Python (programming language): Difference between revisions

Revision as of 03:20, 13 September 2005

Revision as of 07:56, 13 September 2005

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Introduced in Python 2.2 as optional feature and finalized in version 2.3, [[Generator (computer science)|generators]] are Python's mechanism for [[lazy evaluation]] of a function that would otherwise return a space-prohibitive or computationally intensive list.

OneThis is an example fromto lazily generate the python.orgprime websitenumbers:

<!-- this example sucks, since no need for the generate_ints generator, with xrange being an iterable. -->

def generate_ints(N):

for i in xrange(N):

yield i

<!--the above is not incorrect. xrange is the proper way to generate a range in Python, providing you don't need a list. xrange returns an iterator; range returns a list. thus, xrange consumes less memory.-->

import sys

You can now use the generator generate_ints:

def generate_primes(max=sys.maxint):

primes = []

n = 2

while n < max:

composite = False

for ip in xrange(N)primes:

if not n % p:

composite = True

break

if not composite:

primes.append(n)

yield in

n += 1

To use this function simply call, e.g.:

for i in generate_ints(N):

for i in generate_primes(100):

print i

print i,You can now use the generator generate_ints:

Note that the variable ''N'' should be defined before executing the second piece of code.

The definition of a generator appears identical to that of a function, except the keyword <code>yield</code> is used in place of <code>return</code>. However, a generator is an object with persistent state, which can repeatedly enter and leave the same dynamic extent. A generator call can then be used in place of a list, or other structure whose elements will be iterated over. Whenever the <code>for</code>-loop in the example requires the next item, the generator is called, and yields the next item.

==== Generator Expressions ====