When a Python interpreter deals with a .py file, is it different from working with a single expression?

Question

Duration:

a = 257 b = 257 print id(a) == id(b) 

Results in:

The same statement, but opposite results. What for?

Answer 1

The code in test.py parsed together, which is more optimized than the code parsed as separate instructions in the interpreter

When you put it in test.py and run it as a whole, the byte code compiler is more likely to analyze the use of literals and optimize them. (Therefore, you get a and b pointing to the same place)

Unlike when you run separate interpreters (separately understood) in the interpreter (where I think it optimizes only to 256, not 257 through preallocation)

Play with this in the interpreter to see the effect of individual statements:

 >>> a, b = 257, 257 # or if you prefer: a = 257; b = 257 >>> print a is b True >>> a = 257 >>> b = 257 >>> print a is b False 

Defining a function in an interoperator also makes it possible to analyze and optimize the literals used.

 >>> def test(): ... a = 257 ... b = 257 ... print a is b ... >>> test() True 

This optimization is not limited to integers, it works, for example. for float too (floats do not fall under caching as integers in the range [-5, 256] )

 >>> def test(): ... pi = 3.14 ... x = 3.14 ... return x is pi ... >>> test() True # As opposed to separate statements: >>> pi = 3.14 >>> x = 3.14 >>> x is pi False 

Looking at the byte code to see that it really uses the same constant

 >>> dis.dis(test) 2 0 LOAD_CONST 1 (3.14) 3 STORE_FAST 0 (pi) 3 6 LOAD_CONST 1 (3.14) <-- Same constant 1 reused 9 STORE_FAST 1 (x) 4 12 LOAD_FAST 1 (x) 15 LOAD_FAST 0 (pi) 18 COMPARE_OP 8 (is) 21 RETURN_VALUE