On Wed, Apr 6, 2016 at 11:44 AM, Chris Barker - NOAA Federal < chris.bar...@noaa.gov> wrote:
> But the truth is that Numpy arrays are arrays, not matrices and vectors. > > The "right" way to do this is to properly extend and support the > matrix object, adding row and column vector objects, and then it would > be clear. But while there has been a lot of discussion about that in > the past, the fact is that no one wants it bad enough to write the > code. > > So I think it's better to keep Numpy arrays "pure", and if you want to > change the rank of an array, you do so explicitly. > I think that cat is already out of the bag. As long as you can do matrix multiplication on arrays using the @ operator, I think they aren't really "pure" anymore. > BTW, if transposing a (N,) array gives you a (N,1) array, what does > transposing a (N,1) array give you? > > (1,N) or (N,) ? > My suggestion is that this explicitly increases the number of dimensions to at least 2. The result will always have at least 2 dimensions. So 0D -> 2D, 1D -> 2D, 2D -> 2D, 3D -> 3D, 4D -> 4D, etc. So this would be equivalent to the existing `atleast_2d` function.
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