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Python Enhancement Proposals

PEP 343 – The “with” Statement

Guido van Rossum, Alyssa Coghlan
Standards Track
02-Jun-2005, 16-Oct-2005, 29-Oct-2005, 23-Apr-2006, 01-May-2006, 30-Jul-2006

Table of Contents


This PEP adds a new statement “with” to the Python language to make it possible to factor out standard uses of try/finally statements.

In this PEP, context managers provide __enter__() and __exit__() methods that are invoked on entry to and exit from the body of the with statement.

Author’s Note

This PEP was originally written in first person by Guido, and subsequently updated by Alyssa (Nick) Coghlan to reflect later discussion on python-dev. Any first person references are from Guido’s original.

Python’s alpha release cycle revealed terminology problems in this PEP and in the associated documentation and implementation [13]. The PEP stabilised around the time of the first Python 2.5 beta release.

Yes, the verb tense is messed up in a few places. We’ve been working on this PEP for over a year now, so things that were originally in the future are now in the past :)


After a lot of discussion about PEP 340 and alternatives, I decided to withdraw PEP 340 and proposed a slight variant on PEP 310. After more discussion, I have added back a mechanism for raising an exception in a suspended generator using a throw() method, and a close() method which throws a new GeneratorExit exception; these additions were first proposed on python-dev in [2] and universally approved of. I’m also changing the keyword to ‘with’.

After acceptance of this PEP, the following PEPs were rejected due to overlap:

  • PEP 310, Reliable Acquisition/Release Pairs. This is the original with-statement proposal.
  • PEP 319, Python Synchronize/Asynchronize Block. Its use cases can be covered by the current PEP by providing suitable with-statement controllers: for ‘synchronize’ we can use the “locking” template from example 1; for ‘asynchronize’ we can use a similar “unlocking” template. I don’t think having an “anonymous” lock associated with a code block is all that important; in fact it may be better to always be explicit about the mutex being used.

PEP 340 and PEP 346 also overlapped with this PEP, but were voluntarily withdrawn when this PEP was submitted.

Some discussion of earlier incarnations of this PEP took place on the Python Wiki [3].

Motivation and Summary

PEP 340, Anonymous Block Statements, combined many powerful ideas: using generators as block templates, adding exception handling and finalization to generators, and more. Besides praise it received a lot of opposition from people who didn’t like the fact that it was, under the covers, a (potential) looping construct. This meant that break and continue in a block-statement would break or continue the block-statement, even if it was used as a non-looping resource management tool.

But the final blow came when I read Raymond Chen’s rant about flow-control macros [1]. Raymond argues convincingly that hiding flow control in macros makes your code inscrutable, and I find that his argument applies to Python as well as to C. I realized that PEP 340 templates can hide all sorts of control flow; for example, its example 4 (auto_retry()) catches exceptions and repeats the block up to three times.

However, the with-statement of PEP 310 does not hide control flow, in my view: while a finally-suite temporarily suspends the control flow, in the end, the control flow resumes as if the finally-suite wasn’t there at all.

Remember, PEP 310 proposes roughly this syntax (the “VAR =” part is optional):

with VAR = EXPR:

which roughly translates into this:


Now consider this example:

with f = open("/etc/passwd"):

Here, just as if the first line was “if True” instead, we know that if BLOCK1 completes without an exception, BLOCK2 will be reached; and if BLOCK1 raises an exception or executes a non-local goto (a break, continue or return), BLOCK2 is not reached. The magic added by the with-statement at the end doesn’t affect this.

(You may ask, what if a bug in the __exit__() method causes an exception? Then all is lost – but this is no worse than with other exceptions; the nature of exceptions is that they can happen anywhere, and you just have to live with that. Even if you write bug-free code, a KeyboardInterrupt exception can still cause it to exit between any two virtual machine opcodes.)

This argument almost led me to endorse PEP 310, but I had one idea left from the PEP 340 euphoria that I wasn’t ready to drop: using generators as “templates” for abstractions like acquiring and releasing a lock or opening and closing a file is a powerful idea, as can be seen by looking at the examples in that PEP.

