')升序排列, 默认非就地排序
    df.sortlevel(level=1, inplace=False)
    df.sortlevel(level=0, inplace=False)
    """
    columns    c1  c2
    key0 key1        
    a    1      0   1
    b    1      4   5
    a    2      2   3
    b    2      6   7
    c    2      8   9
    """

    # 交换一二层索引,并由第一层索引升序排列
    df.swaplevel(0, 1).sortlevel(0)
    df.swaplevel('key0', 'key1').sortlevel('key1')
    """
    columns    c1  c2
    key1 key0        
    1    a      0   1
         b      4   5
    2    a      2   3
         b      6   7
         c      8   9
    """

    # 获取第二层索引为'1'的行
    df1 = df.swaplevel('key0', 'key1').sortlevel('key1').loc['1']
    """
    columns  c1  c2
    key0           
    a         0   1
    b         4   5
    """

    # 由第二层索引计算具有相同索引的不同行元素之和
    df.sum(level=1, axis=0)
    """
    columns  c1  c2
    key1           
    1         4   6
    2        16  19
    """

    # 一层索引旋转为DataFrame的列
    df.reset_index(level='key1')
    """
    columns key1  c1  c2
    key0                
    a          1   0   1
    a          2   2   3
    b          1   4   5
    b          2   6   7
    c          2   8   9
    """

    # 多层索引旋转为DataFrame的列
    df2 = df.reset_index(level=['key0', 'key1'])
    """
    columns key0 key1  c1  c2
    0          a    1   0   1
    1          a    2   2   3
    2          b    1   4   5
    3          b    2   6   7
    4          c    2   8   9
    """

    # DataFrame的列旋转为索引, 替换原索引
    df21 = df2.set_index('key0', append=False)
    """
    columns key1  c1  c2
    key0                
    a          1   0   1
    a          2   2   3
    b          1   4   5
    b          2   6   7
    c          2   8   9
    """

    # DataFrame的列旋转为索引, 以内层索引形式添加
    df22 = df21.set_index('key1', append=True)
    """
    columns    c1  c2
    key0 key1        
    a    1      0   1
         2      2   3
    b    1      4   5
         2      6   7
    c    2      8   9
    """

    sr = Series(np.arange(5), index=[list('aabbc'), list('12122')])
    sr.index.names = ['key0', 'key1']
    """
    key0  key1
    a     1       0
          2       1
    b     1       2
          2       3
    c     2       4
    """

    # 获取全部二级索引为'1'的行
    m1 = sr[:, '1']
    """
    key0
    a    0
    b    2
    """

    # 获取全部一级索引为a的行
    m2 = sr['a']
    """
    key1
    1    0
    2    1
    """

    # 由第0层索引展开
    sr.unstack(0)
    sr.unstack('key0')
    """
         a    b    c
    1  0.0  2.0  NaN
    2  1.0  3.0  4.0
    """

    # 由第1层索引展开
    sr.unstack(1)
    sr.unstack('key1')
    """
    key1    1    2
    key0          
    a     0.0  1.0
    b     2.0  3.0
    c     NaN  4.0
    """

    # 索引展开再复原(可逆), stack默认将行加入内层索引, 交换索引层级并排序之后可复原
    sr.unstack('key0').stack('key0').swaplevel('key0', 'key1').sortlevel('key0')
    """
Пример #28
0
df3
df3.ix["b", ].ix[1:, ]
df3.ix["a":"b", ].ix[1:, ]
df3.unstack().unstack()

frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'],
                           ['Green', 'Red', 'Green']])
frame
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame
frame.swaplevel("key1", "key2")
frame
frame.sortlevel(0)
frame
frame.sum(level="key1")

frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'],
                           ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame.sum(level="color", axis=1)
frame.sum(level="state", axis=1)

ser = Series(np.arange(3.), index=list("abc"))
ser
ser[-1]
Пример #29
0
frame_swapped.swaplevel(0, 1)  # 交换回来
'''
           A       B
          A1  A2  B1
key1 key2
a    1     0   1   2
     2     3   4   5
b    1     6   7   8
     2     9  10  11
'''
print

print
'对某个索引层进行排序'
print
frame.sortlevel('key2')
'''
           A       B
          A1  A2  B1
key1 key2
a    1     0   1   2
b    1     6   7   8
a    2     3   4   5
b    2     9  10  11
'''
print
frame.swaplevel(0, 1).sortlevel(0)
'''
           A       B
          A1  A2  B1
key2 key1
Пример #30
0
class TestMultiLevel(unittest.TestCase):

    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       labels=[[0, 1, 2, 3]],
                                       names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.levels = [lev.astype('i8')
                                 for lev in self.ymd.index.levels]
        self.ymd.index.names = ['year', 'month', 'day']

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a['A'].append(b['A'])
        tm.assert_series_equal(result, self.frame['A'])

    def test_dataframe_constructor(self):
        multi = DataFrame(np.random.randn(4, 4),
                          index=[np.array(['a', 'a', 'b', 'b']),
                                 np.array(['x', 'y', 'x', 'y'])])
        self.assert_(isinstance(multi.index, MultiIndex))
        self.assert_(not isinstance(multi.columns, MultiIndex))

        multi = DataFrame(np.random.randn(4, 4),
                          columns=[['a', 'a', 'b', 'b'],
                                   ['x', 'y', 'x', 'y']])
        self.assert_(isinstance(multi.columns, MultiIndex))

    def test_series_constructor(self):
        multi = Series(1., index=[np.array(['a', 'a', 'b', 'b']),
                                  np.array(['x', 'y', 'x', 'y'])])
        self.assert_(isinstance(multi.index, MultiIndex))

        multi = Series(1., index=[['a', 'a', 'b', 'b'],
                                  ['x', 'y', 'x', 'y']])
        self.assert_(isinstance(multi.index, MultiIndex))

        multi = Series(range(4), index=[['a', 'a', 'b', 'b'],
                                        ['x', 'y', 'x', 'y']])
        self.assert_(isinstance(multi.index, MultiIndex))

    def test_reindex_level(self):
        # axis=0
        month_sums = self.ymd.sum(level='month')
        result = month_sums.reindex(self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum)

        assert_frame_equal(result, expected)

        # Series
        result = month_sums['A'].reindex(self.ymd.index, level=1)
        expected = self.ymd['A'].groupby(level='month').transform(np.sum)
        assert_series_equal(result, expected)

        # axis=1
        month_sums = self.ymd.T.sum(axis=1, level='month')
        result = month_sums.reindex(columns=self.ymd.index, level=1)
        expected = self.ymd.groupby(level='month').transform(np.sum).T
        assert_frame_equal(result, expected)

    def test_binops_level(self):
        def _check_op(opname):
            op = getattr(DataFrame, opname)
            month_sums = self.ymd.sum(level='month')
            result = op(self.ymd, month_sums, level='month')

            broadcasted = self.ymd.groupby(level='month').transform(np.sum)
            expected = op(self.ymd, broadcasted)
            assert_frame_equal(result, expected)

            # Series
            op = getattr(Series, opname)
            result = op(self.ymd['A'], month_sums['A'], level='month')
            broadcasted = self.ymd['A'].groupby(level='month').transform(np.sum)
            expected = op(self.ymd['A'], broadcasted)
            assert_series_equal(result, expected)

        _check_op('sub')
        _check_op('add')
        _check_op('mul')
        _check_op('div')

    def test_pickle(self):
        import cPickle
        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_repr_name_coincide(self):
        index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')],
                                       names=['a', 'b', 'c'])

        df = DataFrame({'value': [0, 1]}, index=index)

        lines = repr(df).split('\n')
        self.assert_(lines[2].startswith('a 0 foo'))

    def test_getitem_simple(self):
        df = self.frame.T

        col = df['foo', 'one']
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ('foo', 'four'))
        self.assertRaises(KeyError, df.__getitem__, 'foobar')

    def test_series_getitem(self):
        s = self.ymd['A']

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s.reindex(s.index[42:65])
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s.reindex(s.index[49:51])
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_getitem_corner(self):
        s = self.ymd['A']

        # don't segfault, GH #495
        # out of bounds access
        self.assertRaises(IndexError, s.__getitem__, len(self.ymd))

        # generator
        result = s[(x > 0 for x in s)]
        expected = s[s > 0]
        assert_series_equal(result, expected)

    def test_series_setitem(self):
        s = self.ymd['A']

        s[2000, 3] = np.nan
        self.assert_(isnull(s.values[42:65]).all())
        self.assert_(notnull(s.values[:42]).all())
        self.assert_(notnull(s.values[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_frame_getitem_setitem_slice(self):
        # getitem
        result = self.frame.ix[:4]
        expected = self.frame[:4]
        assert_frame_equal(result, expected)

