[Python-Dev] accumulator display syntax

Raymond Hettinger python at rcn.com
Wed Oct 22 01:19:58 EDT 2003

> > If there is any doubt on that score, I would be happy to update
> > the PEP to match the current proposal for iterator expressions
> > and solicit more community feedback.

> Wonderful!  Rename PEP 289 to "generator expressions" and change the
> contents to match this proposal.  Thanks for being the fall guy!

Here is a rough draft on the resurrected PEP.
I'm sure it contains many flaws and I welcome suggested amendments.
In particular, the follow needs attention:

* Precise specification of the syntax including the edge cases
  with commas where enclosing parentheses are required.

* Making sure the acknowledgements are correct and complete.

* Verifying my understanding of the issues surrounding late binding,
  modification of locals, and returning generator expressions.

* Clear articulation of the expected benefits.  There are so many,
  it was difficult to keep it focused.

Raymond Hettinger


PEP: 289
Title: Generator Expressions
Version: $Revision: 1.2 $
Last-Modified: $Date: 2003/08/30 23:57:36 $
Author: python at rcn.com (Raymond D. Hettinger)
Status: Active
Type: Standards Track
Created: 30-Jan-2002
Python-Version: 2.3
Post-History: 22-Oct-2003


    This PEP introduces generator expressions as a high performance,
    memory efficient generalization of list expressions and


    Experience with list expressions has shown their wide-spread
    utility throughout Python.  However, many of the use cases do
    not need to have a full list created in memory.  Instead, they
    only need to iterate over the elements one at a time.

    For instance, the following dictionary constructor code will
    build a full item list in memory, iterate over that item list,
    and, when the reference is no longer needed, delete the list:

        d = dict([(k, func(v)) for k in keylist])

    Time, clarity, and memory are conserved by using an generator
    expession instead:

        d = dict((k, func(v)) for k in keylist)

    Similar benefits are conferred on the constructors for other
    container objects:

        s = Set(word  for line in page  for word in line.split())

    Having a syntax similar to list comprehensions makes it easy to
    to an iterator expression when scaling up application.

    Generator expressions are especially useful in functions that reduce
    an iterable input to a single value:

        sum(len(line) for line.strip() in file if len(line)>5)

    Accordingly, generator expressions are expected to partially
    the need for reduce() which is notorious for its lack of clarity.
    there are additional speed and clarity benefits from writing
    directly instead of using lambda.

    List expressions greatly reduced the need for filter() and map().
    Likewise, generator expressions are expected to minimize the need
    for itertools.ifilter() and itertools.imap().  In contrast, the
    utility of other itertools will be enhanced by generator

        dotproduct = sum(x*y for x,y in itertools.izip(x_vector,

BDFL Pronouncements

    The previous version of this PEP was REJECTED.  The bracketed
    yield syntax left something to be desired; the performance gains had
    not been demonstrated; and the range of use cases had not been
    shown.  After, much discussion on the python-dev list, the PEP has
    been resurrected its present form.  The impetus for the discussion
    was an innovative proposal from Peter Norvig.

The Gory Details

    1) In order to achieve a performance gain, generator expressions
    to be run in the local stackframe; otherwise, the improvement in
    cache performance gets offset by the time spent switching
    stackframes.  The upshot of this is that generator expressions need
    to be both created and consumed within the context of a single
    stackframe.  Accordingly, the generator expression cannot be
    to another function:

        return (k, func(v)) for k in keylist

    2) The loop variable is not exposed to the surrounding function.
    This both facilates the implementation and makes typical use cases
    more reliable.  In some future version of Python, list
    will also hide the induction variable from the surrounding code
    in Py2.4, warnings will be issued for code accessing the induction
    3) Variables references in the generator expressions will exhibit
    binding just like other Python code.  In the following example, the
    iterator runs *after* the value of y is set to one:

        def h():
            y = 0
            l = [1,2]
            def gen(S):
                for x in S:
                    yield x+y
            it = gen(l)
            y = 1
            for v in it:
              print v

    4) List comprehensions will remain unchanged.
    So, [x for x in S] is a list comprehension and
    [(x for x in S)] is a list containing one generator expression.

    5) It is prohibited to use locals() for other than read-only use
    in generator expressions.  This simplifies the implementation and
    precludes a certain class of obfuscated code.


    Peter Norvig resurrected the discussion proposal for "accumulation

    Alex Martelli provided critical measurements that proved the
    the performance benefits of generator expressions.

    Samuele Pedroni provided the example of late binding.

    Guido van Rossum suggested the bracket free, yield free syntax.

    Raymond Hettinger first proposed "generator comprehensions" in
    January 2002.


    [1] PEP 255 Simple Generators

    [2] PEP 202 List Comprehensions

    [3] Peter Norvig's Accumulation Display Proposal


    This document has been placed in the public domain.

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