Inspired by a counter-proposal to PEP 340 by Phillip Eby I tried to create a decorator that would turn a suitable generator into an object with the necessary __enter__() and __exit__() methods. Here I ran into a snag: while it wasn’t too hard for the locking example, it was impossible to do this for the opening example. The idea was to define the template like this:

def opening(filename):
    f = open(filename)
        yield f

and used it like this:

with f = opening(filename): data from f...

The problem is that in PEP 310, the result of calling EXPR is assigned directly to VAR, and then VAR’s __exit__() method is called upon exit from BLOCK1. But here, VAR clearly needs to receive the opened file, and that would mean that __exit__() would have to be a method on the file.

While this can be solved using a proxy class, this is awkward and made me realize that a slightly different translation would make writing the desired decorator a piece of cake: let VAR receive the result from calling the __enter__() method, and save the value of EXPR to call its __exit__() method later. Then the decorator can return an instance of a wrapper class whose __enter__() method calls the generator’s next() method and returns whatever next() returns; the wrapper instance’s __exit__() method calls next() again but expects it to raise StopIteration. (Details below in the section Optional Generator Decorator.)

So now the final hurdle was that the PEP 310 syntax:

with VAR = EXPR:

would be deceptive, since VAR does not receive the value of EXPR. Borrowing from PEP 340, it was an easy step to:

with EXPR as VAR:

Additional discussion showed that people really liked being able to “see” the exception in the generator, even if it was only to log it; the generator is not allowed to yield another value, since the with-statement should not be usable as a loop (raising a different exception is marginally acceptable). To enable this, a new throw() method for generators is proposed, which takes one to three arguments representing an exception in the usual fashion (type, value, traceback) and raises it at the point where the generator is suspended.

Once we have this, it is a small step to proposing another generator method, close(), which calls throw() with a special exception, GeneratorExit. This tells the generator to exit, and from there it’s another small step to proposing that close() be called automatically when the generator is garbage-collected.

Then, finally, we can allow a yield-statement inside a try-finally statement, since we can now guarantee that the finally-clause will (eventually) be executed. The usual cautions about finalization apply – the process may be terminated abruptly without finalizing any objects, and objects may be kept alive forever by cycles or memory leaks in the application (as opposed to cycles or leaks in the Python implementation, which are taken care of by GC).

Note that we’re not guaranteeing that the finally-clause is executed immediately after the generator object becomes unused, even though this is how it will work in CPython. This is similar to auto-closing files: while a reference-counting implementation like CPython deallocates an object as soon as the last reference to it goes away, implementations that use other GC algorithms do not make the same guarantee. This applies to Jython, IronPython, and probably to Python running on Parrot.

(The details of the changes made to generators can now be found in PEP 342 rather than in the current PEP)

Use Cases

See the Examples section near the end.

Specification: The ‘with’ Statement

A new statement is proposed with the syntax:

with EXPR as VAR:

Here, ‘with’ and ‘as’ are new keywords; EXPR is an arbitrary expression (but not an expression-list) and VAR is a single assignment target. It can not be a comma-separated sequence of variables, but it can be a parenthesized comma-separated sequence of variables. (This restriction makes a future extension possible of the syntax to have multiple comma-separated resources, each with its own optional as-clause.)

The “as VAR” part is optional.

The translation of the above statement is:

mgr = (EXPR)
exit = type(mgr).__exit__  # Not calling it yet
value = type(mgr).__enter__(mgr)
exc = True
        VAR = value  # Only if "as VAR" is present
        # The exceptional case is handled here
        exc = False
        if not exit(mgr, *sys.exc_info()):
        # The exception is swallowed if exit() returns true
    # The normal and non-local-goto cases are handled here
    if exc:
        exit(mgr, None, None, None)

Here, the lowercase variables (mgr, exit, value, exc) are internal variables and not accessible to the user; they will most likely be implemented as special registers or stack positions.

