Mapping a NumPy array in place

Question

Is it possible to map a NumPy array in place? If yes, how?

Given a_values - 2D array - this is the bit of code that does the trick for

Answer 1

This is a write-up of contributions scattered in answers and comments, that I wrote after accepting the answer to the question. Upvotes are always welcome, but if you upvote this answer, please don't miss to upvote also those of senderle and (if (s)he writes one) eryksun, who suggested the methods below.

Q: Is it possible to map a numpy array in place?
A: Yes but not with a single array method. You have to write your own code.

Below a script that compares the various implementations discussed in the thread:

import timeit
from numpy import array, arange, vectorize, rint

# SETUP
get_array = lambda side : arange(side**2).reshape(side, side) * 30
dim = lambda x : int(round(x * 0.67328))

# TIMER
def best(fname, reps, side):
    global a
    a = get_array(side)
        t = timeit.Timer('%s(a)' % fname,
                     setup='from __main__ import %s, a' % fname)
    return min(t.repeat(reps, 3))  #low num as in place --> converge to 1

# FUNCTIONS
def mac(array_):
    for row in range(len(array_)):
        for col in range(len(array_[0])):
            array_[row][col] = dim(array_[row][col])

def mac_two(array_):
    li = range(len(array_[0]))
    for row in range(len(array_)):
        for col in li:
            array_[row][col] = int(round(array_[row][col] * 0.67328))

def mac_three(array_):
    for i, row in enumerate(array_):
        array_[i][:] = [int(round(v * 0.67328)) for v in row]


def senderle(array_):
    array_ = array_.reshape(-1)
    for i, v in enumerate(array_):
        array_[i] = dim(v)

def eryksun(array_):
    array_[:] = vectorize(dim)(array_)

def ufunc_ed(array_):
    multiplied = array_ * 0.67328
    array_[:] = rint(multiplied)

# MAIN
r = []
for fname in ('mac', 'mac_two', 'mac_three', 'senderle', 'eryksun', 'ufunc_ed'):
    print('\nTesting `%s`...' % fname)
    r.append(best(fname, reps=50, side=50))
    # The following is for visually checking the functions returns same results
    tmp = get_array(3)
    eval('%s(tmp)' % fname)
    print tmp
tmp = min(r)/100
print('\n===== ...AND THE WINNER IS... =========================')
print('  mac (as in question)       :  %.4fms [%.0f%%]') % (r[0]*1000,r[0]/tmp)
print('  mac (optimised)            :  %.4fms [%.0f%%]') % (r[1]*1000,r[1]/tmp)
print('  mac (slice-assignment)     :  %.4fms [%.0f%%]') % (r[2]*1000,r[2]/tmp)
print('  senderle                   :  %.4fms [%.0f%%]') % (r[3]*1000,r[3]/tmp)
print('  eryksun                    :  %.4fms [%.0f%%]') % (r[4]*1000,r[4]/tmp)
print('  slice-assignment w/ ufunc  :  %.4fms [%.0f%%]') % (r[5]*1000,r[5]/tmp)
print('=======================================================\n')

The output of the above script - at least in my system - is:

  mac (as in question)       :  88.7411ms [74591%]
  mac (optimised)            :  86.4639ms [72677%]
  mac (slice-assignment)     :  79.8671ms [67132%]
  senderle                   :  85.4590ms [71832%]
  eryksun                    :  13.8662ms [11655%]
  slice-assignment w/ ufunc  :  0.1190ms [100%]

As you can observe, using numpy's ufunc increases speed of more than 2 and almost 3 orders of magnitude compared with the second best and worst alternatives respectively.

If using ufunc is not an option, here's a comparison of the other alternatives only:

  mac (as in question)       :  91.5761ms [672%]
  mac (optimised)            :  88.9449ms [653%]
  mac (slice-assignment)     :  80.1032ms [588%]
  senderle                   :  86.3919ms [634%]
  eryksun                    :  13.6259ms [100%]

HTH!