        # setitem
        cp = self.frame.copy()
        cp.ix[:4] = 0

        self.assert_((cp.values[:4] == 0).all())
        self.assert_((cp.values[4:] != 0).all())

    def test_frame_getitem_setitem_multislice(self):
        levels = [['t1', 't2'], ['a','b','c']]
        labels = [[0,0,0,1,1], [0,1,2,0,1]]
        midx = MultiIndex(labels=labels, levels=levels, names=[None, 'id'])
        df = DataFrame({'value':[1,2,3,7,8]}, index=midx)

        result = df.ix[:,'value']
        assert_series_equal(df['value'], result)

        result = df.ix[1:3,'value']
        assert_series_equal(df['value'][1:3], result)

        result = df.ix[:,:]
        assert_frame_equal(df, result)

        result = df
        df.ix[:, 'value'] = 10
        result['value'] = 10
        assert_frame_equal(df, result)

        df.ix[:,:] = 10
        assert_frame_equal(df, result)

    def test_getitem_tuple_plus_slice(self):
        # GH #671
        df = DataFrame({'a' : range(10),
                        'b' : range(10),
                        'c' : np.random.randn(10),
                        'd' : np.random.randn(10)})

        idf = df.set_index(['a', 'b'])

        result = idf.ix[(0, 0), :]
        expected = idf.ix[0, 0]
        expected2 = idf.xs((0, 0))

        assert_series_equal(result, expected)
        assert_series_equal(result, expected2)

    def test_getitem_setitem_tuple_plus_columns(self):
        # GH #1013

        df = self.ymd[:5]

        result = df.ix[(2000, 1, 6), ['A', 'B', 'C']]
        expected = df.ix[2000, 1, 6][['A', 'B', 'C']]
        assert_series_equal(result, expected)

    def test_xs(self):
        xs = self.frame.xs(('bar', 'two'))
        xs2 = self.frame.ix[('bar', 'two')]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs('foo')
        result2 = self.frame.ix['foo']
        expected = self.frame.T['foo'].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_xs_level(self):
        result = self.frame.xs('two', level='second')
        expected = self.frame[self.frame.index.get_level_values(1) == 'two']
        expected.index = expected.index.droplevel(1)

        assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([('x', 'y', 'z'), ('a', 'b', 'c'),
                                        ('p', 'q', 'r')])
        df = DataFrame(np.random.randn(3, 5), index=index)
        result = df.xs('c', level=2)
        expected = df[1:2]
        expected.index = expected.index.droplevel(2)
        assert_frame_equal(result, expected)

    def test_xs_level_multiple(self):
        from pandas import read_table
        from StringIO import StringIO
        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep='\s+')

        result = df.xs(('a', 4), level=['one', 'four'])
        expected = df.xs('a').xs(4, level='four')
        assert_frame_equal(result, expected)

    def test_xs_level0(self):
        from pandas import read_table
        from StringIO import StringIO
        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep='\s+')

        result = df.xs('a', level=0)
        expected = df.xs('a')
        self.assertEqual(len(result), 2)
        assert_frame_equal(result, expected)

    def test_xs_level_series(self):
        s = self.frame['A']
        result = s[:, 'two']
        expected = self.frame.xs('two', level=1)['A']
        assert_series_equal(result, expected)

        s = self.ymd['A']
        result = s[2000, 5]
        expected = self.ymd.ix[2000, 5]['A']
        assert_series_equal(result, expected)

        # not implementing this for now

        self.assertRaises(TypeError, s.__getitem__, (2000, slice(3, 4)))

        # result = s[2000, 3:4]
        # lv =s.index.get_level_values(1)
        # expected = s[(lv == 3) | (lv == 4)]
        # expected.index = expected.index.droplevel(0)
        # assert_series_equal(result, expected)

        # can do this though

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)),
                   index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df['foo']
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df['bar']
        result2 = df.ix[:, 'bar']

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_setitem_slice_integers(self):
        index = MultiIndex(levels=[[0, 1, 2], [0, 2]],
                           labels=[[0, 0, 1, 1, 2, 2],
                                   [0, 1, 0, 1, 0, 1]])

        frame =  DataFrame(np.random.randn(len(index), 4), index=index,
                           columns=['a', 'b', 'c', 'd'])
        res = frame.ix[1:2]
        exp = frame.reindex(frame.index[2:])
        assert_frame_equal(res, exp)

        frame.ix[1:2] = 7
        self.assert_((frame.ix[1:2] == 7).values.all())

        series =  Series(np.random.randn(len(index)), index=index)

        res = series.ix[1:2]
        exp = series.reindex(series.index[2:])
        assert_series_equal(res, exp)

        series.ix[1:2] = 7
        self.assert_((series.ix[1:2] == 7).values.all())

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_getitem_slice_not_sorted(self):
        df = self.frame.sortlevel(1).T

        # buglet with int typechecking
        result = df.ix[:, :np.int32(3)]
        expected = df.reindex(columns=df.columns[:3])
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft['foo', 'two']
        dft['foo', 'two'] = s > s.median()
        assert_series_equal(dft['foo', 'two'], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[('foo', 'two')])
        assert_series_equal(reindexed['foo', 'two'], s > s.median())

    def test_frame_setitem_ix(self):
        self.frame.ix[('bar', 'two'), 'B'] = 5
        self.assertEquals(self.frame.ix[('bar', 'two'), 'B'], 5)

        # with integer labels
        df = self.frame.copy()
        df.columns = range(3)
        df.ix[('bar', 'two'), 1] = 7
        self.assertEquals(df.ix[('bar', 'two'), 1], 7)

    def test_fancy_slice_partial(self):
        result = self.frame.ix['bar':'baz']
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000,2):(2000,4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame['A'].sortlevel(0)
        self.assertRaises(Exception,
                          self.frame.reset_index()['A'].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_delevel_infer_dtype(self):
        tuples = [tuple for tuple in cart_product(['foo', 'bar'],
                                                  [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples,
                                       names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8,3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        self.assert_(com.is_integer_dtype(deleveled['prm1']))
        self.assert_(com.is_float_dtype(deleveled['prm2']))

    def test_reset_index_with_drop(self):
        deleveled = self.ymd.reset_index(drop = True)
        self.assertEquals(len(deleveled.columns), len(self.ymd.columns))

        deleveled = self.series.reset_index()
        self.assert_(isinstance(deleveled, DataFrame))
        self.assert_(len(deleveled.columns) == len(self.series.index.levels)+1)

        deleveled = self.series.reset_index(drop = True)
        self.assert_(isinstance(deleveled, Series))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ['first', 'second']
        result = self.frame.sortlevel(level='second')
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df['foo'] = 'bar'
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(['foo'], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft['foo', 'three'] = 'bar'

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
                           sorted_after.drop([('foo', 'three')], axis=1))

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype('i8')
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame['D'] = 'foo'
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ['A', 'B', 'C'])

    def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'],
                                   ['one', 'two', 'three', 'four']],
                           labels=[[0, 0, 0, 2, 2],
                                   [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame['A'][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0],
                             columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_unstack_multiple_no_empty_columns(self):
        index = MultiIndex.from_tuples([(0, 'foo', 0), (0, 'bar', 0),
                                        (1, 'baz', 1), (1, 'qux', 1)])

        s = Series(np.random.randn(4), index=index)

        unstacked = s.unstack([1, 2])
        expected = unstacked.dropna(axis=1, how='all')
        assert_frame_equal(unstacked, expected)

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked['foo'], df['foo'].stack())
        self.assert_(stacked['bar'].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame({'state': ['naive','naive','naive',
                                  'activ','activ','activ'],
                        'exp':['a','b','b','b','a','a'],
                        'barcode':[1,2,3,4,1,3],
                        'v':['hi','hi','bye','bye','bye','peace'],
                        'extra': np.arange(6.)})

        result = df.groupby(['state','exp','barcode','v']).apply(len)

        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked,
                            result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, 'first')
        self.assertEquals(unstacked.columns.names, ['exp', 'second'])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack('second')
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack('second')
        result = unstacked.stack('exp')
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack('exp')
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(['year', 'month'])
        expected = self.ymd.unstack('year').unstack('month')
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names,
                          expected.columns.names)

        # series
        s = self.ymd['A']
        s_unstacked = s.unstack(['year', 'month'])
        assert_frame_equal(s_unstacked, expected['A'])

        restacked = unstacked.stack(['year', 'month'])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1).dropna(axis=1, how='all')
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1).dropna(axis=1, how='all')
        assert_frame_equal(unstacked, expected.ix[:, unstacked.columns])

    def test_groupby_transform(self):
        s = self.frame['A']
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_groupby_corner(self):
        midx = MultiIndex(levels=[['foo'],['bar'],['baz']],
                          labels=[[0],[0],[0]], names=['one','two','three'])
        df = DataFrame([np.random.rand(4)], columns=['a','b','c','d'],
                       index=midx)
        # should work
        df.groupby(level='three')

    def test_join(self):
        a = self.frame.ix[:5, ['A']]
        b = self.frame.ix[2:, ['B', 'C']]

        joined = a.join(b, how='outer').reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame['A'].swaplevel(0, 1)
        swapped2 = self.frame['A'].swaplevel('first', 'second')
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel('second', 'first')
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel('first', 'second', axis=1)
        exp = self.frame.swaplevel('first', 'second').T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({'ItemA' : self.frame,
                       'ItemB' : self.frame * 2})

        result = panel.swaplevel(0, 1, axis='major')
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_reorder_levels(self):
        result = self.ymd.reorder_levels(['month', 'day', 'year'])
        expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
        assert_frame_equal(result, expected)

        result = self.ymd['A'].reorder_levels(['month', 'day', 'year'])
        expected = self.ymd['A'].swaplevel(0, 1).swaplevel(1, 2)
        assert_series_equal(result, expected)

        result = self.ymd.T.reorder_levels(['month', 'day', 'year'], axis=1)
        expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
        assert_frame_equal(result, expected)

        self.assertRaises(Exception, self.ymd.index.reorder_levels,
                          [1, 2, 3])

