Selecting elements in numpy array using regular expressions

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?

You many have seen this docs.scipy.org/doc/numpy/reference/generated/… , which would be useful if your starting point was a string or a file instead of an array. — Paul
– Paul, Commented Jul 6, 2011 at 13:31
Paul, thank you. Yes I saw this (this is the first google hit for "numpy regexp"), but this doesn't solve my problem — Boris Gorelik
– Boris Gorelik, Commented Jul 6, 2011 at 19:57

Paul · Accepted Answer · 2011-07-06 13:25:10Z

22

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)

answered Jul 6, 2011 at 13:25

Paul

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2 Comments

Ufos Over a year ago

from numpy docs:

The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.

blackbrandt Over a year ago

Link to documentation concerning the performance of vectorize.

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