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Iterators and Generators
Pavan Verma
@YinYangPavan
Founder, P3 InfoTech Solutions Pvt. Ltd.
Python Programming Essentials
© SkillBrew http://skillbrew.com
Iterators
© SkillBrew http://skillbrew.com
Iterable objects
 Iterable objects are those that can be used
with a for loop
© SkillBrew http://skillbrew.com
Examples of iterable objects
>>> for i in [1, 2, 3, 4]:
... print i,
...
1 2 3 4
>>> for c in "python":
... print c,
...
p y t h o n
>>> for k in {"x": 1, "y": 2}:
... print k,
...
y x
© SkillBrew http://skillbrew.com
Many functions consume iterables
>>> ",".join(["a", "b", "c"])
'a,b,c'
>>> ",".join({"x": 1, "y": 2})
'y,x'
>>> list("python")
['p', 'y', 't', 'h', 'o', 'n']
>>> list({"x": 1, "y": 2}) ['y', 'x']
© SkillBrew http://skillbrew.com
iter function
 The built-in function iter takes an
iterable object and returns an iterator
© SkillBrew http://skillbrew.com
iter function example
>>> x = iter([1, 2])
>>> x <listiterator object at 0x1004ca850>
>>> x.next()
1
>>> x.next()
2
>>> x.next()
Traceback (most recent call last): File
"<stdin>", line 1, in <module>
StopIteration
© SkillBrew http://skillbrew.com
Iteration protocol
 Each time we call the next method on
the iterator gives us the next element.
 If there are no more elements, it
raises a StopIteration.
© SkillBrew http://skillbrew.com
Implementing iterator as a class
class yrange:
def __init__(self, n):
self.i = 0
self.n = n
def __iter__(self):
return self
def next(self):
if self.i < self.n:
i = self.i
self.i += 1
return i
else:
raise StopIteration()
© SkillBrew http://skillbrew.com
Implementing iterator as a class (2)
 The __iter__ method is what makes an
object iterable
 Behind the scenes, the iter function calls
__iter__ method on the given object
 The return value of __iter__ is an iterator
 Iterator should have a next method and
should raise StopIteration when there are
no more elements
© SkillBrew http://skillbrew.com
Implementing iterator as a class (3)
>>> y = yrange(2)
>>> y.next()
0
>>> y.next()
1
>>> y.next()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 14, in next StopIteration
© SkillBrew http://skillbrew.com
Iterator performance
 Iterators implemented in code are much
more efficient than iterators backed by
memory based datastructures
 Iterators implemented in code do not use
any memory
 Eg. List from range() vs. xrange()
© SkillBrew http://skillbrew.com
Generators
© SkillBrew http://skillbrew.com
Generators
 Generators simplifies creation of iterators
 A generator is a function that produces a
sequence of results instead of a single
value
 So a generator is also an iterator
 With generator, you don’t have to worry
about the iterator protocol
© SkillBrew http://skillbrew.com
Generator example
def yrange(n):
i = 0
while i < n:
yield i
i += 1
Each time the yield statement is executed the
function generates a new value.
© SkillBrew http://skillbrew.com
Generator example (2)
>>> y = yrange(2)
>>> y <generator object yrange at 0x401f30>
>>> y.next()
0
>>> y.next()
1
>>> y.next()
Traceback (most recent call last):
File "<stdin>", line 1, in <module> StopIteration
© SkillBrew http://skillbrew.com
Generator example – Squares
def squares(n):
for i in xrange(n):
yield i * i
© SkillBrew http://skillbrew.com
Generator Expressions
>>> a = [x*x for x in range(10)]
>>> a
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> b = (x*x for x in xrange(10))
>>> b
>>> <generator object <genexpr> at
0x1c46410>
>>> list(b)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Python Programming Essentials - M35 - Iterators & Generators

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