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        x = Series(data=[1,2,3],
                   index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B",3)]))

        y = Series(data=[4,5,6],
                   index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B",3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 1, 0, 1, 1],
                                   [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df['foo'].values[:] = 0
        self.assert_((df['foo'].values == 0).all())

        # but not if it's mixed-type
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(0, axis=1)
        df['foo']['one'] = 2
        self.assert_((df['foo', 'one'] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df['foo']
        result2 = df.ix[:, 'foo']
        expected = df.reindex(columns=df.columns[arrays[0] == 'foo'])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs('foo')
        result2 = df.ix['foo']
        expected = df.reindex(df.index[arrays[0] == 'foo'])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
        ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s['qux']
        result2 = s.ix['qux']
        expected = s[arrays[0] == 'qux']
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    def test_count(self):
        frame = self.frame.copy()
        frame.index.names = ['a', 'b']

        result = frame.count(level='b')
        expect = self.frame.count(level=1)
        assert_frame_equal(result, expect)

        result = frame.count(level='a')
        expect = self.frame.count(level=0)
        assert_frame_equal(result, expect)

        series = self.series.copy()
        series.index.names = ['a', 'b']

        result = series.count(level='b')
        expect = self.series.count(level=1)
        assert_series_equal(result, expect)

        result = series.count(level='a')
        expect = self.series.count(level=0)
        assert_series_equal(result, expect)

        self.assertRaises(Exception, series.count, 'x')
        self.assertRaises(Exception, frame.count, level='x')

    AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
                     'mad', 'std', 'var']

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS,
                                              range(2),
                                              [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
                                                    range(2), range(2),
                                                    [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            pieces = []
            def aggf(x):
                pieces.append(x)
                return getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level, axis=axis,
                                           skipna=skipna)

            # for good measure, groupby detail
            level_index = frame._get_axis(axis).levels[level]

            self.assert_(leftside._get_axis(axis).equals(level_index))
            self.assert_(rightside._get_axis(axis).equals(level_index))

            assert_frame_equal(leftside, rightside)

    def test_std_var_pass_ddof(self):
        index = MultiIndex.from_arrays([np.arange(5).repeat(10),
                                        np.tile(np.arange(10), 5)])
        df = DataFrame(np.random.randn(len(index), 5), index=index)

        for meth in ['var', 'std']:
            ddof = 4
            alt = lambda x: getattr(x, meth)(ddof=ddof)

            result = getattr(df[0], meth)(level=0, ddof=ddof)
            expected = df[0].groupby(level=0).agg(alt)
            assert_series_equal(result, expected)

            result = getattr(df, meth)(level=0, ddof=ddof)
            expected = df.groupby(level=0).agg(alt)
            assert_frame_equal(result, expected)


    def test_frame_series_agg_multiple_levels(self):
        result = self.ymd.sum(level=['year', 'month'])
        expected = self.ymd.groupby(level=['year', 'month']).sum()
        assert_frame_equal(result, expected)

        result = self.ymd['A'].sum(level=['year', 'month'])
        expected = self.ymd['A'].groupby(level=['year', 'month']).sum()
        assert_series_equal(result, expected)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'),
                                        ('bar', 'one'), ('bar', 'two')])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df['Totals', ''] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd['A'].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd['A'].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df['A'].ix[2000, 4] = 1
        exp['A'].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df['A'].ix[14] = 5
        self.assertEquals(df['A'][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd['E'] = 'foo'
        self.ymd['F'] = 2

        unstacked = self.ymd.unstack('month')
        self.assert_(unstacked['A', 1].dtype == np.float64)
        self.assert_(unstacked['E', 1].dtype == np.object_)
        self.assert_(unstacked['F', 1].dtype == np.float64)

    def test_getitem_lowerdim_corner(self):
        self.assertRaises(KeyError, self.frame.ix.__getitem__,
                          (('bar', 'three'), 'B'))

        self.assertRaises(KeyError, self.frame.ix.__setitem__,
                          (('bar', 'three'), 'B'), 0)

    #----------------------------------------------------------------------
    # AMBIGUOUS CASES!

    def test_partial_ix_missing(self):
        raise nose.SkipTest

        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]['A']
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    #----------------------------------------------------------------------

    def test_to_html(self):
        self.ymd.columns.name = 'foo'
        self.ymd.to_html()
        self.ymd.T.to_html()

    def test_level_with_tuples(self):
        index = MultiIndex(levels=[[('foo', 'bar', 0), ('foo', 'baz', 0),
                                    ('foo', 'qux', 0)],
                                   [0, 1]],
                           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar', 0)]
        result2 = series.ix[('foo', 'bar', 0)]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        self.assertRaises(KeyError, series.__getitem__, (('foo', 'bar', 0), 2))

        result = frame.ix[('foo', 'bar', 0)]
        result2 = frame.xs(('foo', 'bar', 0))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        index = MultiIndex(levels=[[('foo', 'bar'), ('foo', 'baz'),
                                    ('foo', 'qux')],
                                   [0, 1]],
                           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[('foo', 'bar')]
        result2 = series.ix[('foo', 'bar')]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        result = frame.ix[('foo', 'bar')]
        result2 = frame.xs(('foo', 'bar'))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_int_series_slicing(self):
        s = self.ymd['A']
        result = s[5:]
        expected = s.reindex(s.index[5:])
        assert_series_equal(result, expected)

        exp = self.ymd['A'].copy()
        s[5:] = 0
        exp.values[5:] = 0
        self.assert_(np.array_equal(s.values, exp.values))

        result = self.ymd[5:]
        expected = self.ymd.reindex(s.index[5:])
        assert_frame_equal(result, expected)

    def test_mixed_depth_get(self):
        arrays = [[  'a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  [   '',  'OD',  'OD', 'result1',   'result2',  'result1'],
                  [   '',  'wx',  'wy',        '',          '',         '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4,6),columns = index)

        result = df['a']
        expected = df['a','','']
        assert_series_equal(result, expected)
        self.assertEquals(result.name, 'a')

        result = df['routine1','result1']
        expected = df['routine1','result1','']
        assert_series_equal(result, expected)
        self.assertEquals(result.name, ('routine1', 'result1'))

    def test_mixed_depth_insert(self):
        arrays = [[  'a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  [   '',  'OD',  'OD', 'result1',   'result2',  'result1'],
                  [   '',  'wx',  'wy',        '',          '',         '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4,6),columns = index)

        result = df.copy()
        expected = df.copy()
        result['b'] = [1,2,3,4]
        expected['b','',''] = [1,2,3,4]
        assert_frame_equal(result, expected)

    def test_mixed_depth_drop(self):
        arrays = [[  'a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  [   '',  'OD',  'OD', 'result1',   'result2',  'result1'],
                  [   '',  'wx',  'wy',        '',          '',         '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4,6),columns = index)

        result = df.drop('a',axis=1)
        expected = df.drop([('a','','')],axis=1)
        assert_frame_equal(expected, result)

        result = df.drop(['top'],axis=1)
        expected = df.drop([('top','OD','wx')], axis=1)
        expected = expected.drop([('top','OD','wy')], axis=1)
        assert_frame_equal(expected, result)

        result = df.drop(('top', 'OD', 'wx'), axis=1)
        expected = df.drop([('top','OD','wx')], axis=1)
        assert_frame_equal(expected, result)

        expected = df.drop([('top','OD','wy')], axis=1)
        expected = df.drop('top', axis=1)

        result = df.drop('result1', level=1, axis=1)
        expected = df.drop([('routine1', 'result1', ''),
                            ('routine2', 'result1', '')], axis=1)
        assert_frame_equal(expected, result)

    def test_mixed_depth_pop(self):
        arrays = [[  'a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  [   '',  'OD',  'OD', 'result1',   'result2',  'result1'],
                  [   '',  'wx',  'wy',        '',          '',         '']]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4,6),columns = index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop('a')
        expected = df2.pop(('a','',''))
        assert_series_equal(expected, result)
        assert_frame_equal(df1, df2)
        self.assertEquals(result.name,'a')

        expected = df1['top']
        df1 = df1.drop(['top'],axis=1)
        result = df2.pop('top')
        assert_frame_equal(expected, result)
        assert_frame_equal(df1, df2)

    def test_reindex_level_partial_selection(self):
        result = self.frame.reindex(['foo', 'qux'], level=0)
        expected = self.frame.ix[[0, 1, 2, 7, 8, 9]]
        assert_frame_equal(result, expected)

        result = self.frame.T.reindex_axis(['foo', 'qux'], axis=1, level=0)
        assert_frame_equal(result, expected.T)

