for loop in python is 10x slower than matlab -

I have run Python 2.7 and matlab R2010a on the same machine, nothing is doing, and it speed me up 10x separates

I looked online, and heard that it should be the same order Python will be slow and statement for statement and math operator

My question: is this a reality? Or in any other way let them move in the same speed order?

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Here is the Python Code

  Import time for start_time = time.time () for the in xchange (1000): xchange (1000) for c : AliPad_time = time.time () - current-time print 'time cost =', expired_time   
  output: time cost = 0.0377440452576   
 Here is the matlab code  
  tick for i = 1: 1000 j = 1: 1000 end end toc   
  Output: Escape time 0.004200 The second is    
 
 
 The reason for this is being related to the compiler, which is loop Sector is being able to adapt to MATLAB for. You can disable / enable JIT accelerator by using  feature axle off  and  on feature acceleration . When you disable the accelerator, the time varies dramatically.  
 MATLAB: With Excel, the end time is 0.009407 seconds.   
> Passed time is 0.28 9 55 seconds.   
 Python:  time cost = 0.0511920452118   
 Thus jet induction is generated directly at the speed that you see. There is another thing that you should consider, which is related to the method that you have defined the inventory index. In both cases, MATLAB and Python, you used Iterators to define your loop. In MATLAB, you create the actual value by adding square brackets ( [] ), and in Python, you use  xrange  instead of  category  If you make these changes, then the dragon (1000) for range R = 1000 [1: 1000] for j = [1: 1000]: range in C: (1000):     
 The time elapses  
 with acceleration on MATLAB:  the end time is 0.338701 seconds.   
 Closed:  Elapsed time is 0.28 9 20 seconds.   
 Python:  time cost = 0.0606048107147   
 One final idea is if you add a quick count in the loop i.e.  t = t + 1 . Then the time is  
 Excel on the catalog:  The time passed is 1.340830 seconds.   
 Excel with message closed:  The expired time is 0.905956 seconds.  (yes it was fast)  
 Python:  time cost = 0.147221088409   
 I think that here's ethical for calculating speed Loops, out-of-box, are comparable to the very simple loops depending on the situation. However, in Ajnant there are other, numerical devices that can speed things up, numpy and PyPy have been brought so far.