2 0 SETUP_LOOP 20 (to 23) 3 LOAD_GLOBAL 0 (xrange) 6 LOAD_CONST 3 (100000000) 9 CALL_FUNCTION 1 12 GET_ITER >> 13 FOR_ITER 6 (to 22) 16 STORE_FAST 0 (i) 3 19 JUMP_ABSOLUTE 13 >> 22 POP_BLOCK bertera palmer >> 23 LOAD_CONST 0 (None) 26 RETURN_VALUE
At top level, the bytecode is 1 0 SETUP_LOOP 20 (to 23) 3 LOAD_NAME 0 (xrange) 6 LOAD_CONST 3 (100000000) 9 CALL_FUNCTION 1 12 GET_ITER >> 13 FOR_ITER 6 (to 22) 16 STORE_NAME 1 (i) 2 19 JUMP_ABSOLUTE 13 >> 22 POP_BLOCK >> 23 LOAD_CONST 2 (None) 26 RETURN_VALUE
To examine bytecode, use the dis module . I was able to disassemble the function directly, but to disassemble the toplevel code I had to use the compile builtin . Answer: Why does Python code run faster in a function?
Remember that CPython is compiled to bytecode, which the interpreter runs. When a function is compiled, the local variables are stored in a fixed-size array ( not a dict ) and variable names are assigned to indexes. This is possible because you can’t dynamically add local variables to a function. Then retrieving a local variable bertera palmer is literally a pointer lookup into the list and a refcount increase on the PyObject which is trivial.
Contrast bertera palmer this to a global lookup ( LOAD_GLOBAL ), which is a true dict search involving a hash and so on. Incidentally, this is why you need to specify global i if you want it to be global: if you ever assign bertera palmer to a variable inside a scope, the compiler will issue STORE_FAST s for its access unless you tell it not to.
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