There are two kind of types in Python. Immutable types and mutable types.

Immutables

An object of an immutable type cannot be changed. Any attempt to modify the object will result in a copy being created.

This category includes: integers, floats, complex, strings, bytes, tuples, ranges and frozensets.

To highlight this property, let’s play with the id builtin. This function returns the unique identifier of the object passed as parameter. If the id is the same, this is the same object. If it changes, then this is another object. (Some say that this is actually the memory address of the object, but beware of them, they are from the dark side of the force…)

>>> a = 1
>>> id(a)
140128142243264
>>> a += 2
>>> a
3
>>> id(a)
140128142243328

Okay, 1 is not 3… Breaking news… Maybe not. However, this behaviour is often forgotten when it comes to more complex types, especially strings.

>>> stack = "Overflow"
>>> stack
'Overflow'
>>> id(stack)
140128123955504
>>> stack += " rocks!"
>>> stack
'Overflow rocks!'

Aha! See? We can modify it!

>>> id(stack)
140128123911472

No. While it seems we can change the string named by the variable stack, what we actually do, is creating a new object to contain the result of the concatenation. We are fooled because in the process, the old object goes nowhere, so it is destroyed. In another situation, that would have been more obvious:

>>> stack = "Stack"
>>> stackoverflow = stack + "Overflow"
>>> id(stack)
140128069348184
>>> id(stackoverflow)
140128123911480

In this case it is clear that if we want to retain the first string, we need a copy. But is that so obvious for other types?

Exercise

Now, knowing how a immutable types work, what would you say with the below piece of code? Is it wise?

s = ""
for i in range(1, 1000):
    s += str(i)
    s += ","

Mutables

An object of a mutable type can be changed, and it is changed in-situ. No implicit copies are done.

This category includes: lists, dictionaries, bytearrays and sets.

Let’s continue to play with our little id function.