2.1. Advanced Python Constructs

author:Zbigniew Jędrzejewski-Szmek

This chapter is about some features of the Python language which can be considered advanced — in the sense that not every language has them, and also in the sense that they are more useful in more complicated programs or libraries, but not in the sense of being particularly specialized, or particularly complicated.

It is important to underline that this chapter is purely about the language itself — about features supported through special syntax complemented by functionality of the Python stdlib, which could not be implemented through clever external modules.

The process of developing the Python programming language, its syntax, is unique because it is very transparent, proposed changes are evaluated from various angles and discussed on public mailing lists, and the final decision takes into account the balance between the importance of envisioned use cases, the burden of carrying more language features, consistency with the rest of the syntax, and whether the proposed variant is the easiest to read, write, and understand. This process is formalised in Python Enhancement Proposals — PEPs. As a result, features described in this chapter were added after it was shown that they indeed solve real problems and that their use is as simple as possible.

2.1.1. Iterators, generator expressions and generators

2.1.1.1. Iterators

An iterator is an object adhering to the iterator protocol — basically this means that it has a next method, which, when called, returns the next item in the sequence, and when there’s nothing to return, raises the StopIteration exception.

An iterator object allows to loop just once. It holds the state (position) of a single iteration, or from the other side, each loop over a sequence requires a single iterator object. This means that we can iterate over the same sequence more than once concurrently. Separating the iteration logic from the sequence allows us to have more than one way of iteration.

Calling the __iter__ method on a container to create an iterator object is the most straightforward way to get hold of an iterator. The iter function does that for us, saving a few keystrokes.

>>> nums = [1,2,3]      # note that ... varies: these are different objects
>>> iter(nums)
<listiterator object at ...>
>>> nums.__iter__()
<listiterator object at ...>
>>> nums.__reversed__()
<listreverseiterator object at ...>
>>> it = iter(nums)
>>> next(it) # next(obj) simply calls obj.next()
1
>>> it.next()
2
>>> next(it)
3
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration

When used in a loop, StopIteration is swallowed and causes the loop to finish. But with explicit invocation, we can see that once the iterator is exhausted, accessing it raises an exception.

Using the for..in loop also uses the __iter__ method. This allows us to transparently start the iteration over a sequence. But if we already have the iterator, we want to be able to use it in an for loop in the same way. In order to achieve this, iterators in addition to next are also required to have a method called __iter__ which returns the iterator (self).

Support for iteration is pervasive in Python: all sequences and unordered containers in the standard library allow this. The concept is also stretched to other things: e.g. file objects support iteration over lines.

>>> f = open('/etc/fstab')
>>> f is f.__iter__()
True

The file is an iterator itself and it’s __iter__ method doesn’t create a separate object: only a single thread of sequential access is allowed.

2.1.1.2. Generator expressions

A second way in which iterator objects are created is through generator expressions, the basis for list comprehensions. To increase clarity, a generator expression must always be enclosed in parentheses or an expression. If round parentheses are used, then a generator iterator is created. If rectangular parentheses are used, the process is short-circuited and we get a list.

>>> (i for i in nums)                    
<generator object <genexpr> at 0x...>
>>> [i for i in nums]
[1, 2, 3]
>>> list(i for i in nums)
[1, 2, 3]

In Python 2.7 and 3.x the list comprehension syntax was extended to dictionary and set comprehensions. A set is created when the generator expression is enclosed in curly braces. A dict is created when the generator expression contains “pairs” of the form key:value:

>>> {i for i in range(3)}   
set([0, 1, 2])
>>> {i:i**2 for i in range(3)}
{0: 0, 1: 1, 2: 4}

If you are stuck at some previous Python version, the syntax is only a bit worse:

>>> set(i for i in 'abc')
set(['a', 'c', 'b'])
>>> dict((i, ord(i)) for i in 'abc')
{'a': 97, 'c': 99, 'b': 98}

Generator expression are fairly simple, not much to say here. Only one gotcha should be mentioned: in old Pythons the index variable (i) would leak, and in versions >= 3 this is fixed.

2.1.1.3. Generators

A third way to create iterator objects is to call a generator function. A generator is a function containing the keyword yield. It must be noted that the mere presence of this keyword completely changes the nature of the function: this yield statement doesn’t have to be invoked, or even reachable, but causes the function to be marked as a generator. When a normal function is called, the instructions contained in the body start to be executed. When a generator is called, the execution stops before the first instruction in the body. An invocation of a generator function creates a generator object, adhering to the iterator protocol. As with normal function invocations, concurrent and recursive invocations are allowed.

