Question

Low and behold, I ran into a regression in numpy.choose after upgrading to 1.5.1. Past versions (and numeric) supported an, as far as I could tell, unlimited number of potential choices. The “new” choose is limited to 32. Here is a post where another user laments the regression.

I have a list with 100 choices (0-99) that I was using to modify an array. As a work around, I am using the following code. Understandably, it is 7 times slower than using choose. I am not a C programmer, and while I would to get in an fix the numpy issue, I wonder what other potentially faster work arounds exist. Thoughts?

d={...} #A dictionary with my keys and their new mappings
for key, value in d.iteritems():
    array[array==key]=value 

Answer 1

I gather that d has the keys 0 to 99. In that case, the solution is really simple. First, write the values of d in a NumPy array values, in a way that d[i] == values[i] – this seems to be the natural data structure for these values anyway. Then you can access the new array with the values replaced by

values[array]

If you want to modify array in place, simply do

array[:] = values[array]