The details of the above translation are intended to prescribe the exact semantics. If either of the relevant methods are not found as expected, the interpreter will raise AttributeError, in the order that they are tried (__exit__, __enter__). Similarly, if any of the calls raises an exception, the effect is exactly as it would be in the above code. Finally, if BLOCK contains a break, continue or return statement, the __exit__() method is called with three None arguments just as if BLOCK completed normally. (I.e. these “pseudo-exceptions” are not seen as exceptions by __exit__().)

If the “as VAR” part of the syntax is omitted, the “VAR =” part of the translation is omitted (but mgr.__enter__() is still called).

The calling convention for mgr.__exit__() is as follows. If the finally-suite was reached through normal completion of BLOCK or through a non-local goto (a break, continue or return statement in BLOCK), mgr.__exit__() is called with three None arguments. If the finally-suite was reached through an exception raised in BLOCK, mgr.__exit__() is called with three arguments representing the exception type, value, and traceback.

IMPORTANT: if mgr.__exit__() returns a “true” value, the exception is “swallowed”. That is, if it returns “true”, execution continues at the next statement after the with-statement, even if an exception happened inside the with-statement. However, if the with-statement was left via a non-local goto (break, continue or return), this non-local return is resumed when mgr.__exit__() returns regardless of the return value. The motivation for this detail is to make it possible for mgr.__exit__() to swallow exceptions, without making it too easy (since the default return value, None, is false and this causes the exception to be re-raised). The main use case for swallowing exceptions is to make it possible to write the @contextmanager decorator so that a try/except block in a decorated generator behaves exactly as if the body of the generator were expanded in-line at the place of the with-statement.

The motivation for passing the exception details to __exit__(), as opposed to the argument-less __exit__() from PEP 310, was given by the transactional() use case, example 3 below. The template in that example must commit or roll back the transaction depending on whether an exception occurred or not. Rather than just having a boolean flag indicating whether an exception occurred, we pass the complete exception information, for the benefit of an exception-logging facility for example. Relying on sys.exc_info() to get at the exception information was rejected; sys.exc_info() has very complex semantics and it is perfectly possible that it returns the exception information for an exception that was caught ages ago. It was also proposed to add an additional boolean to distinguish between reaching the end of BLOCK and a non-local goto. This was rejected as too complex and unnecessary; a non-local goto should be considered unexceptional for the purposes of a database transaction roll-back decision.

To facilitate chaining of contexts in Python code that directly manipulates context managers, __exit__() methods should not re-raise the error that is passed in to them. It is always the responsibility of the caller of the __exit__() method to do any reraising in that case.

That way, if the caller needs to tell whether the __exit__() invocation failed (as opposed to successfully cleaning up before propagating the original error), it can do so.

If __exit__() returns without an error, this can then be interpreted as success of the __exit__() method itself (regardless of whether or not the original error is to be propagated or suppressed).

However, if __exit__() propagates an exception to its caller, this means that __exit__() itself has failed. Thus, __exit__() methods should avoid raising errors unless they have actually failed. (And allowing the original error to proceed isn’t a failure.)

Transition Plan

In Python 2.5, the new syntax will only be recognized if a future statement is present:

from __future__ import with_statement

This will make both ‘with’ and ‘as’ keywords. Without the future statement, using ‘with’ or ‘as’ as an identifier will cause a Warning to be issued to stderr.

In Python 2.6, the new syntax will always be recognized; ‘with’ and ‘as’ are always keywords.

Generator Decorator

With PEP 342 accepted, it is possible to write a decorator that makes it possible to use a generator that yields exactly once to control a with-statement. Here’s a sketch of such a decorator:

class GeneratorContextManager(object):

   def __init__(self, gen):
       self.gen = gen

   def __enter__(self):
       except StopIteration:
           raise RuntimeError("generator didn't yield")

   def __exit__(self, type, value, traceback):
       if type is None:
           except StopIteration:
               raise RuntimeError("generator didn't stop")
               self.gen.throw(type, value, traceback)
               raise RuntimeError("generator didn't stop after throw()")
           except StopIteration:
               return True
               # only re-raise if it's *not* the exception that was
               # passed to throw(), because __exit__() must not raise
               # an exception unless __exit__() itself failed.  But
               # throw() has to raise the exception to signal
               # propagation, so this fixes the impedance mismatch
               # between the throw() protocol and the __exit__()
               # protocol.
               if sys.exc_info()[1] is not value:

def contextmanager(func):
   def helper(*args, **kwds):
       return GeneratorContextManager(func(*args, **kwds))
   return helper