When next is called, the function is executed until the first yield. Each encountered yield statement gives a value becomes the return value of next. After executing the yield statement, the execution of this function is suspended.

>>> def f():
...   yield 1
...   yield 2
>>> f()                                   
<generator object f at 0x...>
>>> gen = f()
>>> gen.next()
1
>>> gen.next()
2
>>> gen.next()
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
StopIteration

Let’s go over the life of the single invocation of the generator function.

>>> def f():
... print("-- start --")
... yield 3
... print("-- middle --")
... yield 4
... print("-- finished --")
>>> gen = f()
>>> next(gen)
-- start --
3
>>> next(gen)
-- middle --
4
>>> next(gen)
-- finished --
Traceback (most recent call last):
...
StopIteration

Contrary to a normal function, where executing f() would immediately cause the first print to be executed, gen is assigned without executing any statements in the function body. Only when gen.next() is invoked by next, the statements up to the first yield are executed. The second next prints -- middle -- and execution halts on the second yield. The third next prints -- finished -- and falls of the end of the function. Since no yield was reached, an exception is raised.

What happens with the function after a yield, when the control passes to the caller? The state of each generator is stored in the generator object. From the point of view of the generator function, is looks almost as if it was running in a separate thread, but this is just an illusion: execution is strictly single-threaded, but the interpreter keeps and restores the state in between the requests for the next value.

Why are generators useful? As noted in the parts about iterators, a generator function is just a different way to create an iterator object. Everything that can be done with yield statements, could also be done with next methods. Nevertheless, using a function and having the interpreter perform its magic to create an iterator has advantages. A function can be much shorter than the definition of a class with the required next and __iter__ methods. What is more important, it is easier for the author of the generator to understand the state which is kept in local variables, as opposed to instance attributes, which have to be used to pass data between consecutive invocations of next on an iterator object.

A broader question is why are iterators useful? When an iterator is used to power a loop, the loop becomes very simple. The code to initialise the state, to decide if the loop is finished, and to find the next value is extracted into a separate place. This highlights the body of the loop — the interesting part. In addition, it is possible to reuse the iterator code in other places.

2.1.1.4. Bidirectional communication

Each yield statement causes a value to be passed to the caller. This is the reason for the introduction of generators by PEP 255 (implemented in Python 2.2). But communication in the reverse direction is also useful. One obvious way would be some external state, either a global variable or a shared mutable object. Direct communication is possible thanks to PEP 342 (implemented in 2.5). It is achieved by turning the previously boring yield statement into an expression. When the generator resumes execution after a yield statement, the caller can call a method on the generator object to either pass a value into the generator, which then is returned by the yield statement, or a different method to inject an exception into the generator.

The first of the new methods is send(value), which is similar to next(), but passes value into the generator to be used for the value of the yield expression. In fact, g.next() and g.send(None) are equivalent.

The second of the new methods is throw(type, value=None, traceback=None) which is equivalent to:

raise type, value, traceback

at the point of the yield statement.

Unlike raise (which immediately raises an exception from the current execution point), throw() first resumes the generator, and only then raises the exception. The word throw was picked because it is suggestive of putting the exception in another location, and is associated with exceptions in other languages.

What happens when an exception is raised inside the generator? It can be either raised explicitly or when executing some statements or it can be injected at the point of a yield statement by means of the throw() method. In either case, such an exception propagates in the standard manner: it can be intercepted by an except or finally clause, or otherwise it causes the execution of the generator function to be aborted and propagates in the caller.

For completeness’ sake, it’s worth mentioning that generator iterators also have a close() method, which can be used to force a generator that would otherwise be able to provide more values to finish immediately. It allows the generator __del__ method to destroy objects holding the state of generator.

Let’s define a generator which just prints what is passed in through send and throw.

