Performance comparison from range and xrange to yield keyword (top)

Using Xrange

When we get a number of loops, we are accustomed to the for loop and range function, for example:

 for  in range:    Print i

Here the range (10) generates a 10-length list with content from 0 to 9, so the For loop here is actually the element that is traversing it.

If the number of loops is too large, range generates a huge list, which causes the program to degrade performance.

The solution is to use xrange, with the same basic usage as range:

 for  in xrange (Ten):    Print i

But how big is the difference in performance?

Performance evaluation

We use the following procedure to do a test:

 fromTimeImport Time fromTimeImportSleepImportSYSdefcount_time ():deftmp (func):defWrapped (self, *args, * *Kargs): Begin_time=Time () result= Func (self, *args, * *Kargs) End_time=Time () Cost_time= End_time-Begin_timePrint '%s called Cost time:%s Ms'% (func.__name__, float (cost_time) *1000)            returnresultreturnWrappedreturntmp@count_time ()deftest1 (length): forIinchRange (length):Pass@count_time ()deftest2 (length): forIinchxrange (length):Passif __name__=='__main__': Length= Int (sys.argv[1]) test1 (length) test2 (length)

In the above code, Count_time is an adorner that is used to count the time the program runs.

We begin the formal test below:

[Email protected]:~/documents/py|?] Python 10.py 100000Test1 called cost time:13.8590335846Mstest2 called cost time:3.76796722412Ms[email protected]:~/documents/py|? Python 10.py 100000Test1 called cost time:16.725063324Mstest2 called cost time:3.08418273926Ms[email protected]:~/documents/py|? Python 10.py 200000Test1 called cost time:34.875869751Mstest2 called cost time:7.85899162292Ms[email protected]:~/documents/py|? Python 10.py 500000Test1 called cost time:41.6638851166Mstest2 called cost time:17.1940326691Ms[email protected]:~/documents/py|? Python 10.py 500000Test1 called cost time:59.8731040955Mstest2 called cost time:14.0538215637Ms[email protected]:~/documents/py|? Python 10.py 500000Test1 called cost time:94.1109657288Mstest2 called cost time:8.5780620575Ms[email protected]:~/documents/py|? Python 10.py 500000Test1 called cost time:61.615228653Mstest2 called cost time:7.21502304077 ms

The results surprised us, the gap between the two is very clear, the highest time gap is more than 10 times times.

Let's select a few smaller data:

 [email protected]:~/documents/py|? Python 10.py 10 test1 called cost time:  0.00596046447754 Mstest2 called cost time:  0.0109672546387 ms[email protected]:  ~/  Documents/py|? Python 10.py 20test1 called Cost Time:  0.00619888305664  Mstest2 called cost time:  0.159025192261 ms[email&  Nbsp;protected]:  ~/documents/py|? Python 10.py 50test1 called Cost Time:  0.00786781311035  Mstest2 called cost time:  0.00405311584473 ms[  Email protected]:  ~/documents/py|? Python 10.py 100test1 called Cost time:  0.00786781311035 Mstest2 called cost time:  0.00309944152832 ms 

The range's performance is not bad, even beginning to slightly higher.

We can conclude that when n is smaller, we use range, but when I exceeds a certain range, we must consider using xrange .

But where are the reasons for the performance gap?

We analyze below.

Performance comparison from range and xrange to yield keyword (top)