1. Carl Meyer
  2. python-peps


python-peps / pep-0289.txt

PEP: 289
Title: Generator Expressions
Version: $Revision$
Last-Modified: $Date$
Author: python@rcn.com (Raymond Hettinger)
Status: Final
Type: Standards Track
Content-Type: text/x-rst
Created: 30-Jan-2002
Python-Version: 2.4
Post-History: 22-Oct-2003


This PEP introduces generator expressions as a high performance,
memory efficient generalization of list comprehensions [1]_ and
generators [2]_.


Experience with list comprehensions 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 summation code will build a full list of
squares in memory, iterate over those values, and, when the reference
is no longer needed, delete the list::

    sum([x*x for x in range(10)])

Memory is conserved by using a generator expression instead::

    sum(x*x for x in range(10))

Similar benefits are conferred on constructors for container objects::

    s = Set(word  for line in page  for word in line.split())
    d = dict( (k, func(k)) for k in keylist)

Generator expressions are especially useful with functions like sum(),
min(), and max() that reduce an iterable input to a single value::

    max(len(line)  for line in file  if line.strip())

Generator expressions also address some examples of functionals coded
with lambda::

    reduce(lambda s, a: s + a.myattr, data, 0)
    reduce(lambda s, a: s + a[3], data, 0)

These simplify to::

    sum(a.myattr for a in data)
    sum(a[3] for a in data)

List comprehensions 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))

Having a syntax similar to list comprehensions also makes it easy to
convert existing code into an generator expression when scaling up

Early timings showed that generators had a significant performance
advantage over list comprehensions.  However, the latter were highly
optimized for Py2.4 and now the performance is roughly comparable
for small to mid-sized data sets.  As the data volumes grow larger,
generator expressions tend to perform better because they do not
exhaust cache memory and they allow Python to re-use objects between

BDFL Pronouncements

This PEP is ACCEPTED for Py2.4.

The Details

(None of this is exact enough in the eye of a reader from Mars, but I
hope the examples convey the intention well enough for a discussion in
c.l.py.  The Python Reference Manual should contain a 100% exact
semantic and syntactic specification.)

1. The semantics of a generator expression are equivalent to creating
   an anonymous generator function and calling it.  For example::

       g = (x**2 for x in range(10))
       print g.next()

   is equivalent to::

       def __gen(exp):
           for x in exp:
               yield x**2
       g = __gen(iter(range(10)))
       print g.next()

   Only the outermost for-expression is evaluated immediately, the other
   expressions are deferred until the generator is run::

       g = (tgtexp  for var1 in exp1 if exp2 for var2 in exp3 if exp4)

   is equivalent to::

    def __gen(bound_exp):
        for var1 in bound_exp:
            if exp2:
                for var2 in exp3:
                    if exp4:
                        yield tgtexp
    g = __gen(iter(exp1))
    del __gen

2. The syntax requires that a generator expression always needs to be
   directly inside a set of parentheses and cannot have a comma on
   either side.  With reference to the file Grammar/Grammar in CVS,
   two rules change:

   a) The rule::

         atom: '(' [testlist] ')'

      changes to::

         atom: '(' [testlist_gexp] ')'
      where testlist_gexp is almost the same as listmaker, but only
      allows a single test after 'for' ... 'in'::

         testlist_gexp: test ( gen_for | (',' test)* [','] )          

   b)  The rule for arglist needs similar changes.

   This means that you can write::

       sum(x**2 for x in range(10))

   but you would have to write::

       reduce(operator.add, (x**2 for x in range(10)))

   and also::

       g = (x**2 for x in range(10))

   i.e. if a function call has a single positional argument, it can be
   a generator expression without extra parentheses, but in all other
   cases you have to parenthesize it.

   The exact details were checked in to Grammar/Grammar version 1.49.

3. The loop variable (if it is a simple variable or a tuple of simple
   variables) is not exposed to the surrounding function.  This
   facilitates 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).

   For example::

       x = "hello"
       y = list(x for x in "abc")
       print x    # prints "hello", not "c"

4. List comprehensions will remain unchanged.  For example::

       [x for x in S]    # This is a list comprehension.
       [(x for x in S)]  # This is a list containing one generator
                         # expression.