        result = self.frame.ix[['foo', 'qux']]
        assert_frame_equal(result, expected)

        result = self.frame['A'].ix[['foo', 'qux']]
        assert_series_equal(result, expected['A'])

        result = self.frame.T.ix[:, ['foo', 'qux']]
        assert_frame_equal(result, expected.T)

    def test_setitem_multiple_partial(self):
        expected = self.frame.copy()
        result = self.frame.copy()
        result.ix[['foo', 'bar']] = 0
        expected.ix['foo'] = 0
        expected.ix['bar'] = 0
        assert_frame_equal(result, expected)

        expected = self.frame.copy()
        result = self.frame.copy()
        result.ix['foo':'bar'] = 0
        expected.ix['foo'] = 0
        expected.ix['bar'] = 0
        assert_frame_equal(result, expected)

        expected = self.frame['A'].copy()
        result = self.frame['A'].copy()
        result.ix[['foo', 'bar']] = 0
        expected.ix['foo'] = 0
        expected.ix['bar'] = 0
        assert_series_equal(result, expected)

        expected = self.frame['A'].copy()
        result = self.frame['A'].copy()
        result.ix['foo':'bar'] = 0
        expected.ix['foo'] = 0
        expected.ix['bar'] = 0
        assert_series_equal(result, expected)

    def test_drop_level(self):
        result = self.frame.drop(['bar', 'qux'], level='first')
        expected = self.frame.ix[[0, 1, 2, 5, 6]]
        assert_frame_equal(result, expected)

        result = self.frame.drop(['two'], level='second')
        expected = self.frame.ix[[0, 2, 3, 6, 7, 9]]
        assert_frame_equal(result, expected)

        result = self.frame.T.drop(['bar', 'qux'], axis=1, level='first')
        expected = self.frame.ix[[0, 1, 2, 5, 6]].T
        assert_frame_equal(result, expected)

        result = self.frame.T.drop(['two'], axis=1, level='second')
        expected = self.frame.ix[[0, 2, 3, 6, 7, 9]].T
        assert_frame_equal(result, expected)

    def test_unicode_repr_issues(self):
        levels = [Index([u'a/\u03c3', u'b/\u03c3',u'c/\u03c3']),
                  Index([0, 1])]
        labels = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
        index = MultiIndex(levels=levels, labels=labels)

        repr(index.levels)
Пример #31
0
2    b     9  10  11
'''
print frame_swapped.swaplevel(0, 1) # 交换回来
'''
           A       B
          A1  A2  B1
key1 key2           
a    1     0   1   2
     2     3   4   5
b    1     6   7   8
     2     9  10  11
'''
print

print '对某个索引层进行排序'
print frame.sortlevel('key2')
'''
           A       B
          A1  A2  B1
key1 key2           
a    1     0   1   2
b    1     6   7   8
a    2     3   4   5
b    2     9  10  11
'''
print frame.swaplevel(0, 1).sortlevel(0)
'''
           A       B
          A1  A2  B1
key2 key1           
1    a     0   1   2
Пример #32
0
    else:

        #- Load old Dataframe -#

        df_old = read_csv(os.path.join(outputdir,outputfile),\
            header=[0,1,2], skipinitialspace=True) # options required for 3D data

        #- Convert both DataFrames to dictionaries -#

        dict_old = dict(df_old)
        dict_new = dict(df_meanAtQ)
        # Following step seems required to avoid the error:
        # "Exception: cannot handle a non-unique multi-index!"
        for d in (dict_old, dict_new):
            for key in d.keys():
                if "Unnamed" in string.join(key, ''):
                    d.pop(key, None)

            #- Update old dictionary with new dictionaries -#

        dict_old.update(dict_new)

        #- Convert to DataFrame and export to CSV -#

        df_new = DataFrame(dict_old)
        # Following step required to avoid the warning:
        # "PerformanceWarning: indexing past lexsort depth may impact performance."
        df_new.sortlevel(axis=1, inplace=True, sort_remaining=True)
        df_new.to_csv(os.path.join(outputdir, outputfile))
Пример #33
0
class TestMultiLevel(unittest.TestCase):

    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       labels=[[0, 1, 2, 3]],
                                       names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()
        self.ymd.index.names = ['year', 'month', 'day']

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a['A'].append(b['A'])
        tm.assert_series_equal(result, self.frame['A'])

    def test_pickle(self):
        import cPickle
        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[('foo', 'one'), ('bar', 'one')]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df['foo', 'one']
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ('foo', 'four'))
        self.assertRaises(KeyError, df.__getitem__, 'foobar')

    def test_series_getitem(self):
        s = self.ymd['A']

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s[42:65]
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s[49:51]
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_setitem(self):
        s = self.ymd['A']

        s[2000, 3] = np.nan
        self.assert_(isnull(s[42:65]).all())
        self.assert_(notnull(s[:42]).all())
        self.assert_(notnull(s[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_xs(self):
        xs = self.frame.xs(('bar', 'two'))
        xs2 = self.frame.ix[('bar', 'two')]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs('foo')
        result2 = self.frame.ix['foo']
        expected = self.frame.T['foo'].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_fancy_2d(self):
        result = self.frame.ix['foo', 'B']
        expected = self.frame.xs('foo')['B']
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix['B', 'foo']
        expected = ft.xs('B')['foo']
        assert_series_equal(result, expected)

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)),
                   index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df['foo']
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df['bar']
        result2 = df.ix[:, 'bar']

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft['foo', 'two']
        dft['foo', 'two'] = s > s.median()
        assert_series_equal(dft['foo', 'two'], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[('foo', 'two')])
        assert_series_equal(reindexed['foo', 'two'], s > s.median())

    def test_fancy_slice_partial(self):
        result = self.frame.ix['bar':'baz']
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000,2):(2000,4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame['A'].sortlevel(0)
        self.assertRaises(Exception,
                          self.frame.delevel()['A'].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_delevel_infer_dtype(self):
        tuples = [tuple for tuple in cart_product(['foo', 'bar'],
                                                  [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples,
                                       names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8,3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.delevel()
        self.assert_(com.is_integer_dtype(deleveled['prm1']))
        self.assert_(com.is_float_dtype(deleveled['prm2']))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ['first', 'second']
        result = self.frame.sortlevel(level='second')
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df['foo'] = 'bar'
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(['foo'], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft['foo', 'three'] = 'bar'

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
                           sorted_after.drop([('foo', 'three')], axis=1))

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype('i8')
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame['D'] = 'foo'
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ['A', 'B', 'C'])

    def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'],
                                   ['one', 'two', 'three', 'four']],
                           labels=[[0, 0, 0, 2, 2],
                                   [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype('f8'),
                            expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame['A'][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0],
                             columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked['foo'], df['foo'].stack())
        self.assert_(stacked['bar'].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame({'state': ['naive','naive','naive',
                                  'activ','activ','activ'],
                        'exp':['a','b','b','b','a','a'],
                        'barcode':[1,2,3,4,1,3],
                        'v':['hi','hi','bye','bye','bye','peace'],
                        'extra': np.arange(6.)})

        result = df.groupby(['state','exp','barcode','v']).apply(len)
        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked,
                            result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, 'first')
        self.assertEquals(unstacked.columns.names, ['exp', 'second'])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack('second')
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack('second')
        result = unstacked.stack('exp')
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack('exp')
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(['year', 'month'])
        expected = self.ymd.unstack('year').unstack('month')
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names,
                          expected.columns.names)

        # series
        s = self.ymd['A']
        s_unstacked = s.unstack(['year', 'month'])
        assert_frame_equal(s_unstacked, expected['A'])

        restacked = unstacked.stack(['year', 'month'])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1)
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1)
        assert_frame_equal(unstacked, expected)

    def test_groupby_transform(self):
        s = self.frame['A']
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_join(self):
        a = self.frame.ix[:5, ['A']]
        b = self.frame.ix[2:, ['B', 'C']]

        joined = a.join(b, how='outer').reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame['A'].swaplevel(0, 1)
        swapped2 = self.frame['A'].swaplevel('first', 'second')
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel('second', 'first')
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel('first', 'second', axis=1)
        exp = self.frame.swaplevel('first', 'second').T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({'ItemA' : self.frame,
                       'ItemB' : self.frame * 2})

        result = panel.swaplevel(0, 1, axis='major')
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        x = Series(data=[1,2,3],
                   index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B",3)]))

        y = Series(data=[4,5,6],
                   index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B",3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 0, 1, 1, 1],
                                   [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels,
                           labels=[[0, 0, 1, 0, 1, 1],
                                   [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df['foo'].values[:] = 0
        self.assert_((df['foo'].values == 0).all())

        # but not if it's mixed-type
        df['foo', 'four'] = 'foo'
        df = df.sortlevel(0, axis=1)
        df['foo']['one'] = 2
        self.assert_((df['foo', 'one'] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df['foo', 'four'] = 'foo'

