python - 如何通过第二层重新使用MultiIndex DataFrame

我有一个DataFrame和MultiIndex。索引字段是OptionSymbol（级别0）和QuoteDatetime（级别1）。我已经对DataFrame进行了索引和排序，如下所示：

sorted = df.sort_values(
    ['OptionSymbol', 'QuoteDatetime'],
    ascending=[False, True]
)

indexed = sorted.set_index(
    ['OptionSymbol', 'QuoteDatetime'],
    drop=True
)

结果如下：

                                      Id  Strike Expiration OptionType
OptionSymbol       QuoteDatetime
ZBYMZ              2013-09-02     234669   170.0 2011-01-22        put
                   2013-09-03     234901   170.0 2011-01-22        put
                   2013-09-04     235133   170.0 2011-01-22        put
  ...                     ...        ...     ...        ...        ...
YBWNA              2010-02-12     262202    95.0 2010-02-20       call
                   2010-02-16     262454    95.0 2010-02-20       call
                   2010-02-17     262707    95.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...
XWNAX              2012-07-12     262201    90.0 2010-02-20       call
                   2012-07-16     262453    90.0 2010-02-20       call
                   2012-07-17     262706    90.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...
WWWAX              2012-04-12     262201    90.0 2010-02-20       call
                   2012-04-16     262453    90.0 2010-02-20       call
                   2012-04-17     262706    90.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...

不出所料，首先在OptionSymbol组中按OptionSymbol降序对帧进行排序。

我需要做的是现在通过QuoteDatetime中的第一个值求助，因此结果如下所示：

                                      Id  Strike Expiration OptionType
OptionSymbol       QuoteDatetime
XBWNA              2010-02-12     262202    95.0 2010-02-20       call
                   2010-02-16     262454    95.0 2010-02-20       call
                   2010-02-17     262707    95.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...
NWWAX              2012-04-12     262201    90.0 2010-02-20       call
                   2012-04-16     262453    90.0 2010-02-20       call
                   2012-04-17     262706    90.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...
BWNAX              2012-07-12     262201    90.0 2010-02-20       call
                   2012-07-16     262453    90.0 2010-02-20       call
                   2012-07-17     262706    90.0 2010-02-20       call
  ...                     ...        ...     ...        ...        ...
XBYMZ              2013-09-02     234669   170.0 2011-01-22        put
                   2013-09-03     234901   170.0 2011-01-22        put
                   2013-09-04     235133   170.0 2011-01-22        put
  ...                     ...        ...     ...        ...        ...

我尝试了各种通过index = 1求助的方法，但是后来我失去了OptionSymbol组。我该怎么做？

使用代码进行编辑以重新创建

from collections import OrderedDict
df = OrderedDict((
    ('OptionSymbol', pd.Series(['ZBYMZ', 'ZBYMZ', 'ZBYMZ', 'YBWNA', 'YBWNA', 'YBWNA', 'XWNAX', 'XWNAX', 'XWNAX', 'WWWAX', 'WWWAX', 'WWWAX', ])),
    ('QuoteDatetime', pd.Series(['2013-09-02', '2013-09-03', '2013-09-04', '2010-02-12', '2010-02-16', '2010-02-17', '2012-07-12', '2012-07-16', '2012-07-17', '2012-04-12', '2012-04-16', '2012-04-17'])),
    ('Id', pd.Series(np.random.randn(12,))),
    ('Strike', pd.Series(np.random.randn(12,))),
    ('Expiration', pd.Series(np.random.randn(12,))),
    ('OptionType', pd.Series(np.random.randn(12,)))
))

在这种情况下，使用df.sort_index(level=1)怪异的方法可以解决问题，但是在我的整个数据集（超过20列）上，我丢失了OptionSymbol分组。

最佳答案

IIUC您可以简单地按第二级对索引进行排序：

In [27]: df.sort_index(level=1)
Out[27]:
                                Id  Strike  Expiration OptionType
OptionSymbol QuoteDatetime
YBWNA        2010-02-12     262202    95.0  2010-02-20       call
             2010-02-16     262454    95.0  2010-02-20       call
             2010-02-17     262707    95.0  2010-02-20       call
WWWAX        2012-04-12     262201    90.0  2010-02-20       call
             2012-04-16     262453    90.0  2010-02-20       call
             2012-04-17     262706    90.0  2010-02-20       call
XWNAX        2012-07-12     262201    90.0  2010-02-20       call
             2012-07-16     262453    90.0  2010-02-20       call
             2012-07-17     262706    90.0  2010-02-20       call
ZBYMZ        2013-09-02     234669   170.0  2011-01-22        put
             2013-09-03     234901   170.0  2011-01-22        put
             2013-09-04     235133   170.0  2011-01-22        put