sum of all elements in array python numpy

How to sum all the elements of a numpy object array?

stackoverflow.com › questions › 55931915 › how-to-sum-all-the-elements-of-a-numpy-object-array

Use numpy.concatenate with sum:

print (np.concatenate(a).sum())

print (np.sum(np.concatenate(a)))
32

Performance: Depends of number of nested arrays and number of values in arrays, so best test in real data:

a = np.array([np.arange(5), np.arange(2), np.arange(7)] * 1000) 
#print (a)

In [40]: %timeit np.concatenate(a).sum()
830 µs ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [41]: %timeit (np.sum(np.concatenate(a)))
835 µs ± 33.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

#original solution 
In [42]: %timeit sum([np.sum(array) for array in a])
15.3 ms ± 85.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Another solutions:

In [43]: %timeit sum(np.sum(array) for array in a)
17.4 ms ± 2.27 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [44]: %timeit (sum(np.concatenate(a)))
2.28 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Answer from jezrael on Stack Overflow

Stack Overflow

stackoverflow.com › questions › 55931915 › how-to-sum-all-the-elements-of-a-numpy-object-array

python - How to sum all the elements of a numpy object array? - Stack Overflow

Top answer

1 of 3

Use numpy.concatenate with sum:

print (np.concatenate(a).sum())

print (np.sum(np.concatenate(a)))
32

Performance: Depends of number of nested arrays and number of values in arrays, so best test in real data:

a = np.array([np.arange(5), np.arange(2), np.arange(7)] * 1000) 
#print (a)

In [40]: %timeit np.concatenate(a).sum()
830 µs ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [41]: %timeit (np.sum(np.concatenate(a)))
835 µs ± 33.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

#original solution 
In [42]: %timeit sum([np.sum(array) for array in a])
15.3 ms ± 85.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Another solutions:

In [43]: %timeit sum(np.sum(array) for array in a)
17.4 ms ± 2.27 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [44]: %timeit (sum(np.concatenate(a)))
2.28 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

2 of 3

While your code is good, you can also use numpy.concatenate to concatenate your arrays and then calcuate the sum via numpy.sum, python builtin sum, or a sum function over the numpy array

import numpy as np

a = np.array([np.arange(5), np.arange(2), np.arange(7)])

print(np.sum(np.concatenate(a)))
#32

print(sum(np.concatenate(a)))
#32

print(np.concatenate(a).sum())
#32

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Sum of array elements over a given axis. ... Elements to sum. ... Axis or axes along which a sum is performed. The default, axis=None, will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis.

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