[Python] Why the referenced objects have the same ID when the same integer is assigned to different variables

A question I came across while learning about Python's assignment behavior.

If you assign the same integer to different variables, the referenced objects will have the same ID. why?

When 10 is assigned to variables a and b as shown below,

Python

>>> a = 10
>>> b = 10

In my head, like this ↓ "** values are both 10, but two objects are born in memory, each with a and b names. ID (memory address) is of course different. It was an image of "** pointing to **". It's called Python assignment is binding / bind.

However, when I check the ID of the object referenced by the variables a and b,

Python

>>> id(a)
4343178640
>>> id(b)
4343178640
>>>
>>> a is b
True
>>>

#the same...!?!?!?

It showed exactly the same ID. As shown below, ** a and b both refer to the same object **. If I had set b = a, I was satisfied with this result, but I did not substitute a for b.

According to the official documentation, an integer object between -5 and 256 already exists in memory before the assignment

I searched for "python variable int id same" and it didn't come up, so I searched for "python int same address" on Google US and it was solved.

Python WAT !? Integer Cache → [Integer Objects (official document)](https://docs.python.org/3/c- api / long.html # c.PyLong_FromLong)

The current implementation keeps an array of integer objects for all integers between -5 and 256, when you create an int in that range you actually just get back a reference to the existing object. The current implementation keeps an array of integer objects for all integers from -5 to 256, and generating a number in this range actually returns a reference to an existing object. ..

So that's why

"Integers from -5 to 256 are ready to be used in memory. So when the assignment of ʻa = 10`` b = 10is performed, both variables a and b are already in memory. Just reference the integer object10` above, and the result will be the same ID."

If you try with values of 256, 257, which is the boundary between expanded and unexpanded in memory,

Python

>>> a = 256
>>> b = 256
>>> id(a)
4343186512
>>> id(b)
4343186512 #ID is the same
>>> a is b
True
>>>
>>> a = 257
>>> b = 257
>>> id(a)
4346386992
>>> id(b)
4346387088 #ID is different! !!
>>> a is b
False
>>>

As expected from the official documentation, we can see from 257 that the objects referenced by variable a and variable b are different.

If you read the site below, the purpose is to "use numbers from -5 to 256 frequently and make them available immediately for performance". Real Python: Small Integer Caching

Experiment with floating point numbers instead of integers

I was told "I try to keep an array of ** integer objects ** ~", so I tried using floating point numbers ...

Python

>>> a = 0.5
>>> b = 0.5
>>> id(a)
4317942456
>>> id(b)
4317941880 #ID is different
>>> a is b
False
>>>

The result is that the values themselves are the same, but the IDs are different for floating point numbers. I was able to confirm that this is also only an integer as documented.

Tried environment

Python 3.7.1