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!

Answer 2

This is just an updated version of mac's write-up, actualized for Python 3.x, and with numba and numpy.frompyfunc added.

numpy.frompyfunc takes an abitrary python function and returns a function, which when cast on a numpy.array, applies the function elementwise.
However, it changes the datatype of the array to object, so it is not in place, and future calculations on this array will be slower.
To avoid this drawback, in the test numpy.ndarray.astype will be called, returning the datatype to int.

As side note:
Numba isn't included in Python's basic libraries and has to be downloaded externally if you want to test it. In this test, it actually does nothing, and if it would have been called with @jit(nopython=True), it would have given an error message saying that it can't optimize anything there. Since, however, numba can often speed-up code written in a functional style, it is included for integrity.

import timeit
from numpy import array, arange, vectorize, rint, frompyfunc
from numba import autojit

# 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_)

@autojit
def numba(array_):
    for row in range(len(array_)):
        for col in range(len(array_[0])):
            array_[row][col] = dim(array_[row][col])


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

def ufunc_frompyfunc(array_):
    udim = frompyfunc(dim,1,1)
    array_ = udim(array_)
    array_.astype("int")

# MAIN
r = []
totest = ('mac', 'mac_two', 'mac_three', 'senderle', 'eryksun', 'numba','ufunc_ed','ufunc_frompyfunc')
for fname in totest:
    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
results = list(zip(totest,r))
results.sort(key=lambda x: x[1])

print('\n===== ...AND THE WINNER IS... =========================')
for name,time in results:
    Out = '{:<34}: {:8.4f}ms [{:5.0f}%]'.format(name,time*1000,time/tmp)
    print(Out)
print('=======================================================\n')

And finally, the results:

===== ...AND THE WINNER IS... =========================
ufunc_ed                          :   0.3205ms [  100%]
ufunc_frompyfunc                  :   3.8280ms [ 1194%]
eryksun                           :   3.8989ms [ 1217%]
mac_three                         :  21.4538ms [ 6694%]
senderle                          :  22.6421ms [ 7065%]
mac_two                           :  24.6230ms [ 7683%]
mac                               :  26.1463ms [ 8158%]
numba                             :  27.5041ms [ 8582%]
=======================================================

Answer 3

It's only worth trying to do this in-place if you are under significant space constraints. If that's the case, it is possible to speed up your code a little bit by iterating over a flattened view of the array. Since reshape returns a new view when possible, the data itself isn't copied (unless the original has unusual structure).

I don't know of a better way to achieve bona fide in-place application of an arbitrary Python function.

>>> def flat_for(a, f):
...     a = a.reshape(-1)
...     for i, v in enumerate(a):
...         a[i] = f(v)
... 
>>> a = numpy.arange(25).reshape(5, 5)
>>> flat_for(a, lambda x: x + 5)
>>> a

array([[ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29]])

Some timings:

>>> a = numpy.arange(2500).reshape(50, 50)
>>> f = lambda x: x + 5
>>> %timeit flat_for(a, f)
1000 loops, best of 3: 1.86 ms per loop

It's about twice as fast as the nested loop version:

>>> a = numpy.arange(2500).reshape(50, 50)
>>> def nested_for(a, f):
...     for i in range(len(a)):
...         for j in range(len(a[0])):
...             a[i][j] = f(a[i][j])
... 
>>> %timeit nested_for(a, f)
100 loops, best of 3: 3.79 ms per loop

Of course vectorize is still faster, so if you can make a copy, use that:

>>> a = numpy.arange(2500).reshape(50, 50)
>>> g = numpy.vectorize(lambda x: x + 5)
>>> %timeit g(a)
1000 loops, best of 3: 584 us per loop

And if you can rewrite dim using built-in ufuncs, then please, please, don't vectorize:

>>> a = numpy.arange(2500).reshape(50, 50)
>>> %timeit a + 5
100000 loops, best of 3: 4.66 us per loop

numpy does operations like += in place, just as you might expect -- so you can get the speed of a ufunc with in-place application at no cost. Sometimes it's even faster! See here for an example.

---

By the way, my original answer to this question, which can be viewed in its edit history, is ridiculous, and involved vectorizing over indices into a. Not only did it have to do some funky stuff to bypass vectorize's type-detection mechanism, it turned out to be just as slow as the nested loop version. So much for cleverness!

Answer 4

if ufuncs are not possible, you should maybe consider using cython. it is easy to integrate and give big speedups on specific use of numpy arrays.

Answer 5

Why not using numpy implementation, and the out_ trick ?

from numpy import array, arange, vectorize, rint, multiply, round as np_round 

def fmilo(array_):
    np_round(multiply(array_ ,0.67328, array_), out=array_)

got:

===== ...AND THE WINNER IS... =========================
  mac (as in question)       :  80.8470ms [130422%]
  mac (optimised)            :  80.2400ms [129443%]
  mac (slice-assignment)     :  75.5181ms [121825%]
  senderle                   :  78.9380ms [127342%]
  eryksun                    :  11.0800ms [17874%]
  slice-assignment w/ ufunc  :  0.0899ms [145%]
  fmilo                      :  0.0620ms [100%]
=======================================================