>>> import itertools
>>> def g():
... print '--start--'
... for i in itertools.count():
... print '--yielding %i--' % i
... try:
... ans = yield i
... except GeneratorExit:
... print '--closing--'
... raise
... except Exception as e:
... print '--yield raised %r--' % e
... else:
... print '--yield returned %s--' % ans
>>> it = g()
>>> next(it)
--start--
--yielding 0--
0
>>> it.send(11)
--yield returned 11--
--yielding 1--
1
>>> it.throw(IndexError)
--yield raised IndexError()--
--yielding 2--
2
>>> it.close()
--closing--

next or __next__?

In Python 2.x, the iterator method to retrieve the next value is called next. It is invoked implicitly through the global function next, which means that it should be called __next__. Just like the global function iter calls __iter__. This inconsistency is corrected in Python 3.x, where it.next becomes it.__next__. For other generator methods — send and throw — the situation is more complicated, because they are not called implicitly by the interpreter. Nevertheless, there’s a proposed syntax extension to allow continue to take an argument which will be passed to send of the loop’s iterator. If this extension is accepted, it’s likely that gen.send will become gen.__send__. The last of generator methods, close, is pretty obviously named incorrectly, because it is already invoked implicitly.

2.1.1.5. Chaining generators

Note

This is a preview of PEP 380 (not yet implemented, but accepted for Python 3.3).

Let’s say we are writing a generator and we want to yield a number of values generated by a second generator, a subgenerator. If yielding of values is the only concern, this can be performed without much difficulty using a loop such as

subgen = some_other_generator()
for v in subgen:
yield v

However, if the subgenerator is to interact properly with the caller in the case of calls to send(), throw() and close(), things become considerably more difficult. The yield statement has to be guarded by a try..except..finally structure similar to the one defined in the previous section to “debug” the generator function. Such code is provided in PEP 380, here it suffices to say that new syntax to properly yield from a subgenerator is being introduced in Python 3.3:

yield from some_other_generator()

This behaves like the explicit loop above, repeatedly yielding values from some_other_generator until it is exhausted, but also forwards send, throw and close to the subgenerator.

2.1.2. Decorators

Since a function or a class are objects, they can be passed around. Since they are mutable objects, they can be modified. The act of altering a function or class object after it has been constructed but before is is bound to its name is called decorating.

There are two things hiding behind the name “decorator” — one is the function which does the work of decorating, i.e. performs the real work, and the other one is the expression adhering to the decorator syntax, i.e. an at-symbol and the name of the decorating function.

Function can be decorated by using the decorator syntax for functions:

@decorator             # ②
def function(): # ①
pass
  • A function is defined in the standard way. ①
  • An expression starting with @ placed before the function definition is the decorator ②. The part after @ must be a simple expression, usually this is just the name of a function or class. This part is evaluated first, and after the function defined below is ready, the decorator is called with the newly defined function object as the single argument. The value returned by the decorator is attached to the original name of the function.

Decorators can be applied to functions and to classes. For classes the semantics are identical — the original class definition is used as an argument to call the decorator and whatever is returned is assigned under the original name.

Before the decorator syntax was implemented (PEP 318), it was possible to achieve the same effect by assigning the function or class object to a temporary variable and then invoking the decorator explicitly and then assigning the return value to the name of the function. This sounds like more typing, and it is, and also the name of the decorated function doubling as a temporary variable must be used at least three times, which is prone to errors. Nevertheless, the example above is equivalent to:

def function():                  # ①
pass
function = decorator(function) # ②

Decorators can be stacked — the order of application is bottom-to-top, or inside-out. The semantics are such that the originally defined function is used as an argument for the first decorator, whatever is returned by the first decorator is used as an argument for the second decorator, ..., and whatever is returned by the last decorator is attached under the name of the original function.

The decorator syntax was chosen for its readability. Since the decorator is specified before the header of the function, it is obvious that its is not a part of the function body and its clear that it can only operate on the whole function. Because the expression is prefixed with @ is stands out and is hard to miss (“in your face”, according to the PEP :) ). When more than one decorator is applied, each one is placed on a separate line in an easy to read way.

2.1.2.1. Replacing or tweaking the original object

Decorators can either return the same function or class object or they can return a completely different object. In the first case, the decorator can exploit the fact that function and class objects are mutable and add attributes, e.g. add a docstring to a class. A decorator might do something useful even without modifying the object, for example register the decorated class in a global registry. In the second case, virtually anything is possible: when something different is substituted for the original function or class, the new object can be completely different. Nevertheless, such behaviour is not the purpose of decorators: they are intended to tweak the decorated object, not do something unpredictable. Therefore, when a function is “decorated” by replacing it with a different function, the new function usually calls the original function, after doing some preparatory work. Likewise, when a class is “decorated” by replacing if with a new class, the new class is usually derived from the original class. When the purpose of the decorator is to do something “every time”, like to log every call to a decorated function, only the second type of decorators can be used. On the other hand, if the first type is sufficient, it is better to use it, because it is simpler.

