可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试):
问题:
I use TimeGrouper from pandas.tseries.resample to sum monthly return to 6M as follows:
6m_return = monthly_return.groupby(TimeGrouper(freq='6M')).aggregate(numpy.sum)
where monthly_return is like:
2008-07-01 0.003626 2008-08-01 0.001373 2008-09-01 0.040192 2008-10-01 0.027794 2008-11-01 0.012590 2008-12-01 0.026394 2009-01-01 0.008564 2009-02-01 0.007714 2009-03-01 -0.019727 2009-04-01 0.008888 2009-05-01 0.039801 2009-06-01 0.010042 2009-07-01 0.020971 2009-08-01 0.011926 2009-09-01 0.024998 2009-10-01 0.005213 2009-11-01 0.016804 2009-12-01 0.020724 2010-01-01 0.006322 2010-02-01 0.008971 2010-03-01 0.003911 2010-04-01 0.013928 2010-05-01 0.004640 2010-06-01 0.000744 2010-07-01 0.004697 2010-08-01 0.002553 2010-09-01 0.002770 2010-10-01 0.002834 2010-11-01 0.002157 2010-12-01 0.001034
The 6m_return is like:
2008-07-31 0.003626 2009-01-31 0.116907 2009-07-31 0.067688 2010-01-31 0.085986 2010-07-31 0.036890 2011-01-31 0.015283
However I want to get the 6m_return starting 6m from 7/2008 like the following:
2008-12-31 ... 2009-06-31 ... 2009-12-31 ... 2010-06-31 ... 2010-12-31 ...
Tried the different input options (i.e. loffset) in TimeGrouper but doesn't work. Any suggestion will be really appreciated!
回答1:
The problem can be solved by adding closed = 'left'
df.groupby(pd.TimeGrouper('6M', closed = 'left')).aggregate(numpy.sum)
回答2:
This is a workaround for what seems a bug, but give it a try and see if it works for you.
In [121]: ts = pandas.date_range('7/1/2008', periods=30, freq='MS') In [122]: df = pandas.DataFrame(pandas.Series(range(len(ts)), index=ts)) In [124]: df[0] += 1 In [125]: df Out[125]: 0 2008-07-01 1 2008-08-01 2 2008-09-01 3 2008-10-01 4 2008-11-01 5 2008-12-01 6 2009-01-01 7 2009-02-01 8 2009-03-01 9 2009-04-01 10 2009-05-01 11 2009-06-01 12 2009-07-01 13 2009-08-01 14 2009-09-01 15 2009-10-01 16 2009-11-01 17 2009-12-01 18 2010-01-01 19 2010-02-01 20 2010-03-01 21 2010-04-01 22 2010-05-01 23 2010-06-01 24 2010-07-01 25 2010-08-01 26 2010-09-01 27 2010-10-01 28 2010-11-01 29 2010-12-01 30
I've used integers to help confirm that the sums are correct. The workaround that seems to work is to add a month to the front of the dataframe to trick the TimeGrouper into doing what you need.
In [127]: df2 = pandas.DataFrame([0], index = [df.index.shift(-1, freq='MS')[0]]) In [129]: df2.append(df).groupby(pandas.TimeGrouper(freq='6M')).aggregate(numpy.sum)[1:] Out[129]: 0 2008-12-31 21 2009-06-30 57 2009-12-31 93 2010-06-30 129 2010-12-31 165
Note the final [1:] is there to trim off the first group.
回答3:
TimeGrouper that is suggested in other answers is deprecated and will be removed from Pandas. It is replaced with Grouper. So a solution to your question using Grouper is:
df.groupby(pd.Grouper(freq='6M', closed='left')).aggregate(numpy.sum)
