How to resample a dataframe with different functions applied to each column?

Question

I have a times series with temperature and radiation in a pandas dataframe. The time resolution is 1 minute in regular steps.

import datetime
im         


        

Answer 1

You need to use groupby as such:

grouped = frame.groupby(lambda x: x.hour)
grouped.agg({'radiation': np.sum, 'tamb': np.mean})
# Same as: grouped.agg({'radiation': 'sum', 'tamb': 'mean'})

with the output being:

        radiation      tamb
key_0                      
8      298.581107  4.883806
9      311.176148  4.983705
10     315.531527  5.343057
11     288.013876  6.022002
12       5.527616  8.507670

So in essence I am splitting on the hour value and then calculating the mean of tamb and the sum of radiation and returning back the DataFrame (similar approach to R's ddply). For more info I would check the documentation page for groupby as well as this blog post.

Edit: To make this scale a bit better you could group on both the day and time as such:

grouped = frame.groupby(lambda x: (x.day, x.hour))
grouped.agg({'radiation': 'sum', 'tamb': 'mean'})
          radiation      tamb
key_0                        
(5, 8)   298.581107  4.883806
(5, 9)   311.176148  4.983705
(5, 10)  315.531527  5.343057
(5, 11)  288.013876  6.022002
(5, 12)    5.527616  8.507670