How to downsampling time series data in pandas?

Question

I have a time series in pandas that looks like this (order by id):

id    time    value
 1       0        2
 1       1        4
 1       2        5
 1       3       10
 1       4       15
 1       5       16
 1       6       18
 1       7       20
 2      15        3
 2      16        5
 2      17        8
 2      18       10
 4       6        5
 4       7        6

I want downsampling time from 1 minute to 3 minutes for each group id. And value is a maximum of group (id and 3 minutes).

The output should be like:

id    time    value
 1       0        5
 1       1       16
 1       2       20
 2       0        8
 2       1       10
 4       0        6

I tried loop it take long time process.

Any idea how to solve this for large dataframe?

Thanks!

Answer 1

You can convert your time series to an actual timedelta, then use resample for a vectorized solution:

t = pd.to_timedelta(df.time, unit='T')
s = df.set_index(t).groupby('id').resample('3T').last().reset_index(drop=True)
s.assign(time=s.groupby('id').cumcount())

   id  time  value
0   1     0      5
1   1     1     16
2   1     2     20
3   2     0      8
4   2     1     10
5   4     0      6