[英]How to subtract second level columns in multiIndex level dataframe
[英]How to resort a MultiIndex DataFrame by second level
我有一個DataFrame
具有MultiIndex
。 索引字段是OptionSymbol
(級別0)和QuoteDatetime
(級別1)。 我已經對DataFrame
進行了索引和排序, DataFrame
所示:
sorted = df.sort_values(
['OptionSymbol', 'QuoteDatetime'],
ascending=[False, True]
)
indexed = sorted.set_index(
['OptionSymbol', 'QuoteDatetime'],
drop=True
)
結果如下:
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
... ... ... ... ... ...
如預期的那樣,首先在OptionSymbol
組中以OptionSymbol
降序和升序對幀進行排序。
我需要做的是立即使用QuoteDatetime
的第一個值,因此結果如下所示:
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
... ... ... ... ... ...
我嘗試了各種通過index = 1進行OptionSymbol
方法,但是后來我失去了OptionSymbol
組。 我該怎么做?
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,)))
))
在這種情況下,使用df.sort_index(level=1)
奇怪,但是在我的整個數據集(超過20列)上,我卻失去了OptionSymbol
分組的OptionSymbol
。
IIUC您可以簡單地按第二級對索引進行排序:
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
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