Hi Nathaniel, Thanks for the clearing my understand. This is exactly what i needed.
Thanks, Nathaniel Smith wrote: > > On Thu, Jun 28, 2012 at 12:38 AM, astronomer <shailendra.vi...@gmail.com> > wrote: >> >> Hi All, >> I am wondering if there any difference in memory overhead between the >> following code. >> a=numpy.arange(10) >> b=numpy.arange(10) >> c=a+b >> >> and >> a=numpy.arange(10) >> b=numpy.arange(10) >> c=numpy.empty_likes(a) >> c[:]=a+b >> >> Does the later code make a temproray array for the result of (a+b) and >> then >> copy it to c. I beleive it does that, but i wanted to make sure. > > Yes it does. If you want to avoid this extra copy, and have a > pre-existing output array, you can do: > > np.add(a, b, out=c) > > ('+' on numpy array's is just a synonym for np.add; np.add is a ufunc, > and all ufunc's accept this syntax: > http://docs.scipy.org/doc/numpy/reference/ufuncs.html > ) > > -n > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- View this message in context: http://old.nabble.com/memory-allocation-at-assignment-tp34083731p34084248.html Sent from the Numpy-discussion mailing list archive at Nabble.com. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion