BBands wrote: >If I have a NumPy array like so: > >[[1, 12], > [2, 13], > [3, 14], > [4, 15], > [5, 16], > [6, 15], > [7, 14]] > >How can I do an inplace diff, ending up with this? > >[[1, 0], > [2, 1], > [3, 1], > [4, 1], > [5, 1], > [6, -1], > [7, -1]] > > Probably can be done (but it's a bit tricky because you have to be careful not to write over the result before you need it to calculate the difference).
Let 'a' be the array you've given Try: b = a[:,1] b[:0:-1] -= b[5::-1] b[0] = 0 >Also, can I covert to natural logs in place? > >[[1, 2.4849], > [2, 2.5649], > [3, 2.6391], > [4, 2.7081], > [5, 2.7726], > [6, 2.7081], > [7, 2.5649]] > > Definitely *can't* be done because you are changing from integers to double-precision. If the original array is double precision than, numpy.log(a[:,1],a[:,1]) should do it (Makes use of the output argument that all ufuncs accept). -Travis _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
