Python id()

Python cache the id() value of commonly used data types, such as string, integer, tuples etc. So you might find that multiple variables refer to the same object and have same id() value if their values are same. Let’s check this out with an example.

# integers
a = 10
b = 10
c = 11
d = 12
print(id(a))
print(id(b))
print(id(c))
print(id(d))

Output:

4317900064
4317900064
4317900096
4317900128

Notice that id() value of ‘a’ and ‘b’ are same, they have the same integer value. Let’s see if we get the similar behavior with string and tuples too?

# tuples
t = ('A', 'B')
print(id(t))
t1 = ('A', 'B')
print(id(t1))
# strings
s1 = 'ABC'
s2 = 'ABC'
print(id(s1))
print(id(s2))

Output:

4320130056
4320130056
4320080816
4320080816

From the output, it’s clear that Python cache the strings and tuple objects and use them to save memory space.

Caching can work only with immutable objects, notice that integer, string, tuples are immutable. So Python implementation can use caching to save memory space and improve performance.

We know that dictionary is not immutable, let’s see if id() is different for different dictionaries even if the elements are same?

# dict
d1 = {"A": 1, "B": 2}
d2 = {"A": 1, "B": 2}
print(id(d1))
print(id(d2))

Output:

4519884624
4519884768

As we thought, dict objects are returning different id() value and there seems no caching here.

Let’s see a simple example of getting id() value for a custom object.

class Emp:
    a = 0
e1 = Emp()
e2 = Emp()
print(id(e1))
print(id(e2))

Output:

4520251744
4520251856

Python id() value is guaranteed to be unique and constant for an object. We can use this to make sure two objects are referring to the same object in memory or not.

You can checkout complete python script and more Python examples from our GitHub Repository.

Reference: Official Documentation