skip to navigation
skip to content

Python Wiki

Python Insider Blog

Python 2 or 3?

Help Fund Python

[Python resources in languages other than English]

Non-English Resources

Add an event to this calendar.

Times are shown in UTC/GMT.

Add an event to this calendar.

PEP: 255
Title: Simple Generators
Version: e2b5d1a8a663
Last-Modified:  2009-01-18 09:50:42 +0000 (Sun, 18 Jan 2009)
Author: Neil Schemenauer <nas at arctrix.com>, Tim Peters <tim at zope.com>, Magnus Lie Hetland <magnus at hetland.org>
Discussions-To:  <python-iterators at lists.sourceforge.net>
Status: Final
Type: Standards Track
Requires: 234
Created: 18-May-2001
Python-Version: 2.2
Post-History: 14-Jun-2001, 23-Jun-2001

Abstract

    This PEP introduces the concept of generators to Python, as well
    as a new statement used in conjunction with them, the "yield"
    statement.


Motivation

    When a producer function has a hard enough job that it requires
    maintaining state between values produced, most programming languages
    offer no pleasant and efficient solution beyond adding a callback
    function to the producer's argument list, to be called with each value
    produced.

    For example, tokenize.py in the standard library takes this approach:
    the caller must pass a "tokeneater" function to tokenize(), called
    whenever tokenize() finds the next token.  This allows tokenize to be
    coded in a natural way, but programs calling tokenize are typically
    convoluted by the need to remember between callbacks which token(s)
    were seen last.  The tokeneater function in tabnanny.py is a good
    example of that, maintaining a state machine in global variables, to
    remember across callbacks what it has already seen and what it hopes to
    see next.  This was difficult to get working correctly, and is still
    difficult for people to understand.  Unfortunately, that's typical of
    this approach.

    An alternative would have been for tokenize to produce an entire parse
    of the Python program at once, in a large list.  Then tokenize clients
    could be written in a natural way, using local variables and local
    control flow (such as loops and nested if statements) to keep track of
    their state.  But this isn't practical:  programs can be very large, so
    no a priori bound can be placed on the memory needed to materialize the
    whole parse; and some tokenize clients only want to see whether
    something specific appears early in the program (e.g., a future
    statement, or, as is done in IDLE, just the first indented statement),
    and then parsing the whole program first is a severe waste of time.

    Another alternative would be to make tokenize an iterator[1],
    delivering the next token whenever its .next() method is invoked.  This
    is pleasant for the caller in the same way a large list of results
    would be, but without the memory and "what if I want to get out early?"
    drawbacks.  However, this shifts the burden on tokenize to remember
    *its* state between .next() invocations, and the reader need only
    glance at tokenize.tokenize_loop() to realize what a horrid chore that
    would be.  Or picture a recursive algorithm for producing the nodes of
    a general tree structure:  to cast that into an iterator framework
    requires removing the recursion manually and maintaining the state of
    the traversal by hand.

    A fourth option is to run the producer and consumer in separate
    threads.  This allows both to maintain their states in natural ways,
    and so is pleasant for both.  Indeed, Demo/threads/Generator.py in the
    Python source distribution provides a usable synchronized-communication
    class for doing that in a general way.  This doesn't work on platforms
    without threads, though, and is very slow on platforms that do
    (compared to what is achievable without threads).

    A final option is to use the Stackless[2][3] variant implementation of
    Python instead, which supports lightweight coroutines.  This has much
    the same programmatic benefits as the thread option, but is much more
    efficient.  However, Stackless is a controversial rethinking of the
    Python core, and it may not be possible for Jython to implement the
    same semantics.  This PEP isn't the place to debate that, so suffice it
    to say here that generators provide a useful subset of Stackless
    functionality in a way that fits easily into the current CPython
    implementation, and is believed to be relatively straightforward for
    other Python implementations.