This decorator could be used as follows:

def opening(filename):
   f = open(filename) # IOError is untouched by GeneratorContext
       yield f
       f.close() # Ditto for errors here (however unlikely)

A robust implementation of this decorator will be made part of the standard library.

Context Managers in the Standard Library

It would be possible to endow certain objects, like files, sockets, and locks, with __enter__() and __exit__() methods so that instead of writing:

with locking(myLock):

one could write simply:

with myLock:

I think we should be careful with this; it could lead to mistakes like:

f = open(filename)
with f:
with f:

which does not do what one might think (f is closed before BLOCK2 is entered).

OTOH such mistakes are easily diagnosed; for example, the generator context decorator above raises RuntimeError when a second with-statement calls f.__enter__() again. A similar error can be raised if __enter__ is invoked on a closed file object.

For Python 2.5, the following types have been identified as context managers:

- file
- thread.LockType
- threading.Lock
- threading.RLock
- threading.Condition
- threading.Semaphore
- threading.BoundedSemaphore

A context manager will also be added to the decimal module to support using a local decimal arithmetic context within the body of a with statement, automatically restoring the original context when the with statement is exited.

Standard Terminology

This PEP proposes that the protocol consisting of the __enter__() and __exit__() methods be known as the “context management protocol”, and that objects that implement that protocol be known as “context managers”. [4]

The expression immediately following the with keyword in the statement is a “context expression” as that expression provides the main clue as to the runtime environment the context manager establishes for the duration of the statement body.

The code in the body of the with statement and the variable name (or names) after the as keyword don’t really have special terms at this point in time. The general terms “statement body” and “target list” can be used, prefixing with “with” or “with statement” if the terms would otherwise be unclear.

Given the existence of objects such as the decimal module’s arithmetic context, the term “context” is unfortunately ambiguous. If necessary, it can be made more specific by using the terms “context manager” for the concrete object created by the context expression and “runtime context” or (preferably) “runtime environment” for the actual state modifications made by the context manager. When simply discussing use of the with statement, the ambiguity shouldn’t matter too much as the context expression fully defines the changes made to the runtime environment. The distinction is more important when discussing the mechanics of the with statement itself and how to go about actually implementing context managers.

Caching Context Managers

Many context managers (such as files and generator-based contexts) will be single-use objects. Once the __exit__() method has been called, the context manager will no longer be in a usable state (e.g. the file has been closed, or the underlying generator has finished execution).

Requiring a fresh manager object for each with statement is the easiest way to avoid problems with multi-threaded code and nested with statements trying to use the same context manager. It isn’t coincidental that all of the standard library context managers that support reuse come from the threading module - they’re all already designed to deal with the problems created by threaded and nested usage.

This means that in order to save a context manager with particular initialisation arguments to be used in multiple with statements, it will typically be necessary to store it in a zero-argument callable that is then called in the context expression of each statement rather than caching the context manager directly.

When this restriction does not apply, the documentation of the affected context manager should make that clear.

Resolved Issues

The following issues were resolved by BDFL approval (and a lack of any major objections on python-dev).

  1. What exception should GeneratorContextManager raise when the underlying generator-iterator misbehaves? The following quote is the reason behind Guido’s choice of RuntimeError for both this and for the generator close() method in PEP 342 (from [8]):

    “I’d rather not introduce a new exception class just for this purpose, since it’s not an exception that I want people to catch: I want it to turn into a traceback which is seen by the programmer who then fixes the code. So now I believe they should both raise RuntimeError. There are some precedents for that: it’s raised by the core Python code in situations where endless recursion is detected, and for uninitialized objects (and for a variety of miscellaneous conditions).”

