CODEWITHSUNDEEP
pythonpython-2.7numpy
Caleb Hattingh
Caleb Hattingh

Reputation: 9225

Performance of returning a new array and modifying a passed-in array

[Related]

In the snapshot below, I compare the speed of

It seems that the latter is faster. Why should this be the case?

EDIT: Updated with suggestions, and a version that uses numpy's vectorized add(), which is now the fastest.

enter image description here

Upvotes: 1

Views: 338

Answers (2)

HYRY
HYRY

Reputation: 97271

  • x + 1 will create a new array.
  • y[:] = x + 1: create a new array and copy all the data to y
  • y = x + 1: create a new array and bind name y to this new array.
  • np.add(x, 1, out=y): don't create a new array, it's the fastest.

Here is the code:

x = np.zeros(1000000)
y = np.zeros_like(x)
%timeit x + 1
%timeit y[:] = x + 1
%timeit np.add(x, 1, out=y)

the output:

100 loops, best of 3: 4.2 ms per loop
100 loops, best of 3: 6.83 ms per loop
100 loops, best of 3: 2.5 ms per loop

Upvotes: 0

Praveen
Praveen

Reputation: 7222

I don't know much about python/numpy internals, but here's what I assume is happening. By just looking at the code, I get the impression that finline is doing more work than freturn, since finline has all the statements that freturn does (x + 1.0) and more.

Maybe this explains what's going on:

>>> x = np.random.rand(N)
>>> y = np.zeros(N)
>>> super(np.ndarray, y).__repr__()
Out[33]: '<numpy.ndarray object at 0x24c9c80>'
>>> finline(x, y)
>>> y     # see that y was modified
Out[35]: 
array([ 1.92772158,  1.47729293,  1.96549695, ...,  1.37821499,
        1.8672971 ,  1.17013856])
>>> super(np.ndarray, y).__repr__()
Out[36]: '<numpy.ndarray object at 0x24c9c80>'  # address of y did not change
>>> y = freturn(x)
>>> super(np.ndarray, y).__repr__()
Out[38]: '<numpy.ndarray object at 0x24c9fc0>'  # address of y changed

So essentially, I think that finline is doing more work because it has to iterate over the elements of y and initialize each of them to the array returned by the x + 1.0 operation. On the other hand, y = freturn(x) probably just reinitializes the value of the y pointer to be equal to the address of the array initialized by the x + 1.0 operation.

Upvotes: 1

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