PEP 255: Simple Generators
Thu, 14 Jun 2001 12:49:07 -0400
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Title: Simple Generators
Version: $Revision: 1.3 $
Author: firstname.lastname@example.org (Neil Schemenauer),
email@example.com (Tim Peters),
firstname.lastname@example.org (Magnus Lie Hetland)
Type: Standards Track
This PEP introduces the concept of generators to Python, as well
as a new statement used in conjunction with them, the "yield"
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
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
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,
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 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,
which were inspired by iterators in CLU; and generators in Icon, 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:
a, b = 0, 1
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, but of an especially powerful kind.
A new statement is introduced:
yield_stmt: "yield" expression_list
"yield" is a new keyword, so a future statement is needed to phase
this in. [XXX spell this out]
The yield statement may only be used inside functions. A function that
contains a yield statement is called a generator function.
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, 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-
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
A generator function can also contain return statements of the form:
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, nothing is returned, but a
StopIteration exception is raised, signalling that the iterator is
exhausted. The same is true if control flows off the end of the
function. Note that return means "I'm done, and have nothing
interesting to return", for both generator functions and non-generator
# A binary tree class.
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`
s = s + "\n" + self.left.__repr__(level+1, indent)
s = s + "\n" + self.right.__repr__(level+1, indent)
# Create a Tree from a list.
n = len(list)
if n == 0:
i = n / 2
return Tree(list[i], tree(list[:i]), tree(list[i+1:]))
# A recursive generator that generates Tree leaves in in-order.
for x in inorder(t.left):
for x in inorder(t.right):
# Show it off: create a tree.
t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Print the nodes of the tree in in-order.
for x in t:
# A non-recursive generator.
stack = 
node = node.left
while not node.right:
node = stack.pop()
node = node.right
# Exercise the non-recursive generator.
for x in t:
Q & A
Q. Why a new keyword? 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.
A preliminary patch against the CVS Python source is available.
Footnotes and References
 PEP 234, http://python.sf.net/peps/pep-0234.html
 PEP 219, http://python.sf.net/peps/pep-0219.html
 "Iteration Abstraction in Sather"
Murer , Omohundro, Stoutamire and Szyperski
 The concept of iterators is described in PEP 234
This document has been placed in the public domain.