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 row in table:
c = row['collection']
e = row['energy']
if c in energies:
energies[c].append(e)
else:
energies[c] = [e]
# Convert the lists in numpy arrays
for key in energies:
energies[key] = numpy.array(energies[key])
---------------------------------
for c in np.unique(table.col('collection')) :
print c,' : ', table.readWhere('collection == c', field='energy')
and the timing results were rather dramatic :
time 1 = 0.79
time 2 = 0.08
time 3 = 10.35
This was a test on a relatively small table. I'll have to try it out on
something really big next and see how the memory usage works out.
Thanks guys!
- Alan
--
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| Alan K. Jackson | To see a World in a Grain of Sand |
| [EMAIL PROTECTED] | And a Heaven in a Wild Flower, |
| www.ajackson.org | Hold Infinity in the palm of your hand |
| Houston, Texas | And Eternity in an hour. - Blake |
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