2.1.2.2. Decorators implemented as classes and as functions

The only requirement on decorators is that they can be called with a single argument. This means that decorators can be implemented as normal functions, or as classes with a __call__ method, or in theory, even as lambda functions.

Let’s compare the function and class approaches. The decorator expression (the part after @) can be either just a name, or a call. The bare-name approach is nice (less to type, looks cleaner, etc.), but is only possible when no arguments are needed to customise the decorator. Decorators written as functions can be used in those two cases:

>>> def simple_decorator(function):
... print "doing decoration"
... return function
>>> @simple_decorator
... def function():
... print "inside function"
doing decoration
>>> function()
inside function
>>> def decorator_with_arguments(arg):
... print "defining the decorator"
... def _decorator(function):
... # in this inner function, arg is available too
... print "doing decoration,", arg
... return function
... return _decorator
>>> @decorator_with_arguments("abc")
... def function():
... print "inside function"
defining the decorator
doing decoration, abc
>>> function()
inside function

The two trivial decorators above fall into the category of decorators which return the original function. If they were to return a new function, an extra level of nestedness would be required. In the worst case, three levels of nested functions.

>>> def replacing_decorator_with_args(arg):
... print "defining the decorator"
... def _decorator(function):
... # in this inner function, arg is available too
... print "doing decoration,", arg
... def _wrapper(*args, **kwargs):
... print "inside wrapper,", args, kwargs
... return function(*args, **kwargs)
... return _wrapper
... return _decorator
>>> @replacing_decorator_with_args("abc")
... def function(*args, **kwargs):
... print "inside function,", args, kwargs
... return 14
defining the decorator
doing decoration, abc
>>> function(11, 12)
inside wrapper, (11, 12) {}
inside function, (11, 12) {}
14

The _wrapper function is defined to accept all positional and keyword arguments. In general we cannot know what arguments the decorated function is supposed to accept, so the wrapper function just passes everything to the wrapped function. One unfortunate consequence is that the apparent argument list is misleading.

Compared to decorators defined as functions, complex decorators defined as classes are simpler. When an object is created, the __init__ method is only allowed to return None, and the type of the created object cannot be changed. This means that when a decorator is defined as a class, it doesn’t make much sense to use the argument-less form: the final decorated object would just be an instance of the decorating class, returned by the constructor call, which is not very useful. Therefore it’s enough to discuss class-based decorators where arguments are given in the decorator expression and the decorator __init__ method is used for decorator construction.

>>> class decorator_class(object):
... def __init__(self, arg):
... # this method is called in the decorator expression
... print "in decorator init,", arg
... self.arg = arg
... def __call__(self, function):
... # this method is called to do the job
... print "in decorator call,", self.arg
... return function
>>> deco_instance = decorator_class('foo')
in decorator init, foo
>>> @deco_instance
... def function(*args, **kwargs):
... print "in function,", args, kwargs
in decorator call, foo
>>> function()
in function, () {}

Contrary to normal rules (PEP 8) decorators written as classes behave more like functions and therefore their name often starts with a lowercase letter.

In reality, it doesn’t make much sense to create a new class just to have a decorator which returns the original function. Objects are supposed to hold state, and such decorators are more useful when the decorator returns a new object.

>>> class replacing_decorator_class(object):
... def __init__(self, arg):
... # this method is called in the decorator expression
... print "in decorator init,", arg
... self.arg = arg
... def __call__(self, function):
... # this method is called to do the job
... print "in decorator call,", self.arg
... self.function = function
... return self._wrapper
... def _wrapper(self, *args, **kwargs):
... print "in the wrapper,", args, kwargs
... return self.function(*args, **kwargs)
>>> deco_instance = replacing_decorator_class('foo')
in decorator init, foo
>>> @deco_instance
... def function(*args, **kwargs):
... print "in function,", args, kwargs
in decorator call, foo
>>> function(11, 12)
in the wrapper, (11, 12) {}
in function, (11, 12) {}

A decorator like this can do pretty much anything, since it can modify the original function object and mangle the arguments, call the original function or not, and afterwards mangle the return value.

2.1.2.3. Copying the docstring and other attributes of the original function

When a new function is returned by the decorator to replace the original function, an unfortunate consequence is that the original function name, the original docstring, the original argument list are lost. Those attributes of the original function can partially be “transplanted” to the new function by setting __doc__ (the docstring), __module__ and __name__ (the full name of the function), and __annotations__ (extra information about arguments and the return value of the function available in Python 3). This can be done automatically by using functools.update_wrapper.

>>> import functools
>>> def better_replacing_decorator_with_args(arg):
... print "defining the decorator"
... def _decorator(function):
... print "doing decoration,", arg
... def _wrapper(*args, **kwargs):
... print "inside wrapper,", args, kwargs
... return function(*args, **kwargs)
... return functools.update_wrapper(_wrapper, function)
... return _decorator
>>> @better_replacing_decorator_with_args("abc")
... def function():
... "extensive documentation"
... print "inside function"
... return 14
defining the decorator
doing decoration, abc
>>> function
<function function at 0x...>
>>> print function.__doc__
extensive documentation

One important thing is missing from the list of attributes which can be copied to the replacement function: the argument list. The default values for arguments can be modified through the __defaults__, __kwdefaults__ attributes, but unfortunately the argument list itself cannot be set as an attribute. This means that help(function) will display a useless argument list which will be confusing for the user of the function. An effective but ugly way around this problem is to create the wrapper dynamically, using eval. This can be automated by using the external decorator module. It provides support for the decorator decorator, which takes a wrapper and turns it into a decorator which preserves the function signature.

To sum things up, decorators should always use functools.update_wrapper or some other means of copying function attributes.

2.1.2.4. Examples in the standard library

First, it should be mentioned that there’s a number of useful decorators available in the standard library. There are three decorators which really form a part of the language:

  • classmethod causes a method to become a “class method”, which means that it can be invoked without creating an instance of the class. When a normal method is invoked, the interpreter inserts the instance object as the first positional parameter, self. When a class method is invoked, the class itself is given as the first parameter, often called cls.

    Class methods are still accessible through the class’ namespace, so they don’t pollute the module’s namespace. Class methods can be used to provide alternative constructors:

    class Array(object):
    
    def __init__(self, data):
    self.data = data
    @classmethod
    def fromfile(cls, file):
    data = numpy.load(file)
    return cls(data)

    This is cleaner then using a multitude of flags to __init__.

  • staticmethod is applied to methods to make them “static”, i.e. basically a normal function, but accessible through the class namespace. This can be useful when the function is only needed inside this class (its name would then be prefixed with _), or when we want the user to think of the method as connected to the class, despite an implementation which doesn’t require this.

  • property is the pythonic answer to the problem of getters and setters. A method decorated with property becomes a getter which is automatically called on attribute access.

    >>> class A(object):
    
    ... @property
    ... def a(self):
    ... "an important attribute"
    ... return "a value"
    >>> A.a
    <property object at 0x...>
    >>> A().a
    'a value'

    In this example, A.a is an read-only attribute. It is also documented: help(A) includes the docstring for attribute a taken from the getter method. Defining a as a property allows it to be a calculated on the fly, and has the side effect of making it read-only, because no setter is defined.

    To have a setter and a getter, two methods are required, obviously. Since Python 2.6 the following syntax is preferred:

    class Rectangle(object):
    
    def __init__(self, edge):
    self.edge = edge
    @property
    def area(self):
    """Computed area.
    Setting this updates the edge length to the proper value.
    """
    return self.edge**2
    @area.setter
    def area(self, area):
    self.edge = area ** 0.5

    The way that this works, is that the property decorator replaces the getter method with a property object. This object in turn has three methods, getter, setter, and deleter, which can be used as decorators. Their job is to set the getter, setter and deleter of the property object (stored as attributes fget, fset, and fdel). The getter can be set like in the example above, when creating the object. When defining the setter, we already have the property object under area, and we add the setter to it by using the setter method. All this happens when we are creating the class.

    Afterwards, when an instance of the class has been created, the property object is special. When the interpreter executes attribute access, assignment, or deletion, the job is delegated to the methods of the property object.

