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

[Ethan Furman]

On 03/25/2016 06:03 AM, Albert-Jan Roskam wrote:
> Somebody wrote:
>> Somebody else wrote:


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.


No.  The correct answer is: if identity is important either ensure the 
object you are getting back is a singleton (such as None, True, an Enum 
member, etc.) or you assign one name from another name:


--> b = 234
--> v = b
--> b is v
True
--> v = 890
--> w = v
--> v is w
True

If identity is not important, don't use `is`.


A little late to the party, but: how about Ellipsis? Shouldn't "is" also be 
used for that one? (It's rare, I know :))


Ellipsis is a singleton, so `is` is fine.

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

[Chris Angelico]

On Sat, Mar 26, 2016 at 12:03 AM, Albert-Jan Roskam
 wrote:
>> You should not bother with object identity for objects other than None.
>
>
> A little late to the party, but: how about Ellipsis? Shouldn't "is" also be 
> used for that one? (It's rare, I know :))

Yes, and also True and False, if you're checking for the specific
values. (Though it's more common in Python to merely care about
truthiness/falsiness.) Other common identity checks include:

if str is bytes:
# Python 2 handling, where "native strings" are byte strings
else:
# Python 3 handling, where "native strings" are text strings

if iter(x) is x:
# x is an iterator, not some other iterable

_SENTINEL = object()
def func(arg=_SENTINEL):
if arg is _SENTINEL:
# arg was not passed

Anyone who says that identity checks are only for None is
significantly over-simplifying things. This isn't necessarily a bad
thing, but it should never be taken to be the whole story.

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

[Albert-Jan Roskam]

> To: python-list@python.org
> From: __pete...@web.de
> Subject: Effects of caching frequently used objects, was Re: Explaining  
> names  vs variables  in Python
> Date: Wed, 2 Mar 2016 10:12:48 +0100
> 
> 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.


A little late to the party, but: how about Ellipsis? Shouldn't "is" also be 
used for that one? (It's rare, I know :))
Albert-Jan

  
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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.

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