Finding closure with closures

01 Jun 2016

This is a presentation I gave at PyCon 2016. You can watch the video on YouTube and view the slides served from the repo on GitHub.

A friend of mine was asked what a closure was at a programming interview a few years ago. Despite being a competent Python and JavaScript programmer who took advantage of closures in code he wrote, he froze up at the question. It’d be nice to have something to say in response to this question, if not a solid definition.

Programmers more familiar with other languages have also asked me, “Tom, you know Python; does Python even have closures?” and “I heard Python has weak support for closures.” Once we’ve reached closure on this topic, I hope you’ll be able to respond productively and engage with questions and misunderstandings about Python scope others might have.

To find our closure we’ll start with the importance of environment to our functions and compare lexical and dynamic scope. Then we’ll follow the evolution of variables scoping in the Python language over the last 25 years. We’ll conclude that some Python certainly supports closures, but that which Python functions count as closures and since which version of Python they have depends on the definition of closure used.

Consider two functions for formatting strings: one for bolding text in HTML, the other for bolding text in the terminal. We’ll import these functions from their respective modules using the from ... import ... as syntax because they both have the same name.

>>> from htmlformat import bold as htmlbold
>>> from terminalformat import bold as termbold

The source code for these two functions can be viewed with the builtin inspect module.

>>> import inspect
>>> print(inspect.getsource(htmlbold))
def bold(text):
    return '{}{}{}'.format(BOLDBEFORE, text, BOLDAFTER)

>>> print(inspect.getsource(termbold))
def bold(text):
    return '{}{}{}'.format(BOLDBEFORE, text, BOLDAFTER)

Although these functions appear identical, they have different behavior:

>>> htmlbold('eggplant')
>>> termbold('eggplant')

How is this possible; what differs between these two functions? Here’s another, similar question: We saw before that the bold function uses the variable BOLDBEFORE. Because it is neither a parameter to the function nor a local variable, we call it a “free variable”. If we call that function after setting a local variable with the same name, will that change its behavior? Will

>>> from htmlformat import bold as htmlbold
>>> def signbold(phrase):
...     BEFOREBOLD = '(in Sharpie) '
...     return htmlbold(phrase)
>>> signbold('eggplant')

output '<b>eggplant</b>' or '(in Sharpie) eggplant</b'?

The question amounts to whether the Python language uses “open free variables” whose values are determined by looking up the call stack (dynamic scope), or closed free variables that use the value in the environment in which the function was defined (lexical scope).

In a 1970 paper describing implementions of these two approaches, Joel Moses points out that although it might be easier to implement a language with the first behavior, programmers are usually interested in the second. They want their functions to use the variables they created for use with that function, not new variables at their functions’ call sites. The answer is that Python ignores this new variable and bold tags again surround the word eggplant.

What about changing the global variable?

>>> from htmlformat import bold as htmlbold
>>> BEFOREBOLD = '(in Sharpie) '
>>> htmlbold('eggplant')

More eggplant sandwich on bold tags! The global variables in another module are not affected by changes to global variables in this one.

Now let’s finally take a look at those bold functions.

BEFOREBOLD = '<b>'               BEFOREBOLD = '\x1b[1m'
AFTERBOLD = '</b>'               AFTERBOLD = '\x1b[0m'

def bold(text):                  def bold(text):
    return '{}{}{}'.format(          return '{}{}{}'.format(
      BOLDBEFORE,                      BOLDBEFORE,
      text,                            text,
      BOLDAFTER)                       BOLDAFTER)

Each formatting module has its own global variables. Indeed, “global” variables are terribly named because there aren’t global to your whole Python program. Since we’re stuck with that name, perhaps we should imagine each module as its own planet.

When functions are imported from another module, they emerge as emissaries from their planets with live links back to their home worlds they use to look up variables.

For function objects in Python hold not only a reference to the name of their home module (the .__module__ attribute) but also a reference to the very namespace of that module which contains bindings from global variables names to values (__globals__).

In the paper mentioned earlier, Joel Moses described an implementation of this type of behavior: for a function to behave this way, it needs both code to execute and the environment which closes the variables use in that function. He called this combination of code and environment a closure. So Python functions are already sounding a lot like closures!

