Comments on: 10 Python Optimization Tips and Issues

By: Luke Maurits » Blog Archive » Python vs C for performance

Luke Maurits » Blog Archive » Python vs C for performance — Thu, 22 Apr 2010 03:11:13 +0000

[...] Reddit today I came across a fairly decent article on Python optimization tips and issues, which comes across fairly heavily in favour of the idea that by being careful and knowing what [...]

By: Paddy3118

Paddy3118 — Wed, 23 Sep 2009 18:59:18 +0000

Benny,
It might be best to design first for correctness and then address any performance issues.

By: Benny Daon

Benny Daon — Wed, 23 Sep 2009 06:10:31 +0000

Great post, thanks.

I guess the bottom line is that an algorithm should be designed for performance and later use profiling to improve implementation speed.

By: Vaibhav Mallya

Vaibhav Mallya — Tue, 22 Sep 2009 18:56:57 +0000

One cheap trick you can also use is to cache a method call in a tight loop. Method lookups can be expensive, so for example in:

> for i in xrange(1000):
> myobj.compute(i)

you can eliminate the lookup:

> compute = myobj.compute
> for i in xrange(1000):
> compute(i)

By: garg

garg — Tue, 22 Sep 2009 17:05:19 +0000

Thanks for posting this. I found it to be very practical and helpful.

By: Paddy3118

Paddy3118 — Tue, 22 Sep 2009 02:30:31 +0000

One more…
Understand where your program fits. It may be easier to optimise a cooperating program rather than the current one, to gain the performance gain needed for the whole system.

- Paddy.

By: yuval

yuval — Mon, 21 Sep 2009 22:42:09 +0000

Really nice info, I agree with everything. I’ve met lazy people (as programmers should be) that skip the profiling step and just guess where they need to optimize so I’d like to +1 how important it is. Not to mention it’s easy:

import profile
profile.run(“any_python_expression”)

where usually you’d replace any_python_expression with a call to your main function…