Hi Mark On Fri, Jan 26, 2007 at 10:17:58AM -0700, Mark P. Miller wrote: > I've recently been working with numpy's random number generators and > noticed that python's core random number generator is faster than > numpy's for the uniform distribution. > > In other words, > > for a in range(1000000): > b = random.random() #core python code > > is substantially faster than > > for a in range(1000000): > b = numpy.random.rand() #numpy code
With numpy, you can get around the for-loop by doing N.random.random(1000000) which is much faster: In [7]: timeit for i in range(10000): random.random() 100 loops, best of 3: 3.92 ms per loop In [8]: timeit N.random.random(10000) 1000 loops, best of 3: 514 µs per loop Cheers Stéfan _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
