Hello Fernando,
I personally have always found the 64-bit time stamps to be much more
useful (and much less ambiguous) than Python datetime objects.
However, if against my better judgement, you decide to store datetimes, you
effectively have three options (in preferred order):
1. Create a numpy dtype or PyTables description that matches the
structure of datetime to your desired precision and save them in a Table.
If you want to save timezone information as well, I might add an extra
length-3 string column and save the str representation of the tzinfo field
('UTC', 'KST', 'EDT').
2. Look into the new numpy native datetime type. I am not sure how they
are storing this under the covers; it might be as time stamps plus some
meta-data (which would be easy for use to store). Then it would be nice
for PyTables to have an appropriate Col subclass to go along with this.
3. Store an array of pickled datatime objects (and possibly index it).
This is ill-advised as it will likely be slow.
I would go with (1). Number (2) should probably be done eventually (and it
would be great if you did some leg work and had a pull request ~_~).
Be Well
Anthony
On Thu, Nov 3, 2011 at 12:26 AM, Fernando Paolo <fspa...@gmail.com> wrote:
> Hi Ben,
>
> Thanks for the reply. Actually that's the way my measurements come (a
> single number representing seconds since a reference epoch). What I
> need is to fractionate this number into the respective time elements
> (year, month, day, hour, secs, micro secs) and operate with them. So I
> would appreciate any suggestion on how to properly store (or
> represent) these on a very large table in order to be able to perform
> queries (without inconvenient transformations on the fly).
>
> Thank you - Fernando
>
>
> On Wed, Nov 2, 2011 at 9:55 PM, Ben Elliston <b...@air.net.au> wrote:
> > Hi Fernando
> >
> > I don't know about datetime, per se, but you can also convert your
> > times/dates into time since Unix epoch and store that as a single
> > 64-bit integer.
> >
> > Cheers, Ben
> >
> >
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>
>
> --
> Fernando Paolo
> Institute of Geophysics & Planetary Physics
> Scripps Institution of Oceanography
> University of California, San Diego
> 9500 Gilman Drive
> La Jolla, CA 92093-0225
>
> web: fspaolo.net
>
>
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