PEP: 289 Title: Generator Comprehensions Version: $Revision$ Last-Modified: $Date$ Author: python@rcn.com (Raymond D. Hettinger) Status: Rejected Type: Standards Track Created: 30-Jan-2002 Python-Version: 2.3 Post-History: Abstract This PEP introduces generator comprehensions as an idea for enhancing the generators introduced in Python version 2.2 [1]. The goal is to increase the convenience, utility, and power of generators by making it easy to convert a list comprehension into a generator. Rationale Python 2.2 introduced the concept of an iterable interface as proposed in PEP 234 [4]. The iter() factory function was provided as common calling convention and deep changes were made to use iterators as a unifying theme throughout Python. The unification came in the form of establishing a common iterable interface for mappings, sequences, and file objects. Generators, as proposed in PEP 255 [1], were introduced as a means for making it easier to create iterators, especially ones with complex internal execution or variable states. When I created new programs, generators were often the tool of choice for creating an iterator. However, when updating existing programs, I found that the tool had another use, one that improved program function as well as structure. Some programs exhibited a pattern of creating large lists and then looping over them. As data sizes increased, the programs encountered scalability limitations owing to excessive memory consumption (and malloc time) for the intermediate lists. Generators were found to be directly substitutable for the lists while eliminating the memory issues through lazy evaluation a.k.a. just in time manufacturing. Python itself encountered similar issues. As a result, xrange() and xreadlines() were introduced. And, in the case of file objects and mappings, just-in-time evaluation became the norm. Generators provide a tool to program memory conserving for-loops whenever complete evaluation is not desired because of memory restrictions or availability of data. The next step in the evolution of generators is to establish a generator alternative to list comprehensions [3]. This alternative provides a simple way to convert a list comprehension into a generator whenever memory issues arise. This suggestion is designed to take advantage of the existing implementation and require little additional effort to incorporate. It is backward compatible and requires no new keywords. BDFL Pronouncements Generator comprehensions are REJECTED. The rationale is that the benefits are marginal since generators can already be coded directly and the costs are high because implementation and maintenance require major efforts with the parser. Reference Implementation There is not currently a CPython implementation; however, a simulation module written in pure Python is available on SourceForge [5]. The simulation is meant to allow direct experimentation with the proposal. There is also a module [6] with working source code for all of the examples used in this PEP. It serves as a test suite for the simulator and it documents how each the new feature works in practice. The authors and implementers of PEP 255 [1] were contacted to provide their assessment of whether these enhancements were going to be straight-forward to implement and require only minor modification of the existing generator code. Neil felt the assertion was correct. Ka-Ping thought so also. GvR said he could believe that it was true. Later GvR re-assessed and thought that it would be difficult to tweak the code generator to produce a separate object. Tim did not have an opportunity to give an assessment. Specification for Generator Comprehensions : If a list comprehension starts with a 'yield' keyword, then express the comprehension with a generator. For example: g = [yield (len(line),line) for line in file if len(line)>5] This would be implemented as if it had been written: def __temp(self): for line in file: if len(line) > 5: yield (len(line), line) g = __temp() Note A: There is some discussion about whether the enclosing brackets should be part of the syntax for generator comprehensions. On the plus side, it neatly parallels list comprehensions and would be immediately recognizable as a similar form with similar internal syntax (taking maximum advantage of what people already know). More importantly, it sets off the generator comprehension from the rest of the function so as to not suggest that the enclosing function is a generator (currently the only cue that a function is really a generator is the presence of the yield keyword). On the minus side, the brackets may falsely suggest that the whole expression returns a list. Most of the feedback received to date indicates that brackets are helpful and not misleading. Unfortunately, the one dissent is from GvR. A key advantage of the generator comprehension syntax is that it makes it trivially easy to transform existing list comprehension code to a generator by adding yield. Likewise, it can be converted back to a list by deleting yield. This makes it easy to scale-up programs from small datasets to ones large enough to warrant just in time evaluation. Note B: List comprehensions expose their looping variable and leave that variable in the enclosing scope. The code, [str(i) for i in range(8)] leaves 'i' set to 7 in the scope where the comprehension appears. This behavior is by design and reflects an intent to duplicate the result of coding a for-loop instead of a list comprehension. Further, the variable 'i' is in a defined and potentially useful state on the line immediately following the list comprehension. In contrast, generator comprehensions do not expose the looping variable to the enclosing scope. The code, [yield str(i) for i in range(8)] leaves 'i' untouched in the scope where the comprehension appears. This is also by design and reflects an intent to duplicate the result of coding a generator directly instead of a generator comprehension. Further, the variable 'i' is not in a defined state on the line immediately following the list comprehension. It does not come into existence until iteration starts (possibly never). Comments from GvR: Cute hack, but I think the use of the [] syntax strongly suggests that it would return a list, not an iterator. I also think that this is trying to turn Python into a functional language, where most algorithms use lazy infinite sequences, and I just don't think that's where its future lies. I don't think it's worth the trouble. I expect it will take a lot of work to hack it into the code generator: it has to create a separate code object in order to be a generator. List comprehensions are inlined, so I expect that the generator comprehension code generator can't share much with the list comprehension code generator. And this for something that's not that common and easily done by writing a 2-line helper function. IOW the ROI isn't high enough. Comments from Ka-Ping Yee: I am very happy with the things you have proposed in this PEP. I feel quite positive about generator comprehensions and have no reservations. So a +1 on that. Comments from Neil Schemenauer: I'm -0 on the generator list comprehensions. They don't seem to add much. You could easily use a nested generator to do the same thing. They smell like lambda. Comments from Magnus Lie Hetland: Generator comprehensions seem mildly useful, but I vote +0. Defining a separate, named generator would probably be my preference. On the other hand, I do see the advantage of "scaling up" from list comprehensions. Comments from the Community: The response to the generator comprehension proposal has been mostly favorable. There were some 0 votes from people who didn't see a real need or who were not energized by the idea. Some of the 0 votes were tempered by comments that the reviewer did not even like list comprehensions or did not have any use for generators in any form. The +1 votes outnumbered the 0 votes by about two to one. Author response: I've studied several syntactical variations and concluded that the brackets are essential for: - teachability (it's like a list comprehension) - set-off (yield applies to the comprehension not the enclosing function) - substitutability (list comprehensions can be made lazy just by adding yield) What I like best about generator comprehensions is that I can design using list comprehensions and then easily switch to a generator (by adding yield) in response to scalability requirements (when the list comprehension produces too large of an intermediate result). Had generators already been in-place when list comprehensions were accepted, the yield option might have been incorporated from the start. For certain, the mathematical style notation is explicit and readable as compared to a separate function definition with an embedded yield. References [1] PEP 255 Simple Generators http://python.sourceforge.net/peps/pep-0255.html [2] PEP 212 Loop Counter Iteration http://python.sourceforge.net/peps/pep-0212.html [3] PEP 202 List Comprehensions http://python.sourceforge.net/peps/pep-0202.html [4] PEP 234 Iterators http://python.sourceforge.net/peps/pep-0234.html [5] A pure Python simulation of every feature in this PEP is at: http://sourceforge.net/tracker/download.php?group_id=5470&atid=305470&file_id=17348&aid=513752 [6] The full, working source code for each of the examples in this PEP along with other examples and tests is at: http://sourceforge.net/tracker/download.php?group_id=5470&atid=305470&file_id=17412&aid=513756 [7] Another partial implementation is at: http://www.python.org/sf/795947 Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil fill-column: 70 End: