9. MutationΒΆ

The mutable and immutable datatypes in Python cause a lot of headache for new programmers. In simple words, mutable means ‘able to be changed’ and immutable means ‘constant’. Want your head to spin? Consider this example:

foo = ['hi']
print(foo)
# Output: ['hi']

bar = foo
bar += ['bye']
print(foo)
# Output: ['hi', 'bye']

What just happened? We were not expecting that! We were expecting something like this:

foo = ['hi']
print(foo)
# Output: ['hi']

bar = foo
bar += ['bye']

print(foo)
# Output: ['hi']

print(bar)
# Output: ['hi', 'bye']

It’s not a bug. It’s mutability in action. Whenever you assign a variable to another variable of mutable datatype, any changes to the data are reflected by both variables. The new variable is just an alias for the old variable. This is only true for mutable datatypes. Here is a gotcha involving functions and mutable data types:

def add_to(num, target=[]):
    target.append(num)
    return target

add_to(1)
# Output: [1]

add_to(2)
# Output: [1, 2]

add_to(3)
# Output: [1, 2, 3]

You might have expected it to behave differently. You might be expecting that a fresh list would be created when you call add_to like this:

def add_to(num, target=[]):
    target.append(num)
    return target

add_to(1)
# Output: [1]

add_to(2)
# Output: [2]

add_to(3)
# Output: [3]

Well again it is the mutability of lists which causes this pain. In Python the default arguments are evaluated once when the function is defined, not each time the function is called. You should never define default arguments of mutable type unless you know what you are doing. You should do something like this:

def add_to(element, target=None):
    if target is None:
        target = []
    target.append(element)
    return target

Now whenever you call the function without the target argument, a new list is created. For instance:

add_to(42)
# Output: [42]

add_to(42)
# Output: [42]

add_to(42)
# Output: [42]