Why aren't python nested functions called closures?

Question

I have seen and used nested functions in Python, and they match the definition of a closure. So why are they called nested functions instead of closures<

Answer 1

The question has already been answered by aaronasterling

However, someone might be interested in how the variables are stored under the hood.

Before coming to the snippet:

Closures are functions that inherit variables from their enclosing environment. When you pass a function callback as an argument to another function that will do I/O, this callback function will be invoked later, and this function will — almost magically — remember the context in which it was declared, along with all the variables available in that context.

• If a function does not use free variables it doesn't form a closure.

• If there is another inner level which uses free variables -- all previous levels save the lexical environment ( example at the end )

• function attributes func_closure in python < 3.X or __closure__ in python > 3.X save the free variables.

• Every function in python has this closure attributes, but it doesn't save any content if there is no free variables.

example: of closure attributes but no content inside as there is no free variable.

>>> def foo():
...     def fii():
...         pass
...     return fii
...
>>> f = foo()
>>> f.func_closure
>>> 'func_closure' in dir(f)
True
>>>

NB: FREE VARIABLE IS MUST TO CREATE A CLOSURE.

I will explain using the same snippet as above:

>>> def make_printer(msg):
...     def printer():
...         print msg
...     return printer
...
>>> printer = make_printer('Foo!')
>>> printer()  #Output: Foo!

And all Python functions have a closure attribute so let's examine the enclosing variables associated with a closure function.

Here is the attribute func_closure for the function printer

>>> 'func_closure' in dir(printer)
True
>>> printer.func_closure
(<cell at 0x108154c90: str object at 0x108151de0>,)
>>>

The closure attribute returns a tuple of cell objects which contain details of the variables defined in the enclosing scope.

The first element in the func_closure which could be None or a tuple of cells that contain bindings for the function’s free variables and it is read-only.

>>> dir(printer.func_closure[0])
['__class__', '__cmp__', '__delattr__', '__doc__', '__format__', '__getattribute__',
 '__hash__', '__init__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', 
 '__setattr__',  '__sizeof__', '__str__', '__subclasshook__', 'cell_contents']
>>>

Here in the above output you can see cell_contents, let's see what it stores:

>>> printer.func_closure[0].cell_contents
'Foo!'    
>>> type(printer.func_closure[0].cell_contents)
<type 'str'>
>>>

So, when we called the function printer(), it accesses the value stored inside the cell_contents. This is how we got the output as 'Foo!'

Again I will explain using the above snippet with some changes:

 >>> def make_printer(msg):
 ...     def printer():
 ...         pass
 ...     return printer
 ...
 >>> printer = make_printer('Foo!')
 >>> printer.func_closure
 >>>

In the above snippet, I din't print msg inside the printer function, so it doesn't create any free variable. As there is no free variable, there will be no content inside the closure. Thats exactly what we see above.

Now I will explain another different snippet to clear out everything Free Variable with Closure:

>>> def outer(x):
...     def intermediate(y):
...         free = 'free'
...         def inner(z):
...             return '%s %s %s %s' %  (x, y, free, z)
...         return inner
...     return intermediate
...
>>> outer('I')('am')('variable')
'I am free variable'
>>>
>>> inter = outer('I')
>>> inter.func_closure
(<cell at 0x10c989130: str object at 0x10c831b98>,)
>>> inter.func_closure[0].cell_contents
'I'
>>> inn = inter('am')

So, we see that a func_closure property is a tuple of closure cells, we can refer them and their contents explicitly -- a cell has property "cell_contents"

>>> inn.func_closure
(<cell at 0x10c9807c0: str object at 0x10c9b0990>, 
 <cell at 0x10c980f68: str object at   0x10c9eaf30>, 
 <cell at 0x10c989130: str object at 0x10c831b98>)
>>> for i in inn.func_closure:
...     print i.cell_contents
...
free
am 
I
>>>

Here when we called inn, it will refer all the save free variables so we get I am free variable

>>> inn('variable')
'I am free variable'
>>>

Answer 2

I'd like to offer another simple comparison between python and JS example, if this helps make things clearer.

JS:

function make () {
  var cl = 1;
  function gett () {
    console.log(cl);
  }
  function sett (val) {
    cl = val;
  }
  return [gett, sett]
}

and executing:

a = make(); g = a[0]; s = a[1];
s(2); g(); // 2
s(3); g(); // 3

Python:

def make (): 
  cl = 1
  def gett ():
    print(cl);
  def sett (val):
    cl = val
  return gett, sett

and executing:

g, s = make()
g() #1
s(2); g() #1
s(3); g() #1

Reason: As many others said above, in python, if there is an assignment in the inner scope to a variable with the same name, a new reference in the inner scope is created. Not so with JS, unless you explicitly declare one with the var keyword.

Answer 3

Python has a weak support for closure. To see what I mean take the following example of a counter using closure with JavaScript:

function initCounter(){
    var x = 0;
    function counter  () {
        x += 1;
        console.log(x);
    };
    return counter;
}

count = initCounter();

count(); //Prints 1
count(); //Prints 2
count(); //Prints 3

Closure is quite elegant since it gives functions written like this the ability to have "internal memory". As of Python 2.7 this is not possible. If you try

def initCounter():
    x = 0;
    def counter ():
        x += 1 ##Error, x not defined
        print x
    return counter

count = initCounter();

count(); ##Error
count();
count();

You'll get an error saying that x is not defined. But how can that be if it has been shown by others that you can print it? This is because of how Python it manages the functions variable scope. While the inner function can read the outer function's variables, it cannot write them.