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df['foo']
        result2 = df.ix[:, 'foo']
        expected = df.reindex(columns=df.columns[arrays[0] == 'foo'])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs('foo')
        result2 = df.ix['foo']
        expected = df.reindex(df.index[arrays[0] == 'foo'])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
        ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s['qux']
        result2 = s.ix['qux']
        expected = s[arrays[0] == 'qux']
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
                     'mad', 'std', 'var']

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS,
                                              range(2),
                                              [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS,
                                                    range(2), range(2),
                                                    [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            aggf = lambda x: getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level, axis=axis,
                                           skipna=skipna)
            assert_frame_equal(leftside, rightside)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([('foo', 'one'), ('foo', 'two'),
                                        ('bar', 'one'), ('bar', 'two')])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df['Totals', ''] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd['A'].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd['A'].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df['A'].ix[2000, 4] = 1
        exp['A'].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df['A'].ix[14] = 5
        self.assertEquals(df['A'][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd['E'] = 'foo'
        self.ymd['F'] = 2

        unstacked = self.ymd.unstack('month')
        self.assert_(unstacked['A', 1].dtype == np.float64)
        self.assert_(unstacked['E', 1].dtype == np.object_)
        self.assert_(unstacked['F', 1].dtype == np.float64)

    def test_partial_ix_missing(self):
        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]['A']
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    def test_to_html(self):
        self.ymd.columns.name = 'foo'
        self.ymd.to_html()
        self.ymd.T.to_html()
Пример #34
0
print(df)
# column index의 이름 정하기
df.columns.name = ['city', 'color']
print(df)
df.index.names = ['key1', 'key2']
print(df)

print(df['Seoul'])

# 색인 계층의 순서를 바꾸기
# swaplevel() 메서드를 이용해서 바꾼다.
print(df.swaplevel('key1', 'key2'))  # key1과 key2를 바꾸겠다.

# 사전식으로 계층을 바꾸어서 정렬하기
# sortlevel() 메서드를 이용해서 정렬한다.
df2 = df.swaplevel('key1', 'key2')
print(df2.sortlevel(0))  # 어느 깊이의 계층을 할것인지 정해준다.

df3 = df.sortlevel(1)
print(df3.swaplevel(0, 1))  # 숫자를 이용해도 가능

print(df.swaplevel(0, 1).sortlevel(0))

print(df.sortlevel(1).swaplevel(0, 1))
# 전부 다 같은 결과를 낸다.

# sum
print(df)
print(df.sum(level='key2'))  # key2를 기준으로 합

print(df.sum(level='color', axis=1))  # axis 옵션 사용 가능
Пример #35
0
#单独创建MultiIndex
row=pd.MultiIndex.from_arrays([['a','a','b','b'],[1,2,1,2]],names=['key1','key2'])
col=pd.MultiIndex.from_arrays([['Ohio','Ohio','Colorado'],['Green','Red','Green']],
                              names=['state','color'])

#复用MultiIndex构造多层索引dataframe
frame = DataFrame(np.arange(12).reshape((4, 3)),index=row,columns=col)
frame

#多层索引的重新分级排序
frame.swaplevel('key1', 'key2')    #swaplevel接受两个级别编号或名称,返回交换级别的新对象(不改变源数据)
frame.swaplevel(-2,-1,axis=1)      #通过级别编号和axis关键字交换列级别

#排序(按索引)
frame.sortlevel(1)    #默认axis=0(行),level=0(外层)
#注意sortlevel is deprecated, use sort_index(level= ...)
frame.sort_index(level=1)  #建议使用sort_index()

#交换索引级别并排序
frame.swaplevel(0, 1).sort_index(level=0)

#根据级别汇总统计,类似于pandas的groupby
frame.sum(level='key2')
frame.sum(level='color', axis=1)

#行索引与列(不是列索引)的转换
frame = DataFrame({'a': range(7), 'b': range(7, 0, -1),
                   'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],
                   'd': [0, 1, 2, 0, 1, 2, 3]})
frame
Пример #36
0
ser[:, 'a']

# creating dataframe from multi-index level Series
dframe = ser.unstack()
dframe

# construct dataframe with multiple index levels
dframe2 = DataFrame(np.arange(16).reshape(4, 4),
                    index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                    columns=[['NY', 'NY', 'LA', 'SF'],
                             ['cold', 'hot', 'hot', 'cold']])

dframe2

# naming indexes and columns
dframe2.index.names = ['INDEX_1', 'INDEX_2']
dframe2.columns.names = ['Cities', 'Temp']
dframe2

# Interchange index level orders
dframe2.swaplevel('Cities', 'Temp', axis=1)

# sorting levels
dframe2.sortlevel(level=1, axis=0)
dframe2.sortlevel(level=0, axis=0)
dframe2.sortlevel(level=1, axis=1)
dframe2.sortlevel(level=0, axis=1)

# summing on a specific level
dframe2.sum(level='Temp', axis=1)
Пример #37
0
              index=[list('aabb'), list('1212')],
              columns=[['ohio', 'ohio', 'colorado'], ['green', 'red',
                                                      'green']])
#查看索引
s.index
#查看标签c的内容
s['c']
#查看标签b和d的内容
s[['b', 'd']]
#查看标签b到d的内容
s['b':'d']
#把s转化为DataFrame
s.unstack()
#再转回来
s.unstack.stack()
#查看索引
y.index
#给各层起名字
y.index.names = ['key1', 'key2']
y.columns.names = ['state', 'color']
#选取分组
y['ohio']
#互换索引级别
y.swaplevel('key1', 'key2')
#或者
y.swaplevel(0, 1)
#根据单个级别中的值进行排序,可能会产生future warning
y.sortlevel(1)
#改进
y.sort_index(level=1)
Пример #38
0
df3
df3.ix["b", ].ix[1:, ]
df3.ix["a":"b",].ix[1:, ]
df3.unstack().unstack()

frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'],
                           ['Green', 'Red', 'Green']])
frame
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame
frame.swaplevel("key1", "key2")
frame
frame.sortlevel(0)
frame
frame.sum(level="key1")

frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=[['Ohio', 'Ohio', 'Colorado'],
                           ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame.sum(level="color", axis=1)
frame.sum(level="state", axis=1)

ser = Series(np.arange(3.), index=list("abc"))
ser
ser[-1]
frame.cov()
frame2.ix['f'] = np.random.randn(3)
frame.corrwith(frame2)
frame.corrwith(um)
frame.corrwith(um.to_frame())
frame.ix[:, 'Washu':'UMST'].apply(lambda x: x.mean())
frame.set_index('UM', drop=True, inplace=True)
keys = frame.index
frame.reset_index(level=keys)

df = DataFrame(np.random.randn(6, 5),
               columns=['Ohio', 'Dallas', 'Michigan', 'Miami', 'DC'],
               index=[['a', 'a', 'b', 'b', 'c', 'd'], [1, 2, 3, 1, 2, 3]])
df.index
df.ix['a']
df.sortlevel(level=0, axis=0)
df.sortlevel(level=1, axis=0)
df.swaplevel(0, 1)
df_unstack = df.swaplevel(0, 1).unstack()  #turn the column into Multiindex

import pandas.io.data as web
data = {}
for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:
    data[ticker] = web.get_data_yahoo(ticker, '1/1/2000', '1/1/2010')
p = pd.Panel(data)
p2 = p.swapaxes('items', 'major')
"""
Extra
"""
obj.values
np.exp(obj)
# -*- coding: utf-8 -*- 

import numpy as np
from pandas import Series, DataFrame

print '索引层级交换'
frame = DataFrame(np.arange(12).reshape((4, 3)),
                  index = [['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns = [['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2']
frame_swapped = frame.swaplevel('key1', 'key2')
print frame_swapped
print frame_swapped.swaplevel(0, 1)
print

print '根据索引排序'
print frame.sortlevel('key2')
print frame.swaplevel(0, 1).sortlevel(0)
Пример #41
0
                    columns=[["NY", "NY", "LA", "SF"],
                             ["cold", "hot", "hot", "cold"]])

print(dframe2)
print("=" * 50)

#naming the index label
dframe2.index.names = ["INDEX_1", "iNDEX_2"]
dframe2.columns.names = ["Cities", "Temp"]
print(dframe2)
print("=" * 50)

#swapping index level
dframe2.swaplevel("Cities", "Temp", axis=1)
print(dframe2)
print("=" * 50)

#sorting level

#soring the second index (inner) index
print(dframe2.sortlevel(1))
print("=" * 50)

#sorting by outer index note the sortlevel() argment is 0
print(dframe2.sortlevel(0))
print("=" * 50)

#computing sum in temp level
print(dframe2.sum(level="Temp", axis=1))
print("=" * 50)
Пример #42
0
def main():
    """
    Handling of not applicable values
    """

    data = Series(np.random.randn(10),
                  index=[list('aaabbbccdd'), map(int, list('1231231223'))])
    print data
    print data.index
    print type(data.index)

    print data['b']
    print data['b':'c']
    print data.ix[['b', 'd']]
    print data[:, 2]

    print data.unstack()
    print data.unstack().stack()

    print '',''
    frame = DataFrame(np.arange(12).reshape((4, 3)),
                      index=[['a','a','b','b'], [1,2,1,2]],
                      columns=[['Ohio', 'Ohio', 'Colorado'],
                               ['Green', 'Red', 'Green']])
    print frame
    frame.index.names = ['key1', 'key2']
    frame.columns.names = ['state', 'color']
    print frame

    print frame['Ohio']
    print MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']],
                                 names=['state', 'color'])

