Mutable Object

def foo(a=[]):
    a.append(5)
    return a

foo()

[5]

foo()

[5, 5]

foo()

[5, 5, 5]

Reason:

What causes the confusion is the behaviour you get when you use a “mutable” object as a default value; that is, a value that can be modified in place, like a list or a dictionary.

The function keeps using the same object, in each call.

It is a common mistake in Python to set a mutable object as the default value of an argument in a function

more:

https://stackoverflow.com/questions/1132941/least-astonishment-and-the-mutable-default-argument

https://stackoverflow.com/questions/9158294/good-uses-for-mutable-function-argument-default-values

http://effbot.org/zone/default-values.htm

foo([14])

[14, 5]

foo([14])

[14, 5]

# Though it looks like a fundamental flaw, you can use it for caching purpose like we do in lrucache

def add(name, cache={}):
    if name in cache: return cache[name]
    print('getting from expensive calculation')
    cache[name] = result = (name[::-1])
    return result

add('one')

getting from expensive calculation





'eno'

add('one') # This time it is coming from cache

'eno'

add('two')

getting from expensive calculation





'owt'

We will use it to calculate name meter

def get_name_meter(name,  cache={}):
    
    if name in cache: 
        return cache[name]
    
    print('getting from expensive calculation')
    
    chars = list(name) # name.split won't return chars
    
    meter = 0
    for c in chars:
        meter += ord(c)
    
    cache[name] = result = meter
    return result

get_name_meter('Toronto')

getting from expensive calculation





757

get_name_meter('Toronto') # getting from cache

757

get_name_meter('Montreal')

getting from expensive calculation





834

Like above, we can use the mutable objects to take advantage of the situation