Python, Pandas: Use The Groupby.groups Description To Apply It To Another Grouping

Let's consider a DataFrame that contains 1 row of 2 values per each day of the month of Jan 2010: date_range = pd.date_range(dt(2010,1,1), dt(2010,1,31), freq='1D') df = pd.DataFra

Solution 1:

One simple solution to your problem would be to make sure the observations dataframe contains all the dates that the df dataframe does. You can do this with the reindex method. You will then have the exact same groups. You can also use resample('W') instead of groupby(pd.Timegrouper('W'))

obs2 = observations.reindex(df.index)

obs2.resample('W').groups

{Timestamp('2010-01-03 00:00:00', freq='W-SUN'): 3,
 Timestamp('2010-01-10 00:00:00', freq='W-SUN'): 10,
 Timestamp('2010-01-17 00:00:00', freq='W-SUN'): 17,
 Timestamp('2010-01-24 00:00:00', freq='W-SUN'): 24,
 Timestamp('2010-01-31 00:00:00', freq='W-SUN'): 31}

And if we do a simple aggregation like sum we can see the results of both frames

df.resample('W').sum()012010-01-03  1.9905582.5551912010-01-10  2.7077773.7717562010-01-17  2.7998973.3533632010-01-24  3.1654792.7788702010-01-31  4.9465773.394211

And now with obs2 which has 2 missing groups

obs2.resample('W').sum()012010-01-03       NaNNaN2010-01-10       NaNNaN2010-01-17  0.1723410.1371362010-01-24  1.7524722.3753062010-01-31  0.7115250.124271