    # change hierarchy and sort
    print '',''
    print frame.swaplevel('key1', 'key2')
    print '',''
    print frame.sortlevel(1) # sorted by key2
    print '',''
    print frame.swaplevel(0, 1).sortlevel(0) # swap and sorted by key 2

    # summary statistics for each hierarchy
    print '',''
    print frame.sum(level='key2')
    print '',''
    print frame.sum(level='color', axis=1)
    print '',''

    # Using column of the DataFrame for index
    print '','-------------------------'
    frame = DataFrame({
        'a': range(7),
        'b': range(7, 0, -1),
        'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],
        'd': [0, 1, 2, 0, 1, 2, 3],
    })
    print frame
    frame2 = frame.set_index(['c', 'd'])
    print '',''
    print frame2
    print '',''
    print frame.set_index(['c', 'd'], drop=False)
    print '',''
    print frame2.reset_index()
Пример #43
0
class TestMultiLevel(unittest.TestCase):
    def setUp(self):
        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.frame = DataFrame(np.random.randn(10, 3), index=index, columns=Index(["A", "B", "C"], name="exp"))

        self.single_level = MultiIndex(levels=[["foo", "bar", "baz", "qux"]], labels=[[0, 1, 2, 3]], names=["first"])

        # create test series object
        arrays = [
            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
            ["one", "two", "one", "two", "one", "two", "one", "two"],
        ]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        tm.N = 100
        self.tdf = tm.makeTimeDataFrame()
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.levels = [lev.astype("i8") for lev in self.ymd.index.levels]
        self.ymd.index.names = ["year", "month", "day"]

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a["A"].append(b["A"])
        tm.assert_series_equal(result, self.frame["A"])

    def test_reindex_level(self):
        # axis=0
        month_sums = self.ymd.sum(level="month")
        result = month_sums.reindex(self.ymd.index, level=1)
        expected = self.ymd.groupby(level="month").transform(np.sum)

        assert_frame_equal(result, expected)

        # Series
        result = month_sums["A"].reindex(self.ymd.index, level=1)
        expected = self.ymd["A"].groupby(level="month").transform(np.sum)
        assert_series_equal(result, expected)

        # axis=1
        month_sums = self.ymd.T.sum(axis=1, level="month")
        result = month_sums.reindex(columns=self.ymd.index, level=1)
        expected = self.ymd.groupby(level="month").transform(np.sum).T
        assert_frame_equal(result, expected)

    def test_binops_level(self):
        def _check_op(opname):
            op = getattr(DataFrame, opname)
            month_sums = self.ymd.sum(level="month")
            result = op(self.ymd, month_sums, level="month")
            broadcasted = self.ymd.groupby(level="month").transform(np.sum)
            expected = op(self.ymd, broadcasted)
            assert_frame_equal(result, expected)

            # Series
            op = getattr(Series, opname)
            result = op(self.ymd["A"], month_sums["A"], level="month")
            broadcasted = self.ymd["A"].groupby(level="month").transform(np.sum)
            expected = op(self.ymd["A"], broadcasted)
            assert_series_equal(result, expected)

        _check_op("sub")
        _check_op("add")
        _check_op("mul")
        _check_op("div")

    def test_pickle(self):
        import cPickle

        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[("foo", "one"), ("bar", "one")]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df["foo", "one"]
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ("foo", "four"))
        self.assertRaises(KeyError, df.__getitem__, "foobar")

    def test_series_getitem(self):
        s = self.ymd["A"]

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s.reindex(s.index[42:65])
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s.reindex(s.index[49:51])
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_getitem_corner(self):
        s = self.ymd["A"]

        # don't segfault, GH #495
        # out of bounds access
        self.assertRaises(IndexError, s.__getitem__, len(self.ymd))

        # generator
        result = s[(x > 0 for x in s)]
        expected = s[s > 0]
        assert_series_equal(result, expected)

    def test_series_setitem(self):
        s = self.ymd["A"]

        s[2000, 3] = np.nan
        self.assert_(isnull(s.values[42:65]).all())
        self.assert_(notnull(s.values[:42]).all())
        self.assert_(notnull(s.values[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_frame_getitem_setitem_slice(self):
        # getitem
        result = self.frame.ix[:4]
        expected = self.frame[:4]
        assert_frame_equal(result, expected)

        # setitem
        cp = self.frame.copy()
        cp.ix[:4] = 0

        self.assert_((cp.values[:4] == 0).all())
        self.assert_((cp.values[4:] != 0).all())

    def test_frame_getitem_setitem_multislice(self):
        levels = [["t1", "t2"], ["a", "b", "c"]]
        labels = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 1]]
        midx = MultiIndex(labels=labels, levels=levels, names=[None, "id"])
        df = DataFrame({"value": [1, 2, 3, 7, 8]}, index=midx)

        result = df.ix[:, "value"]
        assert_series_equal(df["value"], result)

        result = df.ix[1:3, "value"]
        assert_series_equal(df["value"][1:3], result)

        result = df.ix[:, :]
        assert_frame_equal(df, result)

        result = df
        df.ix[:, "value"] = 10
        result["value"] = 10
        assert_frame_equal(df, result)

        df.ix[:, :] = 10
        assert_frame_equal(df, result)

    def test_getitem_tuple_plus_slice(self):
        # GH #671
        df = DataFrame({"a": range(10), "b": range(10), "c": np.random.randn(10), "d": np.random.randn(10)})

        idf = df.set_index(["a", "b"])

        result = idf.ix[(0, 0), :]
        expected = idf.ix[0, 0]
        expected2 = idf.xs((0, 0))

        assert_series_equal(result, expected)
        assert_series_equal(result, expected2)

    def test_xs(self):
        xs = self.frame.xs(("bar", "two"))
        xs2 = self.frame.ix[("bar", "two")]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs("foo")
        result2 = self.frame.ix["foo"]
        expected = self.frame.T["foo"].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_xs_level(self):
        result = self.frame.xs("two", level="second")
        expected = self.frame[self.frame.index.get_level_values(1) == "two"]
        expected.index = expected.index.droplevel(1)

        assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([("x", "y", "z"), ("a", "b", "c"), ("p", "q", "r")])
        df = DataFrame(np.random.randn(3, 5), index=index)
        result = df.xs("c", level=2)
        expected = df[1:2]
        expected.index = expected.index.droplevel(2)
        assert_frame_equal(result, expected)

    def test_xs_level_multiple(self):
        from pandas import read_table
        from StringIO import StringIO

        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep="\s+")

        result = df.xs(("a", 4), level=["one", "four"])
        expected = df.xs("a").xs(4, level="four")
        assert_frame_equal(result, expected)

    def test_xs_level0(self):
        from pandas import read_table
        from StringIO import StringIO

        text = """                      A       B       C       D        E
one two three   four
a   b   10.0032 5    -0.5109 -2.3358 -0.4645  0.05076  0.3640
a   q   20      4     0.4473  1.4152  0.2834  1.00661  0.1744
x   q   30      3    -0.6662 -0.5243 -0.3580  0.89145  2.5838"""

        df = read_table(StringIO(text), sep="\s+")

        result = df.xs("a", level=0)
        expected = df.xs("a")
        self.assertEqual(len(result), 2)
        assert_frame_equal(result, expected)

    def test_xs_level_series(self):
        s = self.frame["A"]
        result = s[:, "two"]
        expected = self.frame.xs("two", level=1)["A"]
        assert_series_equal(result, expected)

        s = self.ymd["A"]
        result = s[2000, 5]
        expected = self.ymd.ix[2000, 5]["A"]
        assert_series_equal(result, expected)

        # not implementing this for now

        self.assertRaises(TypeError, s.__getitem__, (2000, slice(3, 4)))

        # result = s[2000, 3:4]
        # lv =s.index.get_level_values(1)
        # expected = s[(lv == 3) | (lv == 4)]
        # expected.index = expected.index.droplevel(0)
        # assert_series_equal(result, expected)

        # can do this though

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)), index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df["foo"]
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df["bar"]
        result2 = df.ix[:, "bar"]

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_setitem_slice_integers(self):
        index = MultiIndex(levels=[[0, 1, 2], [0, 2]], labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

        frame = DataFrame(np.random.randn(len(index), 4), index=index, columns=["a", "b", "c", "d"])
        res = frame.ix[1:2]
        exp = frame.reindex(frame.index[2:])
        assert_frame_equal(res, exp)

        frame.ix[1:2] = 7
        self.assert_((frame.ix[1:2] == 7).values.all())

        series = Series(np.random.randn(len(index)), index=index)

        res = series.ix[1:2]
        exp = series.reindex(series.index[2:])
        assert_series_equal(res, exp)

        series.ix[1:2] = 7
        self.assert_((series.ix[1:2] == 7).values.all())

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_getitem_slice_not_sorted(self):
        df = self.frame.sortlevel(1).T

