A Saturday 30 August 2008, Alan Jackson escrigué:
> I tested all three offered solutions :
>
> t = table[:] # convert to structured array
> collections = np.unique(t['collection'])
> for collection in collections:
> cond = t['collection'] == collection
> energy_this_collection = t['energy']
I tested all three offered solutions :
t = table[:] # convert to structured array
collections = np.unique(t['collection'])
for collection in collections:
cond = t['collection'] == collection
energy_this_collection = t['energy'][cond]
--
energies = {}
for ro
A Friday 29 August 2008, Francesc Alted escrigué:
> A Friday 29 August 2008, Alan Jackson escrigué:
> > Looking for advice on a good way to handle this problem.
> >
> > I'm dealing with large tables (Gigabyte large). I would like to
> > efficiently subset values from one column based on the values
A Friday 29 August 2008, Alan Jackson escrigué:
> Looking for advice on a good way to handle this problem.
>
> I'm dealing with large tables (Gigabyte large). I would like to
> efficiently subset values from one column based on the values in
> another column, and get arrays out of the operation. Fo
Alan Jackson wrote:
> Looking for advice on a good way to handle this problem.
>
> I'm dealing with large tables (Gigabyte large). I would like to
> efficiently subset values from one column based on the values in
> another column, and get arrays out of the operation. For example,
> say I have 2 c
Looking for advice on a good way to handle this problem.
I'm dealing with large tables (Gigabyte large). I would like to
efficiently subset values from one column based on the values in
another column, and get arrays out of the operation. For example,
say I have 2 columns, "energy" and "collectio