Because this is a live link, any updates to the bindings that occur in the home module after the function is defined are still available to the function. We can even change global bindings directly by rebinding attributes of this data structurel that represents this environment: the imported module object.

The distinction between function definition time and function execution becomes important with this “live link” behavior. It turns out that Python analyzes function source code, even compiles it, when a function is defined. During this process it determines the scope of each variable. This determines the process that will be used to find the value of each variables, but does not actually look up this value yet.

A Python function object is the result of this process. Each of its attributes stores a different piece of computer-readable information about about the function. Most of this information is in the code object stored by the __code__ attribute.

Since Python has been available on the internet, there have been at least two types of variables in functions: local variables and global variables. Local variables (including function parameters) appear in .__code__.co_varnames and global variables and a few other things make up .__code__.co_names. Identifying the scope of a variable is a task Python programmers do frequently as they read code, so you may already have an intuition for the rules. Let’s try at a few examples to understand the rule.

>>> def movie_titleize(phrase):
...     capitalized = phrase.title()
...     return capitalized + ": The Untold Story"

In this function for building great movie titles, are phrase and capitalized local or global variables?

They are both local. One is a parameter to the function, the other is assigned to on the first line. This type of function, sometimes called a “pure” function, doesn’t need its link to its home module for looking up variables. Without this associated environment, the function would not be a closure, and here we find out first fork in the meaning of the word. Is a function a closure if it has this link to its defining environment but that environment is never used? Some would say functions require free variables to be closures, others that the combination of code and environment is enough, so long as the Python doesn’t remove this link link to home module. For an altogether different reason, most would say that none of the functions we have seen so far are closures. Hang on for that reason in a few minutes.

>>> def catchy(phrase):
...     options = [phrase.title(), DEFAULT_TITLE]
...     options.sort(key=catchiness)
...     return options[1]

In this function for finding catchy phrases, are phrase, options, DEFAULT_TITLE, and catchiness local variables or global variables? Once you decide, you can find out whether you agree with the Python interpreter by checking those interesting attributes of the function’s code object:

>>> catchy.__code__.co_varnames
('phrase', 'options')
>>> catchy.__code__.co_names
('catchiness', 'DEFAULT_TITLE', 'sort', 'catchiness')

phrase and options are local variables because the first was a parameter and the second was assigned to. DEFAULT_TITLE and catchiness fit neither of these descriptions so they are global variables. A few extra strings are in the co_names tuple because Python uses this list for most that storing global variables. If you saw this function in some source code and wanted to copy it alone to use, you wouldn’t be able to: there’s important environment information you would also need to include.

Your programmer intuition might disagree with Python’s categorization in this next example.

>>> HIGH_SCORE = 1000
>>> def new_high_score(score):
...     print('congrats on the high score!')
...     print('old high score:', HIGH_SCORE)
...     HIGH_SCORE = score
>>> new_high_score(1042)

It certainly looks like the author of the function wanted HIGH_SCORE to be a global variable, but Python categorizes it as a local variable because it’s assigned to in the function. Calling the function results in an UnboundLocalError because the variable, considered local for the entirety of the function, doesn’t have a value assigned yet when it’s printed as the old high score.

The programmer can express this authorial intent to Python with the global keyword, which changes the categorization of HIGH_SCORE from local variable to global.

>>> HIGH_SCORE = 1000
>>> def new_high_score(score):
...     global HIGH_SCORE
...     print('congrats on the high score!')
...     print('old high score:', HIGH_SCORE)
...     HIGH_SCORE = score
>>> new_high_score.__code__.co_varnames
('score', 'HIGH_SCORE')
>>> new_high_score.__code__.co_names
>>> new_high_score(1042)
congrats on the high score!
old high score: 1000

With the global keyword we’ve now completed a description of how scope has worked in Python from its inception through to Python 2.0 in the year 2000. Python functions have always closed over their own module-level environment. But an important method of closing free variables was still not available to us, one required by most to classify a function as a closure: using outer scopes that are not the global scope.

def tallest_building():
    buildings = {'Burj Khalifa': 828,
                 'Shanghai Tower': 632,
                 'Abraj Al-Bait': 601}

    def height(name):
        return buildings[name]

    return max(buildings.keys(), key=height)