  2. It is fine to raise AttributeError instead of TypeError if the relevant methods aren’t present on a class involved in a with statement. The fact that the abstract object C API raises TypeError rather than AttributeError is an accident of history, rather than a deliberate design decision [11].
  3. Objects with __enter__/__exit__ methods are called “context managers” and the decorator to convert a generator function into a context manager factory is contextlib.contextmanager. There were some other suggestions [15] during the 2.5 release cycle but no compelling arguments for switching away from the terms that had been used in the PEP implementation were made.

Rejected Options

For several months, the PEP prohibited suppression of exceptions in order to avoid hidden flow control. Implementation revealed this to be a right royal pain, so Guido restored the ability [12].

Another aspect of the PEP that caused no end of questions and terminology debates was providing a __context__() method that was analogous to an iterable’s __iter__() method [5] [7] [9]. The ongoing problems [10] [12] with explaining what it was and why it was and how it was meant to work eventually lead to Guido killing the concept outright [14] (and there was much rejoicing!).

The notion of using the PEP 342 generator API directly to define the with statement was also briefly entertained [6], but quickly dismissed as making it too difficult to write non-generator based context managers.


The generator based examples rely on PEP 342. Also, some of the examples are unnecessary in practice, as the appropriate objects, such as threading.RLock, are able to be used directly in with statements.

The tense used in the names of the example contexts is not arbitrary. Past tense (“-ed”) is used when the name refers to an action which is done in the __enter__ method and undone in the __exit__ method. Progressive tense (“-ing”) is used when the name refers to an action which is to be done in the __exit__ method.

  1. A template for ensuring that a lock, acquired at the start of a block, is released when the block is left:
    def locked(lock):

    Used as follows:

    with locked(myLock):
        # Code here executes with myLock held.  The lock is
        # guaranteed to be released when the block is left (even
        # if via return or by an uncaught exception).
  2. A template for opening a file that ensures the file is closed when the block is left:
    def opened(filename, mode="r"):
        f = open(filename, mode)
            yield f

    Used as follows:

    with opened("/etc/passwd") as f:
        for line in f:
            print line.rstrip()
  3. A template for committing or rolling back a database transaction:
    def transaction(db):
            yield None
  4. Example 1 rewritten without a generator:
    class locked:
       def __init__(self, lock):
           self.lock = lock
       def __enter__(self):
       def __exit__(self, type, value, tb):

    (This example is easily modified to implement the other relatively stateless examples; it shows that it is easy to avoid the need for a generator if no special state needs to be preserved.)

  5. Redirect stdout temporarily:
    def stdout_redirected(new_stdout):
        save_stdout = sys.stdout
        sys.stdout = new_stdout
            yield None
            sys.stdout = save_stdout

    Used as follows:

    with opened(filename, "w") as f:
        with stdout_redirected(f):
            print "Hello world"

    This isn’t thread-safe, of course, but neither is doing this same dance manually. In single-threaded programs (for example, in scripts) it is a popular way of doing things.

  6. A variant on opened() that also returns an error condition:
    def opened_w_error(filename, mode="r"):
            f = open(filename, mode)
        except IOError, err:
            yield None, err
                yield f, None

    Used as follows:

    with opened_w_error("/etc/passwd", "a") as (f, err):
        if err:
            print "IOError:", err
  7. Another useful example would be an operation that blocks signals. The use could be like this:
    import signal
    with signal.blocked():
        # code executed without worrying about signals

    An optional argument might be a list of signals to be blocked; by default all signals are blocked. The implementation is left as an exercise to the reader.

