熊猫重新采样到现有指数

Question

I have a long timeseries that ends with the following data.

               ABC     CDE
Date                      
2017-05-26  107.00  241.71
2017-05-30  107.27  241.50
2017-05-31  107.32  241.44
2017-06-01  107.10  243.36
2017-06-02  107.57  244.17

I would like to resample it so that it becomes monthly data but I am interested in retaining the actual last monthly dates in the time series. If I do,

df.resample('BM').last()

gives the following tail-end output

2017-05-31  107.32  241.44 
2017-06-30  107.57  244.17

which does not give the correct last date of the dataframe. There are other dates within the resampled dataframe that are off as well. Essentially Pandas isn't using the existing index to find the month end but it's own business day calendar.

Is there an option I can feed into the Pandas resampling function so that it uses the existing index to achieve the desired result which is

2017-05-31  107.32  241.44 
2017-06-02  107.57  244.17

Answer 1

You need create new column from index and last set_index :

df = df.assign(Date=df.index).resample('BM').last().set_index('Date')
print (df)
               ABC     CDE
Date                      
2017-05-31  107.32  241.44
2017-06-02  107.57  244.17

But if need resample by month period only:

m = df.index.to_period('m')
df = df.reset_index().groupby(m).last().set_index('Date')
print (df)
               ABC     CDE
Date                      
2017-05-31  107.32  241.44
2017-06-02  107.57  244.17

Answer 2

You can drop duplicates based on year and month and only keep the last row.

df.assign(m=df.index.to_period('m')).drop_duplicates('m','last').drop('m',1)
Out[728]: 
               ABC     CDE
Date                      
2017-05-31  107.32  241.44
2017-06-02  107.57  244.17

Or you can use group by year and month and then take the last row from each group.

df.reset_index()\
  .sort_values('Date')\
  .assign(m=df.index.to_period('m'))\
  .groupby(by='m')\
  .last()\
  .set_index('Date')
Out[677]: 
               ABC     CDE
Date                      
2017-05-31  107.32  241.44
2017-06-02  107.57  244.17