If I recall, the main objection raised before was that there are lots of ways to transpose n-dimensional data.
Fine, but the fact is that 2D arrays are pretty darn common, and so are a special case worth optimizing for.
Furthermore transpose() won't go away if you do need to do some specific kind of axes swapping other than the default, so noone is really going to be harmed by adding it.
I propose to make .T a synonym for .swapaxes(-2,-1) {*}, i.e. the last two axes are interchanged. This should also make it useful in many N-d array cases (whereas the default of .transpose() -- to completely reverse the order of all the axes -- is seldom what you want). Part of the thinking is that when you print an N-d array it's the last two dimensions that get printed like 2-d matrices separated by blank likes. You can think of it as some number of stacks of 2-d matrices. So this .T would just transpose those 2-d matrices in the printout. Those are the parts that are generally most contiguous in memory also, so it makes sense for 2-d matrix bits to be stored in those last two dimensions.
Then, if there is a .T, it makes sense to also have .H which would basically be equivalent to .T.conjugate().
Finally, the matrix class has .A to get the underlying array -- it would also be nice to have a .M on array as a shortcut for asmatrix(). This one would be very handy for matrix users, I think, but I could go either way on that, having abandoned matrix myself. Ex: ones([4,4]).M
Other possibilities:
- Make .T a function, so that you can pass it the same info as .transpose(). Then the shortcut becomes a.T(), which isn't as nice, and isn't consistent with matrix's .T any more.
- Just make .T raise an error for ndim>2. But I don't really see any benefit in making it an error as opposed to defining a reasonable default behavior.
- Make .T on a 1-dim array return a 2-dim Nx1 array. (My default suggestion is to just leave it alone if ndim < 2, an exception would be another possiblility). Would make an easy way to create column vectors from arrays, but I can think of nothing else in Numpy that acts that way.
This is not a 1.0 must have, as it introduces no backward compatibility issues. But it would be trivial to add if the will is there.
{*} except that negative axes for swapaxes doesn't seem work currently, so instead it would need to be something like:
a.transpose( a.shape[:-2] + (a.shape[-1],a.shape[-2]) )
with a check for "if ndim > 1", of course.
--Bill
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