  8. Another use for this feature is the Decimal context. Here’s a simple example, after one posted by Michael Chermside:
    import decimal
    def extra_precision(places=2):
        c = decimal.getcontext()
        saved_prec = c.prec
        c.prec += places
            yield None
            c.prec = saved_prec

    Sample usage (adapted from the Python Library Reference):

    def sin(x):
        "Return the sine of x as measured in radians."
        with extra_precision():
            i, lasts, s, fact, num, sign = 1, 0, x, 1, x, 1
            while s != lasts:
                lasts = s
                i += 2
                fact *= i * (i-1)
                num *= x * x
                sign *= -1
                s += num / fact * sign
        # The "+s" rounds back to the original precision,
        # so this must be outside the with-statement:
        return +s
  9. Here’s a simple context manager for the decimal module:
    def localcontext(ctx=None):
        """Set a new local decimal context for the block"""
        # Default to using the current context
        if ctx is None:
            ctx = getcontext()
        # We set the thread context to a copy of this context
        # to ensure that changes within the block are kept
        # local to the block.
        newctx = ctx.copy()
        oldctx = decimal.getcontext()
            yield newctx
            # Always restore the original context

    Sample usage:

    from decimal import localcontext, ExtendedContext
    def sin(x):
        with localcontext() as ctx:
            ctx.prec += 2
            # Rest of sin calculation algorithm
            # uses a precision 2 greater than normal
        return +s # Convert result to normal precision
    def sin(x):
        with localcontext(ExtendedContext):
            # Rest of sin calculation algorithm
            # uses the Extended Context from the
            # General Decimal Arithmetic Specification
        return +s # Convert result to normal context
  10. A generic “object-closing” context manager:
    class closing(object):
        def __init__(self, obj):
            self.obj = obj
        def __enter__(self):
            return self.obj
        def __exit__(self, *exc_info):
                close_it = self.obj.close
            except AttributeError:

    This can be used to deterministically close anything with a close method, be it file, generator, or something else. It can even be used when the object isn’t guaranteed to require closing (e.g., a function that accepts an arbitrary iterable):

    # emulate opening():
    with closing(open("argument.txt")) as contradiction:
       for line in contradiction:
           print line
    # deterministically finalize an iterator:
    with closing(iter(data_source)) as data:
       for datum in data:

    (Python 2.5’s contextlib module contains a version of this context manager)

  11. PEP 319 gives a use case for also having a released() context to temporarily release a previously acquired lock; this can be written very similarly to the locked context manager above by swapping the acquire() and release() calls:
    class released:
      def __init__(self, lock):
          self.lock = lock
      def __enter__(self):
      def __exit__(self, type, value, tb):

    Sample usage:

    with my_lock:
        # Operations with the lock held
        with released(my_lock):
            # Operations without the lock
            # e.g. blocking I/O
        # Lock is held again here
  12. A “nested” context manager that automatically nests the supplied contexts from left-to-right to avoid excessive indentation:
    def nested(*contexts):
        exits = []
        vars = []
                for context in contexts:
                    exit = context.__exit__
                    enter = context.__enter__
                yield vars
                exc = sys.exc_info()
                exc = (None, None, None)
            while exits:
                exit = exits.pop()
                    exc = sys.exc_info()
                    exc = (None, None, None)
            if exc != (None, None, None):
                # sys.exc_info() may have been
                # changed by one of the exit methods
                # so provide explicit exception info
                raise exc[0], exc[1], exc[2]

    Sample usage:

    with nested(a, b, c) as (x, y, z):
        # Perform operation

    Is equivalent to:

    with a as x:
        with b as y:
            with c as z:
                # Perform operation

    (Python 2.5’s contextlib module contains a version of this context manager)

Reference Implementation

This PEP was first accepted by Guido at his EuroPython keynote, 27 June 2005. It was accepted again later, with the __context__ method added. The PEP was implemented in Subversion for Python 2.5a1 The __context__() method was removed in Python 2.5b1


Many people contributed to the ideas and concepts in this PEP, including all those mentioned in the acknowledgements for PEP 340 and PEP 346.

Additional thanks goes to (in no meaningful order): Paul Moore, Phillip J. Eby, Greg Ewing, Jason Orendorff, Michael Hudson, Raymond Hettinger, Walter Dörwald, Aahz, Georg Brandl, Terry Reedy, A.M. Kuchling, Brett Cannon, and all those that participated in the discussions on python-dev.



Last modified: 2023-10-11 12:05:51 GMT