Altering numpy function output array in place

2024/4/14 11:05:06

I'm trying to write a function that performs a mathematical operation on an array and returns the result. A simplified example could be:

def original_func(A):return A[1:] + A[:-1]

For speed-up and to avoid allocating a new output array for each function call, I would like to have the output array as an argument, and alter it in place:

def inplace_func(A, out):out[:] = A[1:] + A[:-1]

However, when calling these two functions in the following manner,

A = numpy.random.rand(1000,1000)
out = numpy.empty((999,1000))C = original_func(A)inplace_func(A, out)

the original function seems to be twice as fast as the in-place function. How can this be explained? Shouldn't the in-place function be quicker since it doesn't have to allocate memory?


If you want to perform the operation in-place, do

def inplace_func(A, out):np.add(A[1:], A[:-1], out)

This does not create any temporaries (which A[1:] + A[:-1]) does.

All Numpy binary operations have corresponding functions, check the list here:

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