Pandas Rolling Computations on Sliding Windows (Unevenly spaced)

Perhaps it makes more sense to use rolling_sum:

pd.rolling_sum(ts, window=1, freq='1ms')

You can solve most problems of this sort with cumsum and binary search.

from datetime import timedelta

def msum(s, lag_in_ms):
    lag = s.index - timedelta(milliseconds=lag_in_ms)
    inds = np.searchsorted(s.index.astype(np.int64), lag.astype(np.int64))
    cs = s.cumsum()
    return pd.Series(cs.values - cs[inds].values + s[inds].values, index=s.index)

res = msum(ts, 100)
print pd.DataFrame({'a': ts, 'a_msum_100': res})


                            a  a_msum_100
2013-02-01 09:00:00.073479  5           5
2013-02-01 09:00:00.083717  8          13
2013-02-01 09:00:00.162707  1          14
2013-02-01 09:00:00.171809  6          20
2013-02-01 09:00:00.240111  7          14
2013-02-01 09:00:00.258455  0          14
2013-02-01 09:00:00.336564  2           9
2013-02-01 09:00:00.536416  3           3
2013-02-01 09:00:00.632439  4           7
2013-02-01 09:00:00.789746  9           9

[10 rows x 2 columns]

You need a way of handling NaNs and depending on your application, you may need the prevailing value asof the lagged time or not (ie difference between using kdb+ bin vs np.searchsorted).

Hope this helps.

This is an old question, but for those who stumble upon this from google: in pandas 0.19 this is built-in as the function

http://pandas.pydata.org/pandas-docs/stable/computation.html#time-aware-rolling

So to get 1 ms windows it looks like you get a Rolling object by doing

dft.rolling('1ms')

and the sum would be

dft.rolling('1ms').sum()