Addition of multiple arrays in python

Question

I have a number of arrays that I wish to broadcast into a single array using addition, which I know can be simply done such that:

a = numpy.array([1,2,3])
b = numpy.array9[4,5,6])
sum = a + b
print(sum)

[5,7,9]

However, I can't hardcode it like in this simple example as I will run my script numerous times with a different number of inputs and so will have a different number of arrays each time. Sometimes, I may have a and b, but other times I may have a, c and d but not b etc.

Using a loop I therefore append the arrays I do have into a list, so that I end up with something like:

newlist = [array([1,2,3,...5,4,3]), 
          array([5,7,2,...4,6,7]),
          array([3,6,2,...4,5,9])]

What would be the most pythonic way of getting a single array from the arrays in 'newlist' which is the addition of the arrays within it, such that (from newlist):

sum = [8,15,7,...14,15,19]

The arrays are all the same shape.

Ashwini Chaudhary · Accepted Answer · 2014-10-09 13:48:26Z

14

Stick with Numpy array and use its sum() method:

>>> arr = np.array([[1,2,3,5,4,3], 
          [5,7,2,4,6,7],
          [3,6,2,4,5,9]])
>>> arr.sum(axis=0)
array([ 9, 15,  7, 13, 15, 19])

Of course you can do it with Python lists as well but it is going to be slow:

>>> lst = [[1,2,3,5,4,3], 
          [5,7,2,4,6,7],
          [3,6,2,4,5,9]]
>>> map(sum, zip(*lst))
[9, 15, 7, 13, 15, 19]

edited Oct 9, 2014 at 13:48

answered Oct 9, 2014 at 13:40

Ashwini Chaudhary

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Comments

Jaime · Accepted Answer · 2014-10-09 16:23:27Z

10

There is no need to create a 2D array from your pre-existing 1D arrays. It will certainly not be faster than adding them together, e.g. using reduce with np.add:

In [14]: a = [np.random.rand(10) for _ in range(10)]

In [15]: %timeit np.array(a).sum(axis=0)
100000 loops, best of 3: 10.7 us per loop

In [16]: %timeit reduce(np.add, a)
100000 loops, best of 3: 5.24 us per loop

For larger arrays, it is even less advantageous:

In [17]: a = [np.random.rand(1000) for _ in range(1000)]

In [18]: %timeit np.array(a).sum(axis=0)
100 loops, best of 3: 6.26 ms per loop

In [19]: %timeit reduce(np.add, a)
100 loops, best of 3: 2.43 ms per loop

And of course:

In [20]: np.allclose(np.array(a).sum(axis=0), reduce(np.add, a))
Out[20]: True

answered Oct 9, 2014 at 16:23

Jaime

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2 Comments

chthonicdaemon Over a year ago

Why not just use sum?

Jaime Over a year ago

Good point... The only advantage of using reduce and np.add is that it will properly handle if one of the arrays in the list is not an ndarray, but a list. But if they are all guaranteed to be ndarrays, then yes, absolutely, sum is the best option.

STerliakov · Accepted Answer · 2023-11-09 02:47:01Z

-1

Here is another way:

import numpy as np

arr1 = np.array([1,2,3,5,4,3])
arr2 = np.array([5,7,2,4,6,7])
arr3 = np.array([3,6,2,4,5,9])

out_arr = np.add(arr1, arr2)   
out_arr1 = np.add(out_arr, arr3) 
  
print("added array : ", out_arr1)  

# added array :  [ 9 15  7 13 15 19]

edited Nov 9, 2023 at 2:47

STerliakov

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answered Nov 5, 2023 at 21:45

Tayeb Mohammadi

11 bronze badge

1 Comment

Edward Ji Over a year ago

Your code would only work if there are exactly three arrays. The OP specifically said "not to hardcode" and that there "will be a different number of arrays each time".

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