Question

One may select elements in numpy arrays as follows

a = np.random.rand(100)
sel = a > 0.5 #select elements that are greater than 0.5
a[sel] = 0 #do something with the selection

b = np.array(list('abc abc abc'))
b[b==a] = 'A' #convert all the a's to A's

This property is used by the np.where function to retrive indices:

indices = np.where(a>0.9)

What I would like to do is to be able to use regular expressions in such element selection. For example, if I want to select elements from b above that match the [Aab] regexp, I need to write the following code:

regexp = '[Ab]'
selection = np.array([bool(re.search(regexp, element)) for element in b])

This looks too verbouse for me. Is there any shorter and more elegant way to do this?

Answer 1

There’s some setup involved here, but unless numpy has some kind of direct support for regular expressions that I don’t know about, then this is the most “numpytonic” solution. It tries to make iteration over the array more efficient than standard python iteration.

import numpy as np
import re

r = re.compile('[Ab]')
vmatch = np.vectorize(lambda x:bool(r.match(x)))

A = np.array(list('abc abc abc'))
sel = vmatch(A)