        # buglet with int typechecking
        result = df.ix[:, : np.int32(3)]
        expected = df.reindex(columns=df.columns[:3])
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft["foo", "two"]
        dft["foo", "two"] = s > s.median()
        assert_series_equal(dft["foo", "two"], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[("foo", "two")])
        assert_series_equal(reindexed["foo", "two"], s > s.median())

    def test_frame_setitem_ix(self):
        self.frame.ix[("bar", "two"), "B"] = 5
        self.assertEquals(self.frame.ix[("bar", "two"), "B"], 5)

        # with integer labels
        df = self.frame.copy()
        df.columns = range(3)
        df.ix[("bar", "two"), 1] = 7
        self.assertEquals(df.ix[("bar", "two"), 1], 7)

    def test_fancy_slice_partial(self):
        result = self.frame.ix["bar":"baz"]
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000, 2):(2000, 4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame["A"].sortlevel(0)
        self.assertRaises(Exception, self.frame.reset_index()["A"].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_delevel_infer_dtype(self):
        tuples = [tuple for tuple in cart_product(["foo", "bar"], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=["prm0", "prm1", "prm2"])
        df = DataFrame(np.random.randn(8, 3), columns=["A", "B", "C"], index=index)
        deleveled = df.reset_index()
        self.assert_(com.is_integer_dtype(deleveled["prm1"]))
        self.assert_(com.is_float_dtype(deleveled["prm2"]))

    def test_reset_index_with_drop(self):
        deleveled = self.ymd.reset_index(drop=True)
        self.assertEquals(len(deleveled.columns), len(self.ymd.columns))

        deleveled = self.series.reset_index()
        self.assert_(isinstance(deleveled, DataFrame))
        self.assert_(len(deleveled.columns) == len(self.series.index.levels) + 1)

        deleveled = self.series.reset_index(drop=True)
        self.assert_(isinstance(deleveled, Series))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ["first", "second"]
        result = self.frame.sortlevel(level="second")
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df["foo"] = "bar"
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft["foo", "three"] = "bar"

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(
            sorted_before.drop([("foo", "three")], axis=1), sorted_after.drop([("foo", "three")], axis=1)
        )

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype("i8")
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame["D"] = "foo"
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ["A", "B", "C"])

    def test_count_level_series(self):
        index = MultiIndex(
            levels=[["foo", "bar", "baz"], ["one", "two", "three", "four"]], labels=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]]
        )

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype("f8"), expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype("f8"), expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame["A"][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0], columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df["foo", "four"] = "foo"
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked["foo"], df["foo"].stack())
        self.assert_(stacked["bar"].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame(
            {
                "state": ["naive", "naive", "naive", "activ", "activ", "activ"],
                "exp": ["a", "b", "b", "b", "a", "a"],
                "barcode": [1, 2, 3, 4, 1, 3],
                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
                "extra": np.arange(6.0),
            }
        )

        result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)

        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked, result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, "first")
        self.assertEquals(unstacked.columns.names, ["exp", "second"])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack("second")
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack("second")
        result = unstacked.stack("exp")
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack("exp")
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(["year", "month"])
        expected = self.ymd.unstack("year").unstack("month")
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names, expected.columns.names)

        # series
        s = self.ymd["A"]
        s_unstacked = s.unstack(["year", "month"])
        assert_frame_equal(s_unstacked, expected["A"])

        restacked = unstacked.stack(["year", "month"])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1)
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1)
        assert_frame_equal(unstacked, expected)

    def test_groupby_transform(self):
        s = self.frame["A"]
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_groupby_corner(self):
        midx = MultiIndex(levels=[["foo"], ["bar"], ["baz"]], labels=[[0], [0], [0]], names=["one", "two", "three"])
        df = DataFrame([np.random.rand(4)], columns=["a", "b", "c", "d"], index=midx)
        # should work
        df.groupby(level="three")

    def test_join(self):
        a = self.frame.ix[:5, ["A"]]
        b = self.frame.ix[2:, ["B", "C"]]

        joined = a.join(b, how="outer").reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame["A"].swaplevel(0, 1)
        swapped2 = self.frame["A"].swaplevel("first", "second")
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel("second", "first")
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel("first", "second", axis=1)
        exp = self.frame.swaplevel("first", "second").T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({"ItemA": self.frame, "ItemB": self.frame * 2})

        result = panel.swaplevel(0, 1, axis="major")
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_reorder_levels(self):
        result = self.ymd.reorder_levels(["month", "day", "year"])
        expected = self.ymd.swaplevel(0, 1).swaplevel(1, 2)
        assert_frame_equal(result, expected)

        result = self.ymd["A"].reorder_levels(["month", "day", "year"])
        expected = self.ymd["A"].swaplevel(0, 1).swaplevel(1, 2)
        assert_series_equal(result, expected)

        result = self.ymd.T.reorder_levels(["month", "day", "year"], axis=1)
        expected = self.ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
        assert_frame_equal(result, expected)

        self.assertRaises(Exception, self.ymd.index.reorder_levels, [1, 2, 3])

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        x = Series(data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B", 3)]))

        y = Series(data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B", 3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df["foo"].values[:] = 0
        self.assert_((df["foo"].values == 0).all())

        # but not if it's mixed-type
        df["foo", "four"] = "foo"
        df = df.sortlevel(0, axis=1)
        df["foo"]["one"] = 2
        self.assert_((df["foo", "one"] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df["foo", "four"] = "foo"

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df["foo"]
        result2 = df.ix[:, "foo"]
        expected = df.reindex(columns=df.columns[arrays[0] == "foo"])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs("foo")
        result2 = df.ix["foo"]
        expected = df.reindex(df.index[arrays[0] == "foo"])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [
            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
            ["one", "two", "one", "two", "one", "two", "one", "two"],
        ]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s["qux"]
        result2 = s.ix["qux"]
        expected = s[arrays[0] == "qux"]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    def test_count(self):
        frame = self.frame.copy()
        frame.index.names = ["a", "b"]

        result = frame.count(level="b")
        expect = self.frame.count(level=1)
        assert_frame_equal(result, expect)

        result = frame.count(level="a")
        expect = self.frame.count(level=0)
        assert_frame_equal(result, expect)

        series = self.series.copy()
        series.index.names = ["a", "b"]

        result = series.count(level="b")
        expect = self.series.count(level=1)
        assert_series_equal(result, expect)

        result = series.count(level="a")
        expect = self.series.count(level=0)
        assert_series_equal(result, expect)

        self.assertRaises(Exception, series.count, "x")
        self.assertRaises(Exception, frame.count, level="x")

    AGG_FUNCTIONS = ["sum", "prod", "min", "max", "median", "mean", "skew", "mad", "std", "var"]

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS, range(2), [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS, range(2), range(2), [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            aggf = lambda x: getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)

            # for good measure, groupby detail
            level_index = frame._get_axis(axis).levels[level]

            self.assert_(leftside._get_axis(axis).equals(level_index))
            self.assert_(rightside._get_axis(axis).equals(level_index))

            assert_frame_equal(leftside, rightside)

    def test_frame_series_agg_multiple_levels(self):
        result = self.ymd.sum(level=["year", "month"])
        expected = self.ymd.groupby(level=["year", "month"]).sum()
        assert_frame_equal(result, expected)

        result = self.ymd["A"].sum(level=["year", "month"])
        expected = self.ymd["A"].groupby(level=["year", "month"]).sum()
        assert_series_equal(result, expected)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([("foo", "one"), ("foo", "two"), ("bar", "one"), ("bar", "two")])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df["Totals", ""] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd["A"].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd["A"].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df["A"].ix[2000, 4] = 1
        exp["A"].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df["A"].ix[14] = 5
        self.assertEquals(df["A"][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd["E"] = "foo"
        self.ymd["F"] = 2

        unstacked = self.ymd.unstack("month")
        self.assert_(unstacked["A", 1].dtype == np.float64)
        self.assert_(unstacked["E", 1].dtype == np.object_)
        self.assert_(unstacked["F", 1].dtype == np.float64)

    def test_getitem_lowerdim_corner(self):
        self.assertRaises(KeyError, self.frame.ix.__getitem__, (("bar", "three"), "B"))

        self.assertRaises(KeyError, self.frame.ix.__setitem__, (("bar", "three"), "B"), 0)

    # ----------------------------------------------------------------------
    # AMBIGUOUS CASES!

    def test_partial_ix_missing(self):
        raise nose.SkipTest

        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]["A"]
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    def test_fancy_2d(self):
        raise nose.SkipTest

        result = self.frame.ix["foo", "B"]
        expected = self.frame.xs("foo")["B"]
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix["B", "foo"]
        expected = ft.xs("B")["foo"]
        assert_series_equal(result, expected)

    # ----------------------------------------------------------------------

    def test_to_html(self):
        self.ymd.columns.name = "foo"
        self.ymd.to_html()
        self.ymd.T.to_html()

    def test_level_with_tuples(self):
        index = MultiIndex(
            levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]],
            labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
        )

