On Fri, Apr 12, 2013 at 1:42 PM, Amit Saha <amitsaha...@gmail.com> wrote: > On Fri, Apr 12, 2013 at 1:36 PM, Steven D'Aprano <st...@pearwood.info> wrote: >> On 12/04/13 12:53, Amit Saha wrote: >> >>> So for example: >>> >>>>>> a=1 >>>>>> b=1 >>>>>> a is b >>> >>> True >>>>>> >>>>>> id(a) == id(b) >>> >>> True >> >> >> >> This is not a very good example, because that behaviour itself is >> implementation-dependent and not guaranteed. For example, in IronPython 2.6 >> I get completely different behaviour: >> >>>>> a = 1 >>>>> b = 1 >>>>> a is b >> >> False >>>>> >>>>> print id(a), id(b) >> >> 43 44 >> >> >> Even in CPython, the implementation you are probably using, the behaviour >> will depend on all sorts of factors, such as the type of the object, and >> even the value: >> >> py> a = 10001 >> py> b = 10001 >> py> a is b >> False >> >> py> a = 1.0 >> py> b = 1.0 >> py> a is b >> False >> >> >> >> >> >>> So, when are identifiers same and when not? That >>> depends on the type of object - mutable or immutable. >> >> >> No, not really. The best you can say is this: >> >> If Python has a choice between creating a new object, and re-using an >> existing object, and the object is mutable, Python will never re-use the >> object since that might introduce bugs. If the object is immutable, Python >> *may* re-use it, or it may not. > > Indeed. My point was to give the original poster at least *some* idea > of what the issue really is. If he/she takes that hint and > experiments, reads the Python data model, and finds something weird > with that statement - he/she will discover the finer/exactly correct > details. So, thanks for clarifying this. > > >> >> >> When does Python have a choice between re-using existing objects? That's a >> complicated question, and there's no straightforward answer. The best I can >> do is give some examples: >> >> >> # References to a name will ALWAYS use the same object: >> x is x # always True >> >> >> # References to another identifier MIGHT re-use the same object: >> x.attr is x.attr # sometimes True, sometimes False >> x[index] is x[index] # the same >> >> >> # References to a literal MIGHT re-use the same object, if it is immutable: >> 'x' is 'x' # might be True >> ['x'] is ['x'] # always False >> >> >> # The same literal on the same line of code MIGHT be re-used: >> x = 123.5; y = 123.5; x is y # might be True >> >> # ...even if they aren't re-used when on separate lines. >> x = 123.5 >> y = 123.5 >> x is y # probably will be False >> >> >> # If two or more identifiers are assigned to a value at the same time, >> # Python GUARANTEES to use the same object: >> x = y = anything_you_like() >> x is y # always True >> >> >> # Assignment in general never makes a new object: >> x = something() >> y = x >> x is y # always True[1] >> >> >> Object identity is almost never important. About the only time it is >> important is when comparing things to None. >> >> But in practice, you can expect (but not rely on!) CPython to re-use the >> following: >> >> * Small integers. Which values count as small depend on the version, but -1 >> to 100 is common. >> >> * Strings that look like identifiers, e.g. "x", "item", but not "hello >> world" or "?". > > The link to the SO question discusses string interning to some detail. > >> >> But don't rely on this, as it is not guaranteed and could go away at any >> time. > > Yes, the 'is' check shouldn't really be relied on checks such as those > for None objects.
Correction: Yes, the 'is' check shouldn't really be relied on checks other than checks for None objects (for example). -- http://amitsaha.github.com/ _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
