Effects of caching frequently used objects, was Re: Explaining names vs variables in Python

[Peter Otten]

Salvatore DI DIO wrote:

> Hello,
> 
> I know Python does not have variables, but names.
> Multiple names cant then be bound to the same objects.
> 
> So this behavior
> 
>>>> b = 234
>>>> v = 234
>>>> b is v
> True
> 
> according to the above that is ok
> 
> 
> 
> But where is the consistency ? if I try :
> 
>>>> v = 890
>>>> w = 890
>>>> v is w
> False
> 
> It is a little difficult to explain this behavior to a newcommer in Python
> 
> Can someone give me the right argument to expose ?

You should not bother with object identity for objects other than None.
Some small integers are used a lot, e. g.

Python 3.4.3 (default, Oct 14 2015, 20:28:29) 
[GCC 4.8.4] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> sys.getrefcount(0)
606
>>> sys.getrefcount(1)
918
>>> sys.getrefcount(256)
31
>>> sys.getrefcount(-1)
51

therefore as an optimization the Python developers decided to put -5...256 
(actual range may vary across interpreter versions) into a cache and reuse 
them rather than build a new object for every instance. This may save both 
time and memory, but is otherwise irrelevant.

Something similar is done for strings:

>>> a = "hello"
>>> b = "hello"
>>> a is b
True
>>> a = "hello, world"
>>> b = "hello, world"
>>> a is b
False

But:

>>> a = "hello, world"; b = "hello, world"
>>> a is b
True

Again this is an optimization (mostly targeted at attribute names) which 
should not affect the behaviour of a properly written Python program.