On Tue, Jul 20, 2010 at 7:24 AM, Skipper Seabold <jsseab...@gmail.com> wrote: > On Tue, Jul 20, 2010 at 5:11 AM, Gael Varoquaux > <gael.varoqu...@normalesup.org> wrote: >> Is there in numpy a function that does: >> >> np.concatenate([a_[np.newaxis] for a_ in a]) >> >> ? >> >> ie: add a dimension in front and stack along this dimension, just like >> >> np.array(a) >> >> would do, but more efficient. >> >> This is something that do all the time. Am I the only one? >> > > Will one of the stack functions do? I take it your a looks something like > > a = [np.arange(1000), np.arange(1000), np.arange(1000)] > > np.all(np.vstack(a) == np.concatenate([a_[None] for a_ in a])) > # True > > It's about the same speed-wise as concatenate, but it's more terse and > faster than np.array if you already have a list of arrays.
Stacking seems to add some overhead. A reshape is needed if the arrays in the list are more than 1d. >> a = [np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])] >> timeit np.array(a) 100000 loops, best of 3: 6.76 us per loop >> timeit np.concatenate([a_[np.newaxis] for a_ in a]) 100000 loops, best of 3: 4.17 us per loop >> timeit np.vstack(a).reshape(2,2,2) 100000 loops, best of 3: 5.91 us per loop _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