   Unfortunately, there is currently a slight syntactic difference.
   The expression::

       [x for x in 1, 2, 3]

   is legal, meaning::

       [x for x in (1, 2, 3)]

   But generator expressions will not allow the former version::

       (x for x in 1, 2, 3)

   is illegal.

   The former list comprehension syntax will become illegal in Python
   3.0, and should be deprecated in Python 2.4 and beyond.

   List comprehensions also "leak" their loop variable into the
   surrounding scope.  This will also change in Python 3.0, so that
   the semantic definition of a list comprehension in Python 3.0 will
   be equivalent to list(<generator expression>).  Python 2.4 and
   beyond should issue a deprecation warning if a list comprehension's
   loop variable has the same name as a variable used in the
   immediately surrounding scope.

Early Binding versus Late Binding

After much discussion, it was decided that the first (outermost)
for-expression should be evaluated immediately and that the remaining
expressions be evaluated when the generator is executed.

Asked to summarize the reasoning for binding the first expression,
Guido offered [5]_::

    Consider sum(x for x in foo()). Now suppose there's a bug in foo()
    that raises an exception, and a bug in sum() that raises an
    exception before it starts iterating over its argument. Which
    exception would you expect to see? I'd be surprised if the one in
    sum() was raised rather the one in foo(), since the call to foo()
    is part of the argument to sum(), and I expect arguments to be
    processed before the function is called.

    OTOH, in sum(bar(x) for x in foo()), where sum() and foo()
    are bugfree, but bar() raises an exception, we have no choice but
    to delay the call to bar() until sum() starts iterating -- that's
    part of the contract of generators. (They do nothing until their
    next() method is first called.)

Various use cases were proposed for binding all free variables when
the generator is defined.  And some proponents felt that the resulting
expressions would be easier to understand and debug if bound immediately.

However, Python takes a late binding approach to lambda expressions and
has no precedent for automatic, early binding.  It was felt that
introducing a new paradigm would unnecessarily introduce complexity.

After exploring many possibilities, a consensus emerged that binding
issues were hard to understand and that users should be strongly
encouraged to use generator expressions inside functions that consume
their arguments immediately.  For more complex applications, full
generator definitions are always superior in terms of being obvious
about scope, lifetime, and binding [6]_. 

Reduction Functions

The utility of generator expressions is greatly enhanced when combined
with reduction functions like sum(), min(), and max().  The heapq
module in Python 2.4 includes two new reduction functions: nlargest()
and nsmallest().  Both work well with generator expressions and keep
no more than n items in memory at one time.


* Raymond Hettinger first proposed the idea of "generator
  comprehensions" in January 2002.

* Peter Norvig resurrected the discussion in his proposal for
  Accumulation Displays.

* Alex Martelli provided critical measurements that proved the
  performance benefits of generator expressions.  He also provided
  strong arguments that they were a desirable thing to have.

* Phillip Eby suggested "iterator expressions" as the name.

* Subsequently, Tim Peters suggested the name "generator expressions".

* Armin Rigo, Tim Peters, Guido van Rossum, Samuele Pedroni,
  Hye-Shik Chang and Raymond Hettinger teased out the issues surrounding
  early versus late binding [5]_.

* Jiwon Seo single handedly implemented various versions of the proposal
  including the final version loaded into CVS.  Along the way, there
  were periodic code reviews by Hye-Shik Chang and Raymond Hettinger.
  Guido van Rossum made the key design decisions after comments from
  Armin Rigo and newsgroup discussions.  Raymond Hettinger provided
  the test suite, documentation, tutorial, and examples [6]_.


.. [1] PEP 202 List Comprehensions

.. [2] PEP 255 Simple Generators

.. [3] Peter Norvig's Accumulation Display Proposal

.. [4] Jeff Epler had worked up a patch demonstrating
       the previously proposed bracket and yield syntax

.. [5] Discussion over the relative merits of early versus late binding

.. [6] Patch discussion and alternative patches on Source Forge


This document has been placed in the public domain.

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