Are the variables name and buildings local or global variables in the height function above? name is certainly local as a parameter, but buildings is neither local or global, it comes from an outer non-global scope. Since buildings is not a local variable it is assumed to be global in Python 2.0 and calling it produces “global name ‘buildings’ is not defined” NameError. Optionally in Python 2.1, then by default in Python 2.2, variables from outer non-global scopes were added and are found at .__code__.co_freevars:

>>> height.__code__.co_varnames
>>> height.__code__.co_names
>>> height.__code__.co_freevars

Typically when people talk about closures they mean closing around these in between outer scopes that are neither local nor global. Closing over the module-level “global” scope is considered a special case, and indeed is simpler to implement. You may already be familiar with module objects in Python: generally they’re singletons, so a given module has only one mapping of variables to values. But a function can be run many times, producing many different mappings of its local variables to values. Each of which must be kept track of so long as a function that was defined in this or an enclosing scope still exists.

formatters = {}
colors = ['red', 'green', 'blue']
for color in colors:
    def in_color(s):
        return ('<span style="color:' +
                color + '">' + s + '</span>')
    formatters[color] = in_color


The code above defines several functions for formatting text in color in html. With which color does the green one of these functions format the text 'hello'? Since these three functions were defined in the same environment, they share the same mapping of variables to values. If we consider the value of the color variable once the for loop has finished, it becomes clear that all three functions have the same behavior: coloring strings blue. If each function is to have a different value associated with the color variable it is necessary to create separate scopes for these functions to be defined in:

formatters = {}
colors = ['red', 'green', 'blue']
def make_color_func(color):
    def in_color(s):
        return ('<span style="color:' +
                color + '">' + s + '</span>')
    return in_color

for color in colors:
    formatters[color] = make_color_func(color)


Each time the make_color_func function is called, a new local mapping is created binding color to one of red, green or blue; a function called in_color is defined which references the color variable in this outer scope; and the in_color function is returned and stuck in a dictionary.

This solution to the “late-binding” behavior of Python relies on separate scopes being created for each function and requires that Python maintain three sets of bindings for the make_color_func function’s local scope. Precisely how these bindings are maintained by Python is beyond our scope here, but the .__closure__ attribute on each of the three produced functions provides some hint.

We’ve reached the most common definition of a closure: a function with variables closed by an outer, non-global scope. However another fork in definitions occurs here: some would call our three color functions closures and but not the earlier height function because it was used in the same scope it was defined. Although CPython doesn’t implement the two any differently, you can imagine that it becomes more difficult to maintain the environment a function to evaluate its variables once the bindings it needs goes out of scope. The distinction here is that looking up the stack instead of the “closure” solution of code + environment would result in the same behavior in the first case, making whether a function was a closure or not only distinguishable in the second case.

So by Python 2.2, functions in Python are definitely closures: every function is always a closure by my most library definition since they all carry environment with them, and only those which reference variables which have gone out of scope by the strictest. Nothing much changes with scope through the Python 2.x series, so we have now covered scope in Python up through 2.7, through to the year 2008.

But if rumblings of the insufficiency of Python’s closures have ever reached your ears, you may not have found your closure yet. You may have heard that Python has “weak support” for closures, or that Python has “read-only” closures, not “full” closures. This comes from an asymmetry between global variables and outer non-global variables, which I will from now on refer to as “nonlocal” variables.

>>> def get_number_guesser(answer):
...     last_guess = None
...     def guess(n):
...         if n == last_guess:
...             print('already guessed that!')
...         last_guess = n
...         return n == answer
>>> guess = get_number_guesser(12)
>>> guess(9)

Like the earlier example demonstrating the usefulness of the global keyword, the inner guess function above assigns to the variable last_guess that the programmer meant not to be local. How can Python be informed of this intent? With the new nonlocal keyword in Python 3.

Without nonlocal, nonlocal variables cannot be rebound to new values. Nonlocal mutable objects can be mutated for a similar effect, but the identity of the object in an outer binding cannot be changed. But since Python 3, we definitely have “full” closures now; there are no more missing details.

As with the global keyword the change in semantics may seem small, but its lack is met with incredulity in Python 2 by some familiar with closures in other languages. As we find our closure with what closures are and whether they exist in Python, a new question arises: how did we get on without them for so long?

