As far as I know, everything is object in Python and the id() should (am I right?) return a different number for each object.
In my case, id(1)
returns 4298178968
, id(2)
returns 4298178944
but I get the same values for all float types, id(1.1)
returns 4298189032
, id(2.2)
also returns 4298189032
and more.
Why I get the same id for all float values?
In that case the float value containing variable may contain same data but reason behind is to mathematical rules. The only two value are consider after the point & remaining are value get neglected. so the we get two different addresses of the same data containing variables.
Bookmark this question. Show activity on this post. according to this link Senderle answered that immutable object references have the same id and mutable objects like lists have different ids.
Yes, an integral value can be added to a float value. The basic math operations ( + , - , * , / ), when given an operand of type float and int , the int is converted to float first.
Python can reuse memory positions.
When you run:
id(1.1)
you create a float value, ask for its id()
, and then Python deletes the value again because nothing refers to it. When you then create another float value, Python can reuse the same memory position and thus id(2.2)
is likely to return the same value for id()
:
>>> id(1.1)
140550721129024
>>> id(2.2)
140550721129024
Do this instead:
float_one, float_two = 1.1, 2.2
print id(float_one), id(float_two)
Now the float values have references to them (the two variables) and won't be destroyed, and they now have different memory positions and thus id()
values.
The reason you see different id()
values for small integers (from -5 through to 256) is because these values are interned; Python only creates one 1
integer object and re-uses it over and over again. As a result, these integers all have a unique memory address regardles, as the Python interpreter itself already refers to them, and won't delete them until the interpreter exits.
>>> id(1.1)
154154684
As 1.1 was not assigned to any variable so it is garbage collected and next time same id is going to be used for a float:
>>> id(2.2)
154154684
save 1.1 in a variable:
>>> f = 1.1
>>> id(f)
154154684 #this id is locked for now
Now new address is used:
>>> id(1.1)
154154700
>>> id(2.2)
154154700
This applies to integers as well:
>>> id(260)
154302180
>>> id(280)
154302180
Integers from -5
to 256
are actually cached in python, so they are always going to return different IDs.( "is" operator behaves unexpectedly with integers )
For strings:
Like integers some strings are also cached in python. So, id
of such strings is going to be different(For details read : 'is' operator behaves differently when comparing strings with spaces ):
>>> id('foo')
162861592
>>> id('foo')
162861568
Non-alphanumeric string(uses same id
):
>>> id('foo!&9((&')
162840000
>>> id('foo!&9((&')
162840000
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