How do I create a numpy array using a function?

Question

I am using np.fromfunction to create an array of a specific sized based on a function. It looks like this:

import numpy as np
test = [[1,0],[0,2]]
f = lambda i, j: sum(test[i])
matrix = np.fromfunction(f, (len(test), len(test)), dtype=int)

However, I receive the following error:

TypeError: only integer arrays with one element can be converted to an index

Just for reference: Looking at numpy source fromfunction just creates indices array and passes it to user function. So i and j are arrays, not ints. — mg007
– mg007, Commented May 28, 2013 at 20:15
You can see how it performs that task in this similar question: stackoverflow.com/questions/400739/what-does-mean-in-python — sdasdadas
– sdasdadas, Commented May 28, 2013 at 20:23

Florian Rhiem · Accepted Answer · 2013-05-28 20:03:03Z

16

The function needs to handle numpy arrays. An easy way to get this working is:

import numpy as np
test = [[1,0],[0,2]]
f = lambda i, j: sum(test[i])
matrix = np.fromfunction(np.vectorize(f), (len(test), len(test)), dtype=int)

np.vectorize returns a vectorized version of f, which will handle the arrays correctly.

answered May 28, 2013 at 20:03

Florian Rhiem

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2 Comments

blue Over a year ago

passing the function through np.vectorize is the trick documentation fails to point out, thank you so much

Julian - BrainAnnex.org Over a year ago

Excellent trick! It ought to be in the docs. I had similar problem with trivial scenario f = lambda i, j: 3.14 Running np.fromfunction(f, (2, 6)) would simply give me a scalar 3.14 ! By contrast, np.fromfunction(np.vectorize(f), (2, 6)) returns the expected 2x6 matrix whose entries are all 3.14

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