This is a shame really. But with just read-only closure you can at least implement the function decorator pattern for which Python offers syntactic sugar.

Update

As its been pointed out, there are ways to deal with python's scope limitations and I'll expose some.

1. Use the global keyword (in general not recommended).

2. In Python 3.x, use the nonlocal keyword (suggested by @unutbu and @leewz)

3. Define a simple modifiable class Object

class Object(object):
    pass

and create an Object scope within initCounter to store the variables

def initCounter ():
    scope = Object()
    scope.x = 0
    def counter():
        scope.x += 1
        print scope.x

    return counter

Since scope is really just a reference, actions taken with its fields do not really modify scope itself, so no error arises.

4. An alternative way, as @unutbu pointed out, would be to define each variable as an array (x = [0]) and modify it's first element (x[0] += 1). Again no error arises because x itself is not modified.

5. As suggested by @raxacoricofallapatorius, you could make x a property of counter

def initCounter ():

    def counter():
        counter.x += 1
        print counter.x

    counter.x = 0
    return counter

Answer 4

People are confusing about what closure is. Closure is not the inner function. the meaning of closure is act of closing. So inner function is closing over a nonlocal variable which is called free variable.

def counter_in(initial_value=0):
    # initial_value is the free variable
    def inc(increment=1):
        nonlocal initial_value
        initial_value += increment
        return print(initial_value)
    return inc

when you call counter_in() this will return inc function which has a free variable initial_value. So we created a CLOSURE. people call inc as closure function and I think this is confusing people, people think "ok inner functions are closures". in reality inc is not a closure, since it is part of the closure, to make life easy, they call it closure function.

  myClosingOverFunc=counter_in(2)

this returns inc function which is closing over the free variable initial_value. when you invoke myClosingOverFunc

 myClosingOverFunc() 

it will print 2.

when python sees that a closure sytem exists, it creates a new obj called CELL. this will store only the name of the free variable which is initial_value in this case. This Cell obj will point to another object which stores the value of the initial_value.

in our example, initial_value in outer function and inner function will point to this cell object, and this cell object will be point to the value of the initial_value.

  variable initial_value =====>> CELL ==========>> value of initial_value

So when you call counter_in its scope is gone, but it does not matter. because variable initial_value is directly referencing the CELL Obj. and it indirectly references the value of initial_value. That is why even though scope of outer function is gone, inner function will still have access to the free variable

let's say I want to write a function, which takes in a function as an arg and returns how many times this function is called.

def counter(fn):
    # since cnt is a free var, python will create a cell and this cell will point to the value of cnt
    # every time cnt changes, cell will be pointing to the new value
    cnt = 0

    def inner(*args, **kwargs):
        # we cannot modidy cnt with out nonlocal
        nonlocal cnt
        cnt += 1
        print(f'{fn.__name__} has been called {cnt} times')
        # we are calling fn indirectly via the closue inner
        return fn(*args, **kwargs)
    return inner
      

in this example cnt is our free variable and inner + cnt create CLOSURE. when python sees this it will create a CELL Obj and cnt will always directly reference this cell obj and CELL will reference the another obj in the memory which stores the value of cnt. initially cnt=0.

 cnt   ======>>>>  CELL  =============>  0

when you invoke the inner function wih passing a parameter counter(myFunc)() this will increase the cnt by 1. so our referencing schema will change as follow:

 cnt   ======>>>>  CELL  =============>  1  #first counter(myFunc)()
 cnt   ======>>>>  CELL  =============>  2  #second counter(myFunc)()
 cnt   ======>>>>  CELL  =============>  3  #third counter(myFunc)()

this is only one instance of closure. You can create multiple instances of closure with passing another function

counter(differentFunc)()

this will create a different CELL obj from the above. We just have created another closure instance.

 cnt  ======>>  difCELL  ========>  1  #first counter(differentFunc)()
 cnt  ======>>  difCELL  ========>  2  #secon counter(differentFunc)()
 cnt  ======>>  difCELL  ========>  3  #third counter(differentFunc)()


  

Answer 5

A closure occurs when a function has access to a local variable from an enclosing scope that has finished its execution.

def make_printer(msg):
    def printer():
        print msg
    return printer

printer = make_printer('Foo!')
printer()

When make_printer is called, a new frame is put on the stack with the compiled code for the printer function as a constant and the value of msg as a local. It then creates and returns the function. Because the function printer references the msg variable, it is kept alive after the make_printer function has returned.

So, if your nested functions don't

1. access variables that are local to enclosing scopes,
2. do so when they are executed outside of that scope,

then they are not closures.

Here's an example of a nested function which is not a closure.

def make_printer(msg):
    def printer(msg=msg):
        print msg
    return printer

printer = make_printer("Foo!")
printer()  #Output: Foo!

Here, we are binding the value to the default value of a parameter. This occurs when the function printer is created and so no reference to the value of msg external to printer needs to be maintained after make_printer returns. msg is just a normal local variable of the function printer in this context.

Answer 6

I had a situation where I needed a separate but persistent name space. I used classes. I don't otherwise. Segregated but persistent names are closures.

>>> class f2:
...     def __init__(self):
...         self.a = 0
...     def __call__(self, arg):
...         self.a += arg
...         return(self.a)
...
>>> f=f2()
>>> f(2)
2
>>> f(2)
4
>>> f(4)
8
>>> f(8)
16

# **OR**
>>> f=f2() # **re-initialize**
>>> f(f(f(f(2)))) # **nested**
16

# handy in list comprehensions to accumulate values
>>> [f(i) for f in [f2()] for i in [2,2,4,8]][-1] 
16