We use closures all over the place in Python: inner functions (often written with the lambda syntax) that reference outer scopes abound, and the use of functions in interfaces as callbacks makes their use more likely. Decorators always take a function as an argument and often define a new function to replace it, which itself typically holds a reference to the old function through a free variable from the outer function scope of the decorator. We can inspect a function for its .__closure__ attribute to see if it contains free variables that are closed by outer, nonlocal scopes for those who demand this of their closures.

Adding the nonlocal keyword took nine years, from Python 2.2 in 2001 to Python 3 in 2008. If it’s so important a change, why don’t we see a ton of code using it now?

The global keyword may have delayed this need: module-level bindings have been modifiable in Python for a long time. And now that nonlocal is here, the need for compatibility with Python 2 code that many library authors have prevents some uses. Consider this abridged excerpt from Django:

def decorating_function(user_function):
    nonlocal_root = [root]  # make updateable non-locally

    def wrapper():
        nonlocal_root[0] = oldroot[NEXT]

Since the root variable in the outer function cannot directly changed, it is stuck in the simplest possible mutable object – a list – which is mutated to imitate rebinding. Based on the name, it’s clear both that nonlocal would be a good fit here and that the author of this code knew that when they wrote it. But compatibility with Python 2 forces the word nonlocal to be used here only to evoke the idea of a nonlocal variable.

This pattern is less common than in some languages because of Python’s excellent object system, in particular its ability to bind methods to objects. When a callback function which accesses or modifies some internal state is needed, often that state will be placed in a class instance. State on objects in Python is readable and instrospectable, and the methods of an object can be used as callbacks.

def tallest_building():
    buildings = {'Burj Khalifa': 828,
                 'Shanghai Tower': 632,
                 'Abraj Al-Bait': 601}

    return max(buildings.keys(), key=buildings.get)

Here the get method of the builtin Python dictionary object is used as a callback, which concisely expresses what data the method will operate on.

And finally I posit a cultural reason: Python programmers tend to be comfortable with private data being externally accessible. Python and JavaScript are relatively similar languages, and both lack (or at least in certain versions have lacked) private object data which can be accessed by methods of the object but not by outside code, instead using conventions like a single underscore to inform users that an attribute is not part of the public interface with that object. In both languages the following pattern is possible, but in JavaScript it is commonplace while in Python it is unheard of.

>>> class Person(object): pass
>>> def create_person(name):
...     age = 10
...     p = Person()
...     def birthday():
...         nonlocal age
...         age = age + 1
...     p.birthday = birthday
...     p.greet = lambda: print("Hi, I'm", name, "and I'm", age, "years old")
...     return p
>>> me = create_person('Tom')
>>> me.birthday()
>>> me.age
Traceback (most recent call last):
  File "<input>", line 1, in <module>
AttributeError: 'Problem' object has no attribute 'age'

The above code hides data in local variables of a constructor function which inner functions have access to, then adds these methods to the Person instance. Now the Person instance has methods for accessing and modifying private data that are not attributes of the object itself. Again, this pattern is entirely possible and achieves the same aim as in JavaScript, but culturally isn’t used in Python.

I think it’s fine that we don’t use rebinding closures all that much. In new code nonlocal should be used when appropriate instead of the mutable object hack we saw above, but it’s fine for it to remain relatively rare.

Some modern Python functions are most certainly closures: whether it’s all of them or just a few depends on the definition. Is it enough to be capable of referring to variables from outer scopes (all Python functions), or must the functions make use of this ability? Must these outer scopes not be global? Must the scopes referenced by the free variables go out of scope to prove a function is a closure, or is storing the environment such that the variables could go out of scope enough? And must closures be able to rebind these free variables, disqualifying all Python 2 functions?

I like the “all Python 2.2 and greater functions which close over outer, non-global scopes are closures” answer, but have found closure in knowing the discussion to have if I were asked.

I hope I’ve helped you find closure with closures.

Further reading:

Related Python topics:

  • builtins: last resort of failed global variable lookups
  • __closure__ and __code__.cell_vars: how closures are implemented
  • bytecode: what does “compiling” a function really mean?
  • descriptors and method binding: the dark secret that turns functions into methods
  • scopes of various comprehensions and generator expressions: I lied when I said scope hasn’t changed much

Others’ thoughts on closures in Python:

Others’ thoughts on closures: