python - Optimal extraction of columns from numpy matrix -

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say have numpy matrix so:

[[ x1, x2, x3, ... ],  [ y1, y2, y3, ... ],  [ z1, z2, z3, ... ],  [ 1,  1,  1,  ... ]]

from want extract list of lists so:

[[x1, y1, z1], [x2, y2, z2], [x3, y3, z3], ... ]

what optimal way of doing this?

at moment have:

tpoints = [pt[:3].tolist() pt in numpy.asarray(tptmat.t)]

and call tolist() taking disproportionate amount of time, approximately third of time spent in time consuming function of program.

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)  14422540   69.777    0.000   69.777    0.000 {method 'tolist' of 'numpy.ndarray' objects}        20   64.258    3.213  178.057    8.903 trans.py:152(_apply)       ...

why not remove last row before transpose?

m[:3].t.tolist() #      ^^^^^^^^^ optional

micro-benchmark shows method faster yours 61%, , if don't convert list of list 45 times faster, 100×4 matrix.

$ python2.5 -m timeit -s 'import numpy; m = numpy.matrix([[5]*100,[6]*100,[7]*100,[1]*100])' 'm[:3].t' 100000 loops, best of 3: 6.26 usec per loop $ python2.5 -m timeit -s 'import numpy; m = numpy.matrix([[5]*100,[6]*100,[7]*100,[1]*100])' 'm[:3].t.tolist()' 10000 loops, best of 3: 180 usec per loop $ python2.5 -m timeit -s 'import numpy; m = numpy.matrix([[5]*100,[6]*100,[7]*100,[1]*100])' 'numpy.asarray(m[:3].t)' 100000 loops, best of 3: 10.9 usec per loop $ python2.5 -m timeit -s 'import numpy; m = numpy.matrix([[5]*100,[6]*100,[7]*100,[1]*100])' '[p[:3].tolist()for p in numpy.asarray(m.t)]' 1000 loops, best of 3: 289 usec per loop

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