Resampling a pandas dataframe with multi-index containing timeseries

Question

apologies from creating what appears to be a duplicate of this question. I have a dataframe that is shaped more or less like the one below:

df_lenght = 240
d         


        

Answer 1

First let's define a resampler function:

def resampler(x):    
    return x.set_index('datetime').resample('D').mean().rolling(window=2).mean()

Then, we groupby job_id and apply the resampler function:

 df.reset_index(level=2).groupby(level=1).apply(resampler)

Out[657]: 
                          a         b
job_id datetime                      
job1   2017-06-23       NaN       NaN
       2017-06-24  0.053378  0.004727
       2017-06-25  0.265074  0.234081
       2017-06-26  0.192286  0.138148
job2   2017-06-26       NaN       NaN
       2017-06-27 -0.016629 -0.041284
       2017-06-28 -0.028662  0.055399
       2017-06-29  0.113299 -0.204670
job3   2017-06-29       NaN       NaN
       2017-06-30  0.233524 -0.194982
       2017-07-01  0.068839 -0.237573
       2017-07-02 -0.051211 -0.069917

Let me know if this is what you are after.