    To make everything crystal clear, let’s define a “debug” example:

    >>> class D(object):
    
    ... @property
    ... def a(self):
    ... print "getting", 1
    ... return 1
    ... @a.setter
    ... def a(self, value):
    ... print "setting", value
    ... @a.deleter
    ... def a(self):
    ... print "deleting"
    >>> D.a
    <property object at 0x...>
    >>> D.a.fget
    <function a at 0x...>
    >>> D.a.fset
    <function a at 0x...>
    >>> D.a.fdel
    <function a at 0x...>
    >>> d = D() # ... varies, this is not the same `a` function
    >>> d.a
    getting 1
    1
    >>> d.a = 2
    setting 2
    >>> del d.a
    deleting
    >>> d.a
    getting 1
    1

    Properties are a bit of a stretch for the decorator syntax. One of the premises of the decorator syntax — that the name is not duplicated — is violated, but nothing better has been invented so far. It is just good style to use the same name for the getter, setter, and deleter methods.

Some newer examples include:

  • functools.lru_cache memoizes an arbitrary function maintaining a limited cache of arguments:answer pairs (Python 3.2)
  • functools.total_ordering is a class decorator which fills in missing ordering methods (__lt__, __gt__, __le__, ...) based on a single available one (Python 2.7).

2.1.2.5. Deprecation of functions

Let’s say we want to print a deprecation warning on stderr on the first invocation of a function we don’t like anymore. If we don’t want to modify the function, we can use a decorator:

class deprecated(object):
"""Print a deprecation warning once on first use of the function.
>>> @deprecated() # doctest: +SKIP
... def f():
... pass
>>> f() # doctest: +SKIP
f is deprecated
"""
def __call__(self, func):
self.func = func
self.count = 0
return self._wrapper
def _wrapper(self, *args, **kwargs):
self.count += 1
if self.count == 1:
print self.func.__name__, 'is deprecated'
return self.func(*args, **kwargs)

It can also be implemented as a function:

def deprecated(func):
"""Print a deprecation warning once on first use of the function.
>>> @deprecated # doctest: +SKIP
... def f():
... pass
>>> f() # doctest: +SKIP
f is deprecated
"""
count = [0]
def wrapper(*args, **kwargs):
count[0] += 1
if count[0] == 1:
print func.__name__, 'is deprecated'
return func(*args, **kwargs)
return wrapper

2.1.2.6. A while-loop removing decorator

Let’s say we have function which returns a lists of things, and this list created by running a loop. If we don’t know how many objects will be needed, the standard way to do this is something like:

def find_answers():
answers = []
while True:
ans = look_for_next_answer()
if ans is None:
break
answers.append(ans)
return answers

This is fine, as long as the body of the loop is fairly compact. Once it becomes more complicated, as often happens in real code, this becomes pretty unreadable. We could simplify this by using yield statements, but then the user would have to explicitly call list(find_answers()).

We can define a decorator which constructs the list for us:

def vectorized(generator_func):
def wrapper(*args, **kwargs):
return list(generator_func(*args, **kwargs))
return functools.update_wrapper(wrapper, generator_func)

Our function then becomes:

@vectorized
def find_answers():
while True:
ans = look_for_next_answer()
if ans is None:
break
yield ans

2.1.2.7. A plugin registration system

This is a class decorator which doesn’t modify the class, but just puts it in a global registry. It falls into the category of decorators returning the original object:

class WordProcessor(object):
PLUGINS = []
def process(self, text):
for plugin in self.PLUGINS:
text = plugin().cleanup(text)
return text
@classmethod
def plugin(cls, plugin):
cls.PLUGINS.append(plugin)
@WordProcessor.plugin
class CleanMdashesExtension(object):
def cleanup(self, text):
return text.replace('&mdash;', u'\N{em dash}')

Here we use a decorator to decentralise the registration of plugins. We call our decorator with a noun, instead of a verb, because we use it to declare that our class is a plugin for WordProcessor. Method plugin simply appends the class to the list of plugins.

A word about the plugin itself: it replaces HTML entity for em-dash with a real Unicode em-dash character. It exploits the unicode literal notation to insert a character by using its name in the unicode database (“EM DASH”). If the Unicode character was inserted directly, it would be impossible to distinguish it from an en-dash in the source of a program.

