[Raymond]
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.
[Guido]
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@rcn.com (Raymond D. Hettinger) Status: Active Type: Standards Track Created: 30-Jan-2002 Python-Version: 2.3 Post-History: 22-Oct-2003 Abstract This PEP introduces generator expressions as a high performance, memory efficient generalization of list expressions and generators. Rationale 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 switch 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 eliminate the need for reduce() which is notorious for its lack of clarity. And, there are additional speed and clarity benefits from writing expressions 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 expressions: dotproduct = sum(x*y for x,y in itertools.izip(x_vector, y_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 need 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 returned 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 comprehensions will also hide the induction variable from the surrounding code (and, in Py2.4, warnings will be issued for code accessing the induction variable). 3) Variables references in the generator expressions will exhibit late 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. Acknowledgements: Peter Norvig resurrected the discussion proposal for "accumulation displays". 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. References [1] PEP 255 Simple Generators http://python.sourceforge.net/peps/pep-0255.html [2] PEP 202 List Comprehensions http://python.sourceforge.net/peps/pep-0202.html [3] Peter Norvig's Accumulation Display Proposal http:///www.norvig.com/pyacc.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil fill-column: 70 End: