Why Does Python Code Run Faster Inside Functions?

Enhanced Performance of Python Code Within Functions

This question investigates why Python code executes significantly faster when placed within a function. In the provided code, a loop iterates through a large range, and the execution time varies dramatically depending on whether the loop is contained within a function.

Cause of Performance Difference

The performance difference arises from the underlying bytecode generated for the code. Within a function, the bytecode employs the STORE_FAST instruction to assign a value to a local variable. This process is optimized and typically faster than using the STORE_NAME instruction, which is used to assign a value to a global or nonlocal variable.

Bytecode Analysis

The bytecode for the loop within the function is as follows:

SETUP_LOOP              20 (to 23)
LOAD_GLOBAL              0 (xrange)
LOAD_CONST               3 (100000000)
CALL_FUNCTION            1
GET_ITER            
FOR_ITER                 6 (to 22)
STORE_FAST               0 (i)
JUMP_ABSOLUTE           13
POP_BLOCK           
LOAD_CONST               0 (None)
RETURN_VALUE        

In contrast, the bytecode for the loop outside the function is:

SETUP_LOOP              20 (to 23)
LOAD_NAME                0 (xrange)
LOAD_CONST               3 (100000000)
CALL_FUNCTION            1
GET_ITER            
FOR_ITER                 6 (to 22)
STORE_NAME               1 (i)
JUMP_ABSOLUTE           13
POP_BLOCK           
LOAD_CONST               2 (None)
RETURN_VALUE        

Conclusion

The use of STORE_FAST instead of STORE_NAME in the bytecode generated for the loop within the function contributes to its enhanced execution speed. This demonstrates the importance of understanding bytecode optimization techniques to improve Python code performance.

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