2.1.2.8. More examples and reading

2.1.3. Context managers

A context manager is an object with __enter__ and __exit__ methods which can be used in the with statement:

with manager as var:
do_something(var)

is in the simplest case equivalent to

var = manager.__enter__()
try:
do_something(var)
finally:
manager.__exit__()

In other words, the context manager protocol defined in PEP 343 permits the extraction of the boring part of a try..except..finally structure into a separate class leaving only the interesting do_something block.

  1. The __enter__ method is called first. It can return a value which will be assigned to var. The as-part is optional: if it isn’t present, the value returned by __enter__ is simply ignored.
  2. The block of code underneath with is executed. Just like with try clauses, it can either execute successfully to the end, or it can break, continue` or return, or it can throw an exception. Either way, after the block is finished, the __exit__ method is called. If an exception was thrown, the information about the exception is passed to __exit__, which is described below in the next subsection. In the normal case, exceptions can be ignored, just like in a finally clause, and will be rethrown after __exit__ is finished.

Let’s say we want to make sure that a file is closed immediately after we are done writing to it:

>>> class closing(object):
... def __init__(self, obj):
... self.obj = obj
... def __enter__(self):
... return self.obj
... def __exit__(self, *args):
... self.obj.close()
>>> with closing(open('/tmp/file', 'w')) as f:
... f.write('the contents\n')

Here we have made sure that the f.close() is called when the with block is exited. Since closing files is such a common operation, the support for this is already present in the file class. It has an __exit__ method which calls close and can be used as a context manager itself:

>>> with open('/tmp/file', 'a') as f:
... f.write('more contents\n')

The common use for try..finally is releasing resources. Various different cases are implemented similarly: in the __enter__ phase the resource is acquired, in the __exit__ phase it is released, and the exception, if thrown, is propagated. As with files, there’s often a natural operation to perform after the object has been used and it is most convenient to have the support built in. With each release, Python provides support in more places:

2.1.3.1. Catching exceptions

When an exception is thrown in the with-block, it is passed as arguments to __exit__. Three arguments are used, the same as returned by sys.exc_info(): type, value, traceback. When no exception is thrown, None is used for all three arguments. The context manager can “swallow” the exception by returning a true value from __exit__. Exceptions can be easily ignored, because if __exit__ doesn’t use return and just falls of the end, None is returned, a false value, and therefore the exception is rethrown after __exit__ is finished.

The ability to catch exceptions opens interesting possibilities. A classic example comes from unit-tests — we want to make sure that some code throws the right kind of exception:

class assert_raises(object):
# based on pytest and unittest.TestCase
def __init__(self, type):
self.type = type
def __enter__(self):
pass
def __exit__(self, type, value, traceback):
if type is None:
raise AssertionError('exception expected')
if issubclass(type, self.type):
return True # swallow the expected exception
raise AssertionError('wrong exception type')
with assert_raises(KeyError):
{}['foo']

2.1.3.2. Using generators to define context managers

When discussing generators, it was said that we prefer generators to iterators implemented as classes because they are shorter, sweeter, and the state is stored as local, not instance, variables. On the other hand, as described in Bidirectional communication, the flow of data between the generator and its caller can be bidirectional. This includes exceptions, which can be thrown into the generator. We would like to implement context managers as special generator functions. In fact, the generator protocol was designed to support this use case.

@contextlib.contextmanager
def some_generator(<arguments>):
<setup>
try:
yield <value>
finally:
<cleanup>

The contextlib.contextmanager helper takes a generator and turns it into a context manager. The generator has to obey some rules which are enforced by the wrapper function — most importantly it must yield exactly once. The part before the yield is executed from __enter__, the block of code protected by the context manager is executed when the generator is suspended in yield, and the rest is executed in __exit__. If an exception is thrown, the interpreter hands it to the wrapper through __exit__ arguments, and the wrapper function then throws it at the point of the yield statement. Through the use of generators, the context manager is shorter and simpler.

Let’s rewrite the closing example as a generator:

@contextlib.contextmanager
def closing(obj):
try:
yield obj
finally:
obj.close()

Let’s rewrite the assert_raises example as a generator:

@contextlib.contextmanager
def assert_raises(type):
try:
yield
except type:
return
except Exception as value:
raise AssertionError('wrong exception type')
else:
raise AssertionError('exception expected')

Here we use a decorator to turn generator functions into context managers!