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  1. PO
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    <p>Please consider trimming down your <code>f_wo_append</code> function:</p> <pre><code>def f_wo_append(): '''Function without append''' P = 75 numcustomers = 0 for person in popn.people: person.customer = iscust = person.utility &gt;= P numcustomers += iscust return numcustomers </code></pre> <p><strong>Edit</strong> in response to OP's comment """This made it a lot worse! The trimmed version takes 4 times more time than the version I have posted above. """</p> <p>There is no way that that could take "4 times more" (5 times?) ... here is my code, which demonstrates a significant reduction in the "without append" case, as I suggested, and also introduces a significant improvement in the "with append" case.</p> <pre><code>import random # instead of numpy import time timer_func = time.clock # better on Windows, use time.time on *x platform class Person(object): def __init__(self, util): self.utility = util self.customer = 0 class Population(object): def __init__(self, numpeople): random.seed(1) self.people = [Person(random.uniform(0, 300)) for i in xrange(numpeople)] self.cus = [] self.noncus = [] def f_w_append(popn): '''Function with append''' P = 75 cus = [] noncus = [] for per in popn.people: if per.utility &gt;= P: per.customer = 1 cus.append(per) else: per.customer = 0 noncus.append(per) popn.cus = cus # omitted from OP's code popn.noncus = noncus # omitted from OP's code return len(cus) def f_w_append2(popn): '''Function with append''' P = 75 popn.cus = [] popn.noncus = [] cusapp = popn.cus.append noncusapp = popn.noncus.append for per in popn.people: if per.utility &gt;= P: per.customer = 1 cusapp(per) else: per.customer = 0 noncusapp(per) return len(popn.cus) def f_wo_append(popn): '''Function without append''' P = 75 for per in popn.people: if per.utility &gt;= P: per.customer = 1 else: per.customer = 0 numcustomers = 0 for per in popn.people: if per.customer == 1: numcustomers += 1 return numcustomers def f_wo_append2(popn): '''Function without append''' P = 75 numcustomers = 0 for person in popn.people: person.customer = iscust = person.utility &gt;= P numcustomers += iscust return numcustomers if __name__ == "__main__": import sys popsize, which, niter = map(int, sys.argv[1:4]) pop = Population(popsize) func = (f_w_append, f_w_append2, f_wo_append, f_wo_append2)[which] t0 = timer_func() for _unused in xrange(niter): nc = func(pop) t1 = timer_func() print "popsize=%d func=%s niter=%d nc=%d seconds=%.2f" % ( popsize, func.__name__, niter, nc, t1 - t0) </code></pre> <p>and here are the results of running it (Python 2.7.1, Windows 7 Pro, "Intel Core i3 CPU 540 @ 3.07 GHz"):</p> <pre><code>C:\junk&gt;\python27\python ncust.py 300 0 80000 popsize=300 func=f_w_append niter=80000 nc=218 seconds=5.48 C:\junk&gt;\python27\python ncust.py 300 1 80000 popsize=300 func=f_w_append2 niter=80000 nc=218 seconds=4.62 C:\junk&gt;\python27\python ncust.py 300 2 80000 popsize=300 func=f_wo_append niter=80000 nc=218 seconds=5.55 C:\junk&gt;\python27\python ncust.py 300 3 80000 popsize=300 func=f_wo_append2 niter=80000 nc=218 seconds=4.29 </code></pre> <p><strong>Edit 3</strong> Why numpy takes longer:</p> <pre><code>&gt;&gt;&gt; import numpy &gt;&gt;&gt; utils = numpy.random.uniform(0, 300, 10) &gt;&gt;&gt; print repr(utils[0]) 42.777972538362874 &gt;&gt;&gt; type(utils[0]) &lt;type 'numpy.float64'&gt; </code></pre> <p>and here's why my f_wo_append2 function took 4 times longer:</p> <pre><code>&gt;&gt;&gt; x = utils[0] &gt;&gt;&gt; type(x) &lt;type 'numpy.float64'&gt; &gt;&gt;&gt; type(x &gt;= 75) &lt;type 'numpy.bool_'&gt; # iscust refers to a numpy.bool_ &gt;&gt;&gt; type(0 + (x &gt;= 75)) &lt;type 'numpy.int32'&gt; # numcustomers ends up referring to a numpy.int32 &gt;&gt;&gt; </code></pre> <p>The empirical evidence is that these custom types aren't so fast when used as scalars ... perhaps because they need to reset the floating-point hardware each time they are used. OK for big arrays, not for scalars.</p> <p>Are you using any other numpy functionality? If not, just use the <code>random</code> module. If you have other uses for numpy, you may wish to coerce the <code>numpy.float64</code> to <code>float</code> during the population setup.</p>
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    1. COThis made it a lot worse! The trimmed version takes 4 times more time than the version I have posted above.
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    2. CO@Curious2learn: Please consider carefully whether or not you have an experimental error, and publish the code that you are using. See my edited answer.
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    3. COMY MISTAKE! I see how you ran it. Let me try. (...Continued from above) When I did this with the trimmed code, it gave me 52.xxx and with my original code it have 13. I assumed they were seconds because it took around that much real time too. If you did 80000 iterations in 5 seconds that is awesome. Now I hope I really figure this out because that will substantially save my time and allow me to run simulations with many different configurations. Thank you for helping me with this.
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