time series data and NumPy
Diez B. Roggisch
deets at nospam.web.de
Fri Jan 26 18:29:34 CET 2007
> Good morning,
> I store time series data in a SQL database. The results of a typical
> query using pyodbc look like this.
> Date Close
> "2007-01-17" 22.57
> Where Date is a datetime.date object and Close is a float.
> I'd like to put this data in a NumPy array for processing, but am
> unsure as to how to handle the date. In the past I've used lists, but I
> am looking to boost speed a bit as I wish to do a large number of
> transformations and comparisons.
> Can one index an array using datetime objects?
> For example it would be nice to do a union of two arrays so that any
> dates missing in either one were eliminated.
> Thoughts on doing rolling operations, such as an n-period average or
> Thoughts on working with time series data in arrays in general?
I'm pretty sure you're out of luck here - even _if_ NumPy would handle
arbitrary data-types (AFAIK it doesn't, but then I'm not a total expert
there), it certainly won't be able to make its hi-performance functions
work on them.
What you could do would be to convert the date-column into a timestamp,
which is a int/long, and use that. Would that help?
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