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

• mg007: 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.
• sdasdadas: @mg007 I believe the function will unbox those, however.
• sdasdadas: You can see how it performs that task in this similar question: stackoverflow.com/questions/400739/what-does-mean-in-python

Answer 1

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.

• blue: passing the function through np.vectorize is the trick documentation fails to point out, thank you so much
• Julian - BrainAnnex.org: 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