I think it would be a great idea to have pylab.load in numpy. It also seems to be a lot faster than scipy.io.
One thing that is very nice about pylab.load is that it can read-in dates. However, it can't, as far a I know, handle other non-float data. I played around with python's csv module and pylab.load for a while resulting in a database class I posted in the cookbook section: http://www.scipy.org/Cookbook/dbase This class can read any type of data in a csv file, including dates, into a dictionary but is based on both pylab.load and the csv module. I use cPickle for storing the data once it is read-in once. I haven't tried PyTables but hear a lot of good things about it. Vincent On 4/19/07 10:58 AM, "Christopher Barker" <[EMAIL PROTECTED]> wrote: > Lisandro Dalcin wrote: >> I am also +1 on this, but this functionality should be implemented in >> C, I think. > > well, maybe. > >> I've just tested numpy.fromfile('name.txt', sep=' ') >> against pylab.load('name.txt') for a 35MB text file, the number are: >> >> numpy.fromfile: 2.66 sec. >> pylab.load: 16.64 sec. > > exactly that's expected. fromfile is designed to do the easy cases as > fast as possible, pylab.load is designed to be be flexible, I'm not user > you need both the speed and flexibility at the same time. > > By the way, I haven't looked at pylab.load() for a while, but it could > perhaps be sped up by using fromfile() and or fromstring internally. > There may be some opportunity to special case the easy ones too (i.e. > all columns desired, etc.) > > -Chris > > > > -- _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion