On Fri, Mar 23, 2007 at 11:09:03AM -0400, Robert Pyle wrote:
> > In [65]:concatenate((a.reshape(10,1),b.reshape(10,1)),axis=1)
> > Out[65]:
> > array([[ 0, -10],
> > [ 1, -9],
> > [ 2, -8],
> > [ 3, -7],
> > [ 4, -6],
> > [ 5, -5],
> > [ 6, -4],
> > [ 7, -3],
> > [ 8, -2],
> > [ 9, -1]])
> >
> >
> > ?
> >
> > I thought there would be an easier way. Did I overlook something?
>
> What's wrong with zip? Or did *I* miss the point? (I'm just getting
> the hang of numpy.)
If you use 'zip' you don't make use of numpy's fast array mechanisms.
I attach some code you can run as a benchmark. From my ipython
session:
In [1]: run vsbench.py
In [2]: timeit using_vstack(x,y)
1000 loops, best of 3: 997 µs per loop
In [3]: timeit using_zip(x,y)
10 loops, best of 3: 503 ms per loop
In [4]: timeit using_custom_iteration(x,y)
1000 loops, best of 3: 1.64 ms per loop
Cheers
Stéfan
import numpy as N
x = N.random.random(100000)
y = N.random.random(100000)
def using_vstack(*args):
return N.vstack(args).T
def using_zip(*args):
return N.array(zip(*args))
def using_custom_iteration(*args):
x = N.empty((len(args[0]),len(args)))
for i,vals in enumerate(args):
x[:,i] = vals
return x
check = N.vstack((x,y)).T
assert N.all(using_vstack(x,y) == check)
assert N.all(using_zip(x,y) == check)
assert N.all(using_custom_iteration(x,y) == check)
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
