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up vote 2 down vote favorite

I have a mx1 array, a, that contains some values. Moreover, I have a nxk array, say b, that contains indices between 0 and m.

Example:

a = np.array((0.1, 0.2, 0.3))
b = np.random.randint(0, 3, (4, 4))

For every index value in b I want to get the corresponding value from a. I can do it with a loop:

c = np.zeros_like(b).astype('float')
n, k = b.shape
for i in range(n):
    for j in range(k):
        c[i, j] = a[b[i, j]]

Is there any built-it numpy function or trick that is more elegant? This approach looks a little dumb to me. PS: originally, a and b are Pandas objects if that helps.

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You have to set the second parameter of `randint´ to 3 in order to get values of [0, 1, 2]. –  MaxPowers Jun 24 at 7:44
    
Thanks, I sometimes struggle with the half-open intervals notation. Coming from Matlab/R/Gauss, every now and then I fall back :-) –  BayerSe Jun 24 at 7:57
    
It doesn't help that the standard library random module includes the right endpoint for random.randint, while NumPy excludes it for numpy.random.randint. –  user2357112 Jun 24 at 7:58
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2 Answers

up vote 7 down vote accepted 
>>> a
array([ 0.1,  0.2,  0.3])
>>> b
array([[0, 0, 1, 1],
       [0, 0, 1, 1],
       [0, 1, 1, 0],
       [0, 1, 0, 1]])
>>> a[b]
array([[ 0.1,  0.1,  0.2,  0.2],
       [ 0.1,  0.1,  0.2,  0.2],
       [ 0.1,  0.2,  0.2,  0.1],
       [ 0.1,  0.2,  0.1,  0.2]])

Tada! It's just a[b]. (Also, you probably wanted the upper bound on the randint call to be 3.)

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up vote 1 down vote

Try iteration with a numpy.flatiter object:

a = np.array((0.1, 0.2, 0.3))
b = np.random.randint(0, 3, (4, 4))

c = np.array([a[i] for i in b.flat]).reshape(b.shape)
print(c)

array([[ 0.2,  0.2,  0.2,  0.1],
       [ 0.3,  0.3,  0.2,  0.1],
       [ 0.2,  0.1,  0.3,  0.3],
       [ 0.3,  0.3,  0.3,  0.1]])
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1  
Works, but using a list comprehension with NumPy kind of defeats the point. –  user2357112 Jun 24 at 7:50
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