Python: Way to speed up a repeatedly executed eval statement?

Question

In my code, I\'m using eval to evaluate a string expression given by the user. Is there a way to compile or otherwise speed up this statement?

Answer 1

I think you are optimising the wrong end. If you want to perform the same operation for a lot of numbers you should consider using numpy:

import numpy
import time
import math
import random

result_count = 100000
expression = "sin(x) * y"

namespace = dict(
    x=numpy.array(
        [random.random() for _ in xrange(result_count)]),
    y=numpy.array(
        [random.random() for _ in xrange(result_count)]),
    sin=numpy.sin,
)
print ('Evaluating %d instances '
       'of the given expression:') % result_count
print expression

start = time.time()
result = eval(expression, namespace)
numpy_time = time.time() - start
print "With numpy:", numpy_time


assert len(result) == result_count
assert all(math.sin(a) * b == c for a, b, c in
           zip(namespace["x"], namespace["y"], result))

To give you an idea about the possible gain I've added a variant using generic python and the lambda trick:

from math import sin
from itertools import izip

start = time.time()
f = eval("lambda: " + expression)
result = [f() for x, y in izip(namespace["x"], namespace["y"])]
generic_time = time.time() - start
print "Generic python:", generic_time
print "Ratio:", (generic_time / numpy_time)

Here are the results on my aging machine:

$ python speedup_eval.py 
Evaluating 100000 instances of the given expression:
sin(x) * y
With numpy: 0.006098985672
Generic python: 0.270224094391
Ratio: 44.3063992807

The speed-up is not as high as I expected, but still significant.