* Remove claim to minimize use of reduce()
* Add claim to minimize use of lambda. * Remove details of proposed new accumulator functions. * Reference Jeff Epler's proof-of-concept patch. * Minor wording improvements everywhere.
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Raymond Hettinger
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126
pep-0289.txt
126
pep-0289.txt
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@ -43,15 +43,21 @@ Similar benefits are conferred on constructors for container objects::
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s = Set(word for line in page for word in line.split())
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d = dict( (k, func(v)) for k in keylist)
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Generator expressions are especially useful in functions that reduce
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an iterable input to a single value::
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Generator expressions are especially useful with functions like sum(),
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min(), and max() that reduce an iterable input to a single value::
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sum(len(line) for line in file if line.strip())
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max(len(line) for line in file if line.strip())
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Accordingly, generator expressions are expected to partially eliminate
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the need for reduce() which is notorious for its lack of clarity. And,
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there are additional speed and clarity benefits from writing expressions
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directly instead of using lambda.
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Generator expressions also address some examples of functionals coded
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with lambda::
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reduce(lambda s, a: s + a.myattr, data, 0)
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reduce(lambda s, a: s + a[3], data, 0)
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These simplify to::
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sum(a.myattr for a in data)
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sum(a[3] for a in data)
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List comprehensions greatly reduced the need for filter() and map().
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Likewise, generator expressions are expected to minimize the need
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@ -76,13 +82,13 @@ its present form. The impetus for the discussion was an innovative
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proposal from Peter Norvig [3]_.
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The Gory Details
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================
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The Details
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===========
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1. The semantics of a generator expression are equivalent to creating
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an anonymous generator function and calling it. There's still discussion
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about whether that generator function should copy the current value of all
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free variables into default arguments.
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about whether that generator function should copy the current value of
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all free variables into default arguments.
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2. The syntax requires that a generator expression always needs to be inside
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a set of parentheses and cannot have a comma on either side. Unfortunately,
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@ -135,95 +141,10 @@ Reduction Functions
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===================
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The utility of generator expressions is greatly enhanced when combined
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with appropriate reduction functions like sum(), min(), and max(). I
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propose creating a set of high speed reduction functions designed to tap the
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power of generator expressions and replace the most common uses of reduce()::
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def xorsum(it):
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return reduce(operator.xor, it, 0)
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def product(it):
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return reduce(operator.mul, it, 1)
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def anytrue(it):
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for elem in it:
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if it:
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return True
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return False
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def alltrue(it):
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for elem in it:
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if it:
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return False
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return True
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def horner(it, x):
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'horner([6,3,4], 5) evaluates 6*x**2 + 3*x + 4 at x=5'
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cum = 0.0
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for c in it:
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cum = cum*x + c
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return cum
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def mean(it):
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data = list(it)
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return sum(data) / len(data)
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def smallest(it, siz=1):
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result = []
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for elem in it:
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if len(result) < siz:
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bisect.insort_left(result, elem)
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elif elem < result[-1]:
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result.pop()
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bisect.insort_left(result, elem)
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return result
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def largest(it, siz=1):
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result = []
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for elem in it:
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if len(result) < siz:
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bisect.insort_left(result, elem)
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elif elem > result[0]:
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result.pop(0)
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bisect.insort_left(result, elem)
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result.reverse()
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return result
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Notes on reduce()
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=================
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Reduce typically has three types of use cases:
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1) Common reduction functions applied directly to elements in a sequence.
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This use case is addressed by the sum(), min(), max(), and the additional
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functions listed above.
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2) Reduce is often used with lambda when the data needs be extracted from
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complex sequence elements. For example::
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reduce(lambda sum, x: sum + x.myattr, data, 0)
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reduce(lambda prod, x: prod * x[3], data, 1)
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In concert with reduction functions, generator expressions completely
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fulfill these use cases::
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sum(x.myattr for x in data)
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product(x[3] for x in data)
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3) On rare occasions, the reduction function is non-standard and requires
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custom coding::
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reduce(lambda cum, c: (cum >> 8) ^ crc32[ord(a) ^ (cum & 0x00ff)], data, -1)
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Because a complex lambda is required, this use case becomes clearer and
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faster when coded directly as a for-loop::
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cum = -1
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for c in data:
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cum = (cum >> 8) ^ crc32[ord(a) ^ (cum & 0x00ff)]
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In conclusion, after adding generator expressions and a set of common reduction
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functions, few, if any cases remain for reduce().
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with reduction functions like sum(), min(), and max(). Separate
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proposals are forthcoming that recommend several new accumulation
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functions possibly including: product(), average(), alltrue(),
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anytrue(), nlargest(), nsmallest().
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Acknowledgements
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@ -258,7 +179,10 @@ References
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.. [3] Peter Norvig's Accumulation Display Proposal
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http:///www.norvig.com/pyacc.html
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.. [4] Jeff Epler had worked up a patch demonstrating
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the previously proposed bracket and yield syntax
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http://python.org/sf/795947
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Copyright
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=========
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