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

IIUC, you wish to group by job_id and (daily) datetimes, and wish to ignore the first level of the DataFrame index. Therefore, instead of grouping by

( [ level_values(i) for i in [0,1] ] + [ pd.Grouper(freq='D', level=2) ] )

you'd want to groupby

[df.index.get_level_values(1), pd.Grouper(freq='D', level=2)]

---

import numpy as np
import pandas as pd
np.random.seed(2017)

df_length = 240
df = pd.DataFrame(np.random.randn(df_length,2), columns=['a','b'] )
df['datetime'] = pd.date_range('23/06/2017', periods=df_length, freq='H')

unique_jobs = ['job1','job2','job3',]
job_id = [unique_jobs for i in range (1, int((df_length/len(unique_jobs))+1) ,1) ]
df['job_id'] = sorted( [val for sublist in job_id for val in sublist] )

df.set_index(['job_id','datetime'], append=True, inplace=True)

grouped = df.groupby([df.index.get_level_values(1), pd.Grouper(freq='D', level=2)])
result = grouped.mean().rolling(window=2).mean()

print(result)

yields

                          a         b
job_id datetime                      
job1   2017-06-23       NaN       NaN
       2017-06-24 -0.203083  0.176141
       2017-06-25 -0.077083  0.072510
       2017-06-26 -0.237611 -0.493329
job2   2017-06-26 -0.297775 -0.370543
       2017-06-27  0.005124  0.052603
       2017-06-28  0.226142 -0.015584
       2017-06-29 -0.065595  0.210628
job3   2017-06-29 -0.186865  0.347683
       2017-06-30  0.051508  0.029909
       2017-07-01  0.005341  0.075378
       2017-07-02 -0.027131  0.132192