    That exhausts the current alternatives.  Some other high-level
    languages provide pleasant solutions, notably iterators in Sather[4],
    which were inspired by iterators in CLU; and generators in Icon[5], a
    novel language where every expression "is a generator".  There are
    differences among these, but the basic idea is the same:  provide a
    kind of function that can return an intermediate result ("the next
    value") to its caller, but maintaining the function's local state so
    that the function can be resumed again right where it left off.  A
    very simple example:

        def fib():
            a, b = 0, 1
            while 1:
                yield b
                a, b = b, a+b

    When fib() is first invoked, it sets a to 0 and b to 1, then yields b
    back to its caller.  The caller sees 1.  When fib is resumed, from its
    point of view the yield statement is really the same as, say, a print
    statement:  fib continues after the yield with all local state intact.
    a and b then become 1 and 1, and fib loops back to the yield, yielding
    1 to its invoker.  And so on.  From fib's point of view it's just
    delivering a sequence of results, as if via callback.  But from its
    caller's point of view, the fib invocation is an iterable object that
    can be resumed at will.  As in the thread approach, this allows both
    sides to be coded in the most natural ways; but unlike the thread
    approach, this can be done efficiently and on all platforms.  Indeed,
    resuming a generator should be no more expensive than a function call.

    The same kind of approach applies to many producer/consumer functions.
    For example, tokenize.py could yield the next token instead of invoking
    a callback function with it as argument, and tokenize clients could
    iterate over the tokens in a natural way:  a Python generator is a kind
    of Python iterator[1], but of an especially powerful kind.


Specification: Yield

    A new statement is introduced:

        yield_stmt:    "yield" expression_list

    "yield" is a new keyword, so a future statement[8] is needed to phase
    this in:  in the initial release, a module desiring to use generators
    must include the line

        from __future__ import generators

    near the top (see PEP 236[8]) for details).  Modules using the
    identifier "yield" without a future statement will trigger warnings.
    In the following release, yield will be a language keyword and the
    future statement will no longer be needed.

    The yield statement may only be used inside functions.  A function that
    contains a yield statement is called a generator function.  A generator
    function is an ordinary function object in all respects, but has the
    new CO_GENERATOR flag set in the code object's co_flags member.

    When a generator function is called, the actual arguments are bound to
    function-local formal argument names in the usual way, but no code in
    the body of the function is executed.  Instead a generator-iterator
    object is returned; this conforms to the iterator protocol[6], so in
    particular can be used in for-loops in a natural way.  Note that when
    the intent is clear from context, the unqualified name "generator" may
    be used to refer either to a generator-function or a generator-
    iterator.

    Each time the .next() method of a generator-iterator is invoked, the
    code in the body of the generator-function is executed until a yield
    or return statement (see below) is encountered, or until the end of
    the body is reached.

    If a yield statement is encountered, the state of the function is
    frozen, and the value of expression_list is returned to .next()'s
    caller.  By "frozen" we mean that all local state is retained,
    including the current bindings of local variables, the instruction
    pointer, and the internal evaluation stack:  enough information is
    saved so that the next time .next() is invoked, the function can
    proceed exactly as if the yield statement were just another external
    call.

    Restriction:  A yield statement is not allowed in the try clause of a
    try/finally construct.  The difficulty is that there's no guarantee
    the generator will ever be resumed, hence no guarantee that the finally
    block will ever get executed; that's too much a violation of finally's
    purpose to bear.

    Restriction:  A generator cannot be resumed while it is actively
    running:

        >>> def g():
        ...     i = me.next()
        ...     yield i
        >>> me = g()
        >>> me.next()
        Traceback (most recent call last):
         ...
          File "<string>", line 2, in g
        ValueError: generator already executing


Specification: Return

    A generator function can also contain return statements of the form:

        "return"

    Note that an expression_list is not allowed on return statements
    in the body of a generator (although, of course, they may appear in
    the bodies of non-generator functions nested within the generator).

    When a return statement is encountered, control proceeds as in any
    function return, executing the appropriate finally clauses (if any
    exist).  Then a StopIteration exception is raised, signalling that the
    iterator is exhausted.   A StopIteration exception is also raised if
    control flows off the end of the generator without an explict return.

    Note that return means "I'm done, and have nothing interesting to
    return", for both generator functions and non-generator functions.

    Note that return isn't always equivalent to raising StopIteration:  the
    difference lies in how enclosing try/except constructs are treated.
    For example,

        >>> def f1():
        ...     try:
        ...         return
        ...     except:
        ...        yield 1
        >>> print list(f1())
        []

    because, as in any function, return simply exits, but

        >>> def f2():
        ...     try:
        ...         raise StopIteration
        ...     except:
        ...         yield 42
        >>> print list(f2())
        [42]

    because StopIteration is captured by a bare "except", as is any
    exception.


