Thanks Julian. Mistakenly, I have (a[:1:] + a[:1,:])/2 type of construct somewhere in my code. It works fine, however I wasn't sure if this is something leading to a wrong calculation. Now your explanation makes it clearer.
On Fri, Feb 28, 2014 at 6:48 PM, Julian Taylor < [email protected]> wrote: > On 01.03.2014 00:32, Gökhan Sever wrote: > > > > Hello, > > > > Given this simple 2D array: > > > > In [1]: np.arange(9).reshape((3,3)) > > Out[1]: > > array([[0, 1, 2], > > [3, 4, 5], > > [6, 7, 8]]) > > > > In [2]: a = np.arange(9).reshape((3,3)) > > > > In [3]: a[:1:] > > Out[3]: array([[0, 1, 2]]) > > > > In [4]: a[:1,:] > > Out[4]: array([[0, 1, 2]]) > > > > Could you tell me why the last two indexing (note the comma!) results in > > the same array? Thanks. > > > > > if you specify less indices than dimensions the latter dimensions are > implicitly all selected. > so these are identical for three dimensional arrays: > d = np.ones((3,3,3)) > d[1] > d[1,:] > d[1,:,:] > d[1,...] (... or Ellipsis selects all remaining dimensions) > > this only applies to latter dimensions in the shape, if you want to > select all earlier dimensions they have to be explicitly selected: > d[:,1] == d[:,1,:] > d[..., 1] = d[:,:,1] > > > as for :1: vs 1:, its standard python rules: start:stop:step, with all > three having defaults of 0:len(sequence):1 > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Gökhan
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