While executing the below code I'm simply getting the first row(split) of the matrix back not the sum of the elements as what I'm expecting. Is my understanding incorrect or did I do something stupid?
My objective is to get the sum of all elements in each row.
import numpy as np
from functools import reduce
matrix = 100*np.random.rand(4,4)
matrix=matrix.astype(int)
print(matrix)
s_matrix = np.vsplit(matrix, 4)
sum_test = reduce((lambda a,b : a+b), list(s_matrix[0]))
print(sum_test)
Output:
[[79 75 33 26]
[49 45 16 19]
[58 33 83 55]
[40 14 2 93]]
[79 75 33 26]
Expected:
[213, 129, 229, 149]
You can use reduce() for this by continually adding the results to a list in the accumulator:
import numpy as np
from functools import reduce
matrix = 100*np.random.rand(4,4)
matrix=matrix.astype(int)
sum_test = reduce(lambda a,b : a+[sum(b)], list(matrix), [])
print(sum_test)
...but you really shouldn't. One of the major points of Numpy is that you can avoid explicit python loops. Instead you should just use the array's sum function. You can pass it an axis to tell it to sum rows instead of the whole thing:
import numpy as np
matrix = np.random.randint(0, 100, [4,4])
print(matrix)
print(matrix.sum(axis = 1))
Result
[[64 89 97 15]
[12 47 81 31]
[52 81 37 78]
[27 64 79 50]]
[265 171 248 220]