Specification: Generators and Exception Propagation

    If an unhandled exception-- including, but not limited to,
    StopIteration --is raised by, or passes through, a generator function,
    then the exception is passed on to the caller in the usual way, and
    subsequent attempts to resume the generator function raise
    StopIteration.  In other words, an unhandled exception terminates a
    generator's useful life.

    Example (not idiomatic but to illustrate the point):

    >>> def f():
    ...     return 1/0
    >>> def g():
    ...     yield f()  # the zero division exception propagates
    ...     yield 42   # and we'll never get here
    >>> k = g()
    >>> k.next()
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
      File "<stdin>", line 2, in g
      File "<stdin>", line 2, in f
    ZeroDivisionError: integer division or modulo by zero
    >>> k.next()  # and the generator cannot be resumed
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    StopIteration
    >>>


Specification: Try/Except/Finally

    As noted earlier, yield is not allowed in the try clause of a try/
    finally construct.  A consequence is that generators should allocate
    critical resources with great care.  There is no restriction on yield
    otherwise appearing in finally clauses, except clauses, or in the try
    clause of a try/except construct:

    >>> def f():
    ...     try:
    ...         yield 1
    ...         try:
    ...             yield 2
    ...             1/0
    ...             yield 3  # never get here
    ...         except ZeroDivisionError:
    ...             yield 4
    ...             yield 5
    ...             raise
    ...         except:
    ...             yield 6
    ...         yield 7     # the "raise" above stops this
    ...     except:
    ...         yield 8
    ...     yield 9
    ...     try:
    ...         x = 12
    ...     finally:
    ...         yield 10
    ...     yield 11
    >>> print list(f())
    [1, 2, 4, 5, 8, 9, 10, 11]
    >>>


Example

        # A binary tree class.
        class Tree:

            def __init__(self, label, left=None, right=None):
                self.label = label
                self.left = left
                self.right = right

            def __repr__(self, level=0, indent="    "):
                s = level*indent + `self.label`
                if self.left:
                    s = s + "\n" + self.left.__repr__(level+1, indent)
                if self.right:
                    s = s + "\n" + self.right.__repr__(level+1, indent)
                return s

            def __iter__(self):
                return inorder(self)

        # Create a Tree from a list.
        def tree(list):
            n = len(list)
            if n == 0:
                return []
            i = n / 2
            return Tree(list[i], tree(list[:i]), tree(list[i+1:]))

        # A recursive generator that generates Tree labels in in-order.
        def inorder(t):
            if t:
                for x in inorder(t.left):
                    yield x
                yield t.label
                for x in inorder(t.right):
                    yield x

        # Show it off: create a tree.
        t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
        # Print the nodes of the tree in in-order.
        for x in t:
            print x,
        print

        # A non-recursive generator.
        def inorder(node):
            stack = []
            while node:
                while node.left:
                    stack.append(node)
                    node = node.left
                yield node.label
                while not node.right:
                    try:
                        node = stack.pop()
                    except IndexError:
                        return
                    yield node.label
                node = node.right

        # Exercise the non-recursive generator.
        for x in t:
            print x,
        print

    Both output blocks display:

        A B C D E F G H I J K L M N O P Q R S T U V W X Y Z


Q & A

    Q. Why not a new keyword instead of reusing "def"?

    A. See BDFL Pronouncements section below.

    Q. Why a new keyword for "yield"?  Why not a builtin function instead?

    A. Control flow is much better expressed via keyword in Python, and
       yield is a control construct.  It's also believed that efficient
       implementation in Jython requires that the compiler be able to
       determine potential suspension points at compile-time, and a new
       keyword makes that easy.  The CPython referrence implementation also
       exploits it heavily, to detect which functions *are* generator-
       functions (although a new keyword in place of "def" would solve that
       for CPython -- but people asking the "why a new keyword?" question
       don't want any new keyword).

