Question

I have a large (in thousands) collection of word : value (float) pairs. I need to find the best of the value and extract the corresponding associated word. For example, I have (a,2.4),(b,5.2),(c,1.2),(d,9.2),(e,6.3),(f,0.4). I would like (d,9.2) as the output.

Currently, I am using a dictionary to store these tuples and use the max operator to retrieve the maximum key value in the dictionary. I was wondering if a numpy array would be more efficient. Soliciting expert opinions here.

Answer 1

Using Numpy here would involve keeping the float values in a separate ndarray. Find the index of max value using argmax and get the word from a separate list. This is very fast, but constructing the ndarray only to find the max is not. Example:

import numpy as np
import operator

names = [str(x) for x in xrange(10000)]
values = [float(x) for x in xrange(10000)]
tuples = zip(names, values)
dic = dict(tuples)
npvalues = np.fromiter(values, np.float)

def fa():
    return names[npvalues.argmax()]

def fb():
    return max(tuples, key=operator.itemgetter(1))[0]

def fc():
    return max(dic, key=dic.get)

def fd():
    v = np.fromiter((x[1] for x in tuples), np.float)
    return tuples[v.argmax()][0]

Timings: fa 67 µs, fb 2300 µs, fc 2580 µs, fd 3780 µs.

So, using Numpy (fa) is over 30 times faster than using a plain list (fb) or dictionary (fc), when the time to construct the Numpy array is not taken into account. (fd takes it into account)