DenkzeitWiki

Main /

Python Optimizations

Python
PythonIdioms
only optimize when there is a proven speed bottleneck
[1]

• Python Patterns - An Optimization Anecdote

The Python Profiler

Or in other words, if your Python program doesn't run fast enough, use the profiler.[2]

The first step to speeding up your program is learning where the bottlenecks lie.[3]

Speed only matters in certain critical bottlenecks.[4]

You don't need to know the type of every argument in every call in the program. You do need to be able to declare the types of arguments in the bottlenecks. And even more, you need to be able to find out where the bottlenecks are.[5]

• Python Performance Tips @ Python Wiki
• Python optimization tips

The more time your program spends at the Python level, the more chances you have to restructure your code so that more work is done by compiled C.[6]

When scalability and performance are needed, a little profiling, a little bit of high-level refactoring of your Python code, and careful implementation of critical logic in C extensions give you a level of flexibility and performance at an unmatched price point.[7]

---

taken from Python Speed @ Python Wiki

• dicts are faster that sequences
  • a in b, b should be a dict
• join strings with ' '.join(seq)
• use iterators instead of lists where appropriate (memory and speed)
• use modules and builtin types (which are written in C)
  • dict, lists, tuples, sets
• sort(key=str.lower) is faster than sort(cmp=compareFunction)
• local variable-lookup is faster than globals or builtin
  • localize variables (e.g. random=self.random)
• list-comprehension are a bit faster than for-loops
• "x,y=a,b" is slower than "x=a; y=b"
• "x,y=y,x" is faster than "t=x; x=y; y=t"
• Write "x < y < z" instead of "x < y and y < z"

---

• accessing dictionaries
  • use if-else if key is often not in dict
  • use exceptions if key is most likely in dict