sqlite - timeseries database to use with python -


i have application written in python, stores values in text file in format of "datetime value". worked fine far.

the problem need start retrieving data time intervals. have converted files sqlite database. find performance poor. ran queries like:

select min(value) data dt > '2013-05-13 15:48:13' , dt < '2013-05-13 15:49:13'

so lowest time interval 1m.

but seems take abou 0.036s slow when need produce graphs small time intervals.

what other approach suggest use problem.

if don't have tooooo data, load memory pandas timeseries.

import pandas pd ts = pd.timeseries(range(86400), index=pd.datetimeindex(start='2013-05-14 00:00:00', freq='1s', periods=86400))

creates timeseries 86400 values each second of today's day.

the following line needs 2.72ms , returns value awaited:

ts.between_time('2013-05-14 15:48:13', '2013-05-14 15:49:13').min()

you can have different frequency , not equally spaced values well:

> pd.timeseries([1,2,3], index=pd.datetimeindex([datetime(2013,5,14,0,0,0,100000), datetime(2013,5,14,0,0,0,200000), datetime(2013,5,14,0,0,0,900000)]))  2013-05-14 00:00:00.100000    1 2013-05-14 00:00:00.200000    2 2013-05-14 00:00:00.900000    3

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