Numpy array: assignment of value fails

Question

I have this code snippet:

import numpy as np

a=np.array([5,6,7,8,9])
b=np.array([5,6,7,8,9])

scoreA = np.array([float(1) / (i + 1) for i in range(len(a))])
scoreB = np.array([0 for i in range(len(b))])

for eleA in a:
    if eleA in b:
        i, = np.where(b == eleA)
        i = i[0]
        j, = np.where(a == eleA)
        j = j[0]
        scoreB[i] = scoreA[j]

        print "B is: %f" % scoreB[i]
        print "A is: %f" % scoreA[j]

So the basic idea is: for arrays a and b, if an element is found in both of them, then I'll assign the score of that element in scoreA to scoreB. But the result is like this:

B is: 1.000000
A is: 1.000000
B is: 0.000000
A is: 0.500000
B is: 0.000000
A is: 0.333333
B is: 0.000000
A is: 0.250000
B is: 0.000000
A is: 0.200000

which means the line:

scoreB[i] = scoreA[j]

is not working properly? How can I resolve this?

IIUC, just do : mask = a==b; scoreB[mask] = scoreA[mask]. — Divakar
– Divakar, Commented Aug 24, 2017 at 10:24

Oleg · Accepted Answer · 2017-08-24 10:42:23Z

2

Your solution is rather strange and it is better to make it as @Divakar mentioned. But anyway the straitforward problem is that scoreA and scoreB have different types: float64 and int64.

make

scoreA = np.array([float(1) / (i + 1) for i in range(len(a))],dtype=float)
scoreB = np.array([0 for i in range(len(b))],dtype=float)

or some other dtype to make sure that all your scores have the same type.

edited Aug 24, 2017 at 10:42

answered Aug 24, 2017 at 10:36

Oleg

3,2204 gold badges43 silver badges53 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Numpy array: assignment of value fails

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related