How to progressively sum a NumPy array

Question

Forgive me if this has been asked before, I couldn't find it. I am trying to progressively sum a numpy array into a new numpy array using vector operations. What I mean by this is that the 2nd index of the new array is equal to the 1st + 2nd index of the old array. or A[n] = B[0] + B[1] ... + B[n]. I know how to do this using a for loop but I'm looking for a vectorized solution.

Here is my non-vectorized solution:

import numpy as np  
A = np.arange(10)
B = np.empty(10)

for i in range(len(A)):
    B[i] = sum(A[0:i+1])

print(B)

it's the "cumulative sum", see docs.scipy.org/doc/numpy/reference/generated/numpy.cumsum.html — B. Go
– B. Go, Commented Feb 21, 2020 at 22:06

Wesley Yang · Accepted Answer · 2020-02-21 22:12:03Z

5

You can do it like this:

import numpy as np  

A = np.arange(10)
B = np.cumsum(A)
# [ 0  1  3  6 10 15 21 28 36 45]

Thanks

answered Feb 21, 2020 at 22:12

Wesley Yang

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finefoot · Accepted Answer · 2020-02-21 22:12:35Z

1

The "progressive" sum is called cumulative sum. Use NumPy's cumsum for this.

Using your example and comparing B to np.cumsum(A) results in equal arrays:

>>> import numpy as np
>>> A = np.arange(10)
>>> B = np.empty(10)
>>> for i in range(len(A)):
...     B[i] = sum(A[0:i+1])
... 
>>> np.array_equal(B, np.cumsum(A))
True

answered Feb 21, 2020 at 22:12

finefoot

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