    Q: Then why not some other special syntax without a new keyword?  For
       example, one of these instead of "yield 3":

       return 3 and continue
       return and continue 3
       return generating 3
       continue return 3
       return >> , 3
       from generator return 3
       return >> 3
       return << 3
       >> 3
       << 3
       * 3

    A: Did I miss one <wink>?  Out of hundreds of messages, I counted three
       suggesting such an alternative, and extracted the above from them.
       It would be nice not to need a new keyword, but nicer to make yield
       very clear -- I don't want to have to *deduce* that a yield is
       occurring from making sense of a previously senseless sequence of
       keywords or operators.  Still, if this attracts enough interest,
       proponents should settle on a single consensus suggestion, and Guido
       will Pronounce on it.

    Q. Why allow "return" at all?  Why not force termination to be spelled
       "raise StopIteration"?

    A. The mechanics of StopIteration are low-level details, much like the
       mechanics of IndexError in Python 2.1:  the implementation needs to
       do *something* well-defined under the covers, and Python exposes
       these mechanisms for advanced users.  That's not an argument for
       forcing everyone to work at that level, though.  "return" means "I'm
       done" in any kind of function, and that's easy to explain and to use.
       Note that "return" isn't always equivalent to "raise StopIteration"
       in try/except construct, either (see the "Specification: Return"
       section).

    Q. Then why not allow an expression on "return" too?

    A. Perhaps we will someday.  In Icon, "return expr" means both "I'm
       done", and "but I have one final useful value to return too, and
       this is it".  At the start, and in the absence of compelling uses
       for "return expr", it's simply cleaner to use "yield" exclusively
       for delivering values.


BDFL Pronouncements

    Issue:  Introduce another new keyword (say, "gen" or "generator") in
    place of "def", or otherwise alter the syntax, to distinguish
    generator-functions from non-generator functions.

    Con:  In practice (how you think about them), generators *are*
    functions, but with the twist that they're resumable.  The mechanics of
    how they're set up is a comparatively minor technical issue, and
    introducing a new keyword would unhelpfully overemphasize the
    mechanics of how generators get started (a vital but tiny part of a
    generator's life).

    Pro:  In reality (how you think about them), generator-functions are
    actually factory functions that produce generator-iterators as if by
    magic.  In this respect they're radically different from non-generator
    functions, acting more like a constructor than a function, so reusing
    "def" is at best confusing.  A "yield" statement buried in the body is
    not enough warning that the semantics are so different.

    BDFL:  "def" it stays.  No argument on either side is totally
    convincing, so I have consulted my language designer's intuition.  It
    tells me that the syntax proposed in the PEP is exactly right - not too
    hot, not too cold.  But, like the Oracle at Delphi in Greek mythology,
    it doesn't tell me why, so I don't have a rebuttal for the arguments
    against the PEP syntax.  The best I can come up with (apart from
    agreeing with the rebuttals ... already made) is "FUD".  If this had
    been part of the language from day one, I very much doubt it would have
    made Andrew Kuchling's "Python Warts" page.


Reference Implementation

    The current implementation, in a preliminary state (no docs, but well
    tested and solid), is part of Python's CVS development tree[9].  Using
    this requires that you build Python from source.

    This was derived from an earlier patch by Neil Schemenauer[7].


Footnotes and References

    [1] PEP 234, Iterators, Yee, Van Rossum
        http://www.python.org/dev/peps/pep-0234/

    [2] http://www.stackless.com/

    [3] PEP 219, Stackless Python, McMillan
        http://www.python.org/dev/peps/pep-0219/

    [4] "Iteration Abstraction in Sather"
        Murer , Omohundro, Stoutamire and Szyperski
        http://www.icsi.berkeley.edu/~sather/Publications/toplas.html

    [5] http://www.cs.arizona.edu/icon/

    [6] The concept of iterators is described in PEP 234.  See [1] above.

    [7] http://python.ca/nas/python/generator.diff

    [8] PEP 236, Back to the __future__, Peters
        http://www.python.org/dev/peps/pep-0236/

    [9] To experiment with this implementation, check out Python from CVS
        according to the instructions at
            http://sf.net/cvs/?group_id=5470
        Note that the std test Lib/test/test_generators.py contains many
        examples, including all those in this PEP.


Copyright

    This document has been placed in the public domain.