What is the difference between np.sum and np.add.reduce?

Question

What is the difference between np.sum and np.add.reduce?
While the docs are quite explicit:

For example, add.reduce() is equivalent to sum().

The performance of the two seems to be quite different: for relatively small array sizes add.reduce is about twice faster.

$ python -mtimeit -s"import numpy as np; a = np.random.rand(100); summ=np.sum" "summ(a)"
100000 loops, best of 3: 2.11 usec per loop
$ python -mtimeit -s"import numpy as np; a = np.random.rand(100); summ=np.add.reduce" "summ(a)"
1000000 loops, best of 3: 0.81 usec per loop

$ python -mtimeit -s"import numpy as np; a = np.random.rand(1000); summ=np.sum" "summ(a)"
100000 loops, best of 3: 2.78 usec per loop
$ python -mtimeit -s"import numpy as np; a = np.random.rand(1000); summ=np.add.reduce" "summ(a)"
1000000 loops, best of 3: 1.5 usec per loop

For larger array sizes, the difference seems to go away:

$ python -mtimeit -s"import numpy as np; a = np.random.rand(10000); summ=np.sum" "summ(a)"
100000 loops, best of 3: 10.7 usec per loop
$ python -mtimeit -s"import numpy as np; a = np.random.rand(10000); summ=np.add.reduce" "summ(a)"
100000 loops, best of 3: 9.2 usec per loop

Asked By: ev-br

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Answer 1

Short answer: when the argument is a numpy array, np.sum ultimately calls add.reduce to do the work. The overhead of handling its argument and dispatching to add.reduce is why np.sum is slower.

Longer answer:
np.sum is defined in numpy/core/fromnumeric.py. In the definition of np.sum, you’ll
see that the work is passed on to _methods._sum. That function, in _methods.py, is simply:

def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
    return um.add.reduce(a, axis=axis, dtype=dtype,
                            out=out, keepdims=keepdims)

um is the module where the add ufunc is defined.

Answered By: Warren Weckesser

Answer 2

There is actually one difference that might bite you if you were to blindly refactor from one to the other:

>>> import numpy as np
>>> a = np.arange(4).reshape(2, 2)
>>> 
>>> np.sum(a)
6
>>> np.add.reduce(a)
array([2, 4])
>>>

The axis default values are different!

Answered By: Paul Panzer

Answer 3

To answer the question in the title, simply: When working with matrices,
you will find an important distinction between the two functions:

np.sum (without specifying the axis) will return the sum of all elements in the matrix.

np.add.reduce (without specifying the axis) will return the sum along axis=0.
That is, add.reduce(a) is equivalent to sum(a, axis=0)

However, both will return the same if you specify the axis.
I’m posting as answer because I don’t have enough rep to comment.

Answered By: Shrinidhi H R

Answer 4

np.add.reduce() is useful if you have an array of numpy arrays. Consider the following example:

import numpy as np
arr = [np.array([2, 2]), np.array([0, 3]), np.array([4, 8])]
res = np.add.reduce(arr)

This will produce res = array([ 6, 13]).

It is the same as

import numpy as np
import functools
arr = [np.array([2, 2]), np.array([0, 3]), np.array([4, 8])]
res = functools.reduce(np.add, arr)

Answered By: Smiley1000

What is the difference between np.sum and np.add.reduce?

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