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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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