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[("foo", "bar", 0)]
        result2 = series.ix[("foo", "bar", 0)]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        self.assertRaises(KeyError, series.__getitem__, (("foo", "bar", 0), 2))

        result = frame.ix[("foo", "bar", 0)]
        result2 = frame.xs(("foo", "bar", 0))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        index = MultiIndex(
            levels=[[("foo", "bar"), ("foo", "baz"), ("foo", "qux")], [0, 1]],
            labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
        )

        series = Series(np.random.randn(6), index=index)
        frame = DataFrame(np.random.randn(6, 4), index=index)

        result = series[("foo", "bar")]
        result2 = series.ix[("foo", "bar")]
        expected = series[:2]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

        result = frame.ix[("foo", "bar")]
        result2 = frame.xs(("foo", "bar"))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_int_series_slicing(self):
        s = self.ymd["A"]
        result = s[5:]
        expected = s.reindex(s.index[5:])
        assert_series_equal(result, expected)

        exp = self.ymd["A"].copy()
        s[5:] = 0
        exp.values[5:] = 0
        self.assert_(np.array_equal(s.values, exp.values))

        result = self.ymd[5:]
        expected = self.ymd.reindex(s.index[5:])
        assert_frame_equal(result, expected)

    def test_mixed_depth_get(self):
        arrays = [
            ["a", "top", "top", "routine1", "routine1", "routine2"],
            ["", "OD", "OD", "result1", "result2", "result1"],
            ["", "wx", "wy", "", "", ""],
        ]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df["a"]
        expected = df["a", "", ""]
        assert_series_equal(result, expected)
        self.assertEquals(result.name, "a")

        result = df["routine1", "result1"]
        expected = df["routine1", "result1", ""]
        assert_series_equal(result, expected)
        self.assertEquals(result.name, ("routine1", "result1"))

    def test_mixed_depth_insert(self):
        arrays = [
            ["a", "top", "top", "routine1", "routine1", "routine2"],
            ["", "OD", "OD", "result1", "result2", "result1"],
            ["", "wx", "wy", "", "", ""],
        ]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df.copy()
        expected = df.copy()
        result["b"] = [1, 2, 3, 4]
        expected["b", "", ""] = [1, 2, 3, 4]
        assert_frame_equal(result, expected)

    def test_mixed_depth_drop(self):
        arrays = [
            ["a", "top", "top", "routine1", "routine1", "routine2"],
            ["", "OD", "OD", "result1", "result2", "result1"],
            ["", "wx", "wy", "", "", ""],
        ]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        result = df.drop("a", axis=1)
        expected = df.drop([("a", "", "")], axis=1)
        assert_frame_equal(expected, result)

        result = df.drop(["top"], axis=1)
        expected = df.drop([("top", "OD", "wx")], axis=1)
        expected = expected.drop([("top", "OD", "wy")], axis=1)
        assert_frame_equal(expected, result)

    def test_mixed_depth_pop(self):
        arrays = [
            ["a", "top", "top", "routine1", "routine1", "routine2"],
            ["", "OD", "OD", "result1", "result2", "result1"],
            ["", "wx", "wy", "", "", ""],
        ]

        tuples = zip(*arrays)
        tuples.sort()
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop("a")
        expected = df2.pop(("a", "", ""))
        assert_series_equal(expected, result)
        assert_frame_equal(df1, df2)
        self.assertEquals(result.name, "a")

        expected = df1["top"]
        df1 = df1.drop(["top"], axis=1)
        result = df2.pop("top")
        assert_frame_equal(expected, result)
        assert_frame_equal(df1, df2)
Пример #44
0
import numpy as np
from pandas import Series, DataFrame
import pandas as pd
from numpy.random import randn
ser = Series(np.random.randn(6),
             index=[[1, 1, 1, 2, 2, 2], ['a', 'b', 'c', 'a', 'b', 'c']])
ser[2]
print(ser[:, 'a'])
dframe = ser.unstack()  #Seriesのindexを使ってデータフレームにする
dframe.T.unstack()  #データフレームからシリーズに
dframe2 = DataFrame(np.arange(16).reshape((4, 4)),
                    index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                    columns=[['NY', 'NY', 'LA', 'SF'],
                             ['cold', 'hot', 'hot', 'cold']])
dframe2.index.names = ['INDEX_1', 'INDEX_2']  #行方向のindexに名前をつける
dframe2.columns.names = ['Cities', 'Temp']  #カラムの名前をつける
dframe2.swaplevel('Cities', 'Temp', axis=1)  #行方向のIndexを入れ替える
dframe2.sortlevel(1)  #2列目のindexをソートする(並べ替える)
dframe2.sum(level='Temp', axis=1)  #Tempに注目して都市名を無視して合計を計算してくれる
Пример #45
0
data.ix['b','d']
data.ix[['b','d']]
data[:,2]
data.unstack()
data.unstack().stacl()
data.unstack().stack()
frame = DataFrame(np.arange(12).reshape((4,3)),index=[['a','a','b','b'],[1,2,1,2]],columns=[['Ohio','Ohio','Colorado'],['Green','Red','Green']])
frame
frame.index.names=['key1','key2']
frame.columns.names['state','color']
frame.columns.names=['state','color']
frame
frame['Ohio']
MultiIndex.from_arrays
frame.swaplevel('key1','key2')
frame.sortlevel(1)
frame.swaplevel(0,1).sortlevel(0)
frame.sum(level='key2')
frame.sum(level='color',axis=1)
frame = DataFrame({'a':range(7),'b':range(7,0,-1), 'c':['one','one','one','two','two','two','two'],'d':[0,1,2,0,1,2,3]})
frame
frame2 = frame.set_index('c','d'])
frame2 = frame.set_index(['c','d'])
frame2
frame2 = frame.set_index(['c','d'],drop=False)
frame2
frame2.reset_index()
ser = Series(np.arange(3))
ser
ser[-1]
ser2 = Series(np.arange(3.),index=['a','b','c'])
Пример #46
0
data1.index.names = ['key1','key2']
data1.columns.names= ['state','color']
data1.index.name='key'
data1.columns.name='st'
data1


# In[642]:

#重排分级顺序
#有时,你需要重新调整某条轴上各级别的顺序,或根据指定级别上的值对数据进行排序。swaplevel接受两个级别编号或名称,并返回一个互换了级别的新对象(但数据不会发生变化)

data1.swaplevel('key1','key2')
#而sortlevel则根据单个级别中的值对数掘进行排序(稳定的) 。交换级别时,常常也会用到 sortlevel ,这样最终结果就是有序的了:

data1.sortlevel(0)

#注意: 在层次化索号|的对象上,如果索引是按字典方式从外到内排序(目r1调用sortlevel(0) 或sort_index()的结果),数据选取操作的性能要好很多。

#根据级别汇总统计
data1.sum(level= 'key2')   #frame.sum(level='color’, axis=l)


# In[242]:

#使用DataFrame 的列    列转索引 或者索引转列
#人们经常想要将DataFrame的一个或多个列当做行索引来用, 或者可能希望将行索引变成DataFrame的列。以下面这个DataFrame为例:
import pandas as pd

from  pandas import DataFrame
Пример #47
0
dframe2 = DataFrame(np.arange(16).reshape(4,4),
                    index = [['a','a','b','b'],[1,2,1,2]],
                    columns = [['NY','NY','LA','SF'],['cold','hot','hot','cold']])

dframe2

# naming indexes and columns
dframe2.index.names = ['INDEX_1', 'INDEX_2']
dframe2.columns.names = ['Cities','Temp']
dframe2

# Interchange index level orders
dframe2.swaplevel('Cities','Temp',axis = 1)

# sorting levels
dframe2.sortlevel(level = 1, axis = 0)
dframe2.sortlevel(level = 0, axis = 0)
dframe2.sortlevel(level = 1, axis = 1)
dframe2.sortlevel(level = 0, axis = 1)

# summing on a specific level
dframe2.sum(level = 'Temp', axis = 1)








Пример #48
0
frame.columns.names = ['state', 'color']

print(frame)
print('\n')

###############################################################

'''
MultiIndex.from_arrays([['Ohio','Ohio','Colorado'],['Green','Red','Green']],
                        names = ['state','color'])
'''
###############################################################

print(frame.swaplevel('key1', 'key2'))
print('\n')
print(frame.sortlevel(1))
print('\n')
print(frame.sortlevel(0))
print('\n')
print(frame.swaplevel(0, 1).sortlevel(0))
print('\n')

###############################################################

print(frame.sum(level='key2'))
print('\n')
print(frame.sum(level='color',axis=1))
print('\n')

###############################################################
Пример #49
0
frame_swapped.swaplevel(0, 1)  # 交换回来
'''
           A       B
          A1  A2  B1
key1 key2
a    1     0   1   2
     2     3   4   5
b    1     6   7   8
     2     9  10  11
'''
print

print
'对某个索引层进行排序'
print
frame.sortlevel('key2')
'''
           A       B
          A1  A2  B1
key1 key2
a    1     0   1   2
b    1     6   7   8
a    2     3   4   5
b    2     9  10  11
'''
print
frame.swaplevel(0, 1).sortlevel(0)
'''
           A       B
          A1  A2  B1
key2 key1