Generators & Iterators in python

def primenumbers_lessthan5():
  yield "2"
  yield "3"
for i in primenumbers_lessthan5():
print(i)

We are using the python yield statement to extract prime numbers less than 5. The result of this code will be:

2
3

We have got the result as we wanted using the yield statement. The yield statement can also be used in the ‘for’ loop in same way as we make use of iterators in Python.

Here is an example for this:

def my_program():
   yield 'Python'
   yield 'Ruby'
   yield 'C'
for char in my_program():
  print(char)

As we can see in the above example we have used ‘for’ statement along with the ‘for’ loop. The result of the code will be as follows.

Python
Ruby
C

Python Generator can also be used to generate infinite values.

[ Learn About Python Classes and Objects ]

Generator expressions

Python Generator expressions are similar to list comprehensions in the appearance. The difference is that instead of the list, python generator expressions return a generator.

They are helpful in saving memory. It will become clear with this example:

k = sum(x*x*x for x in range(1,5))
print (k)

As we can see that the as per the syntax of the python generator expression, we have to ensure that we use parentheses inside on the either side. Here is the result:

100

Since the Python generator can also be looped upon, the generators are also iterators.

Generator Functions

Generators are sometimes known as generator functions or resumable functions. These functions return a generator instead of the generator iterator. The generator is defined like a normal function however whenever it requires to generate a value. It uses ‘yield’ keyword for that instead of using the return.

They allow us to declare if any generator functions behave as an iterator.

Advanced generators

Python Generators are very useful to programmers in case they have to deal with a large amount of data. We can iterate a large amount of numbers and even infinite datasets without having to store them in the memory. Here we will look at the endless generator examples:

def fibonacci():
"""Generates an infinite sequence of Fibonacci numbers on demand"""
a, b = 0, 1
while True:
     yield a
     a, b = b, a + b
f = fibonacci()
counter = 0
for x in f:
print(x, " ", end="")
counter += 1
print()

In this code, you can see that we are generating a Fibonacci sequence. Since there is no counter break, we will get an infinite result and ultimately result in the time limit exceeded. This will not be convenient to display it here. Hence we will display the same code but this time with a break.

def fibonacci():
"""Generates an infinite sequence of Fibonacci numbers on demand"""
a, b = 0, 1
while True:
     yield a
     a, b = b, a + b
f = fibonacci()
counter = 0
for x in f:
print(x, " ", end="")
counter += 1
if (counter > 10):
     break
print()

0  1  1  2  3  5  8  13  21  34  55 

Decorating generator:

We require using the next function for starting the python3 iterator and then we can advance it to the python yield statement. We will use a decorator generator code now. 

def infinite_looper(objects):
count = -1
message = yield None
while True:
     count += 1
     if message != None:
         count = 0 if message < 0 else message
     if count >= len(objects):
         count = 0
     message = yield objects[count]      
x = infinite_looper("batman")
print(next(x))
print(x.send(4))
print(next(x))
print(next(x))
print(x.send(5))
print(next(x))

The result of the decorator code will be as follows.

b
a
n
b
n
b

Itertools

The itertools modules provide a lot of tools that we can use for a variety of activities. Some of those functions have been described below:

Chain:

This tool is used if we need to chain multiple iterators together.

from itertools import chain
for i in chain(['x', 'y', 'z'], ['a', 'b', 'c']):
print (i)

Output:

x
y
z
a
b
c

izip:

This tool returns an iterator combines the elements of iterators into tuples.

from itertools import *
for x, y in zip(["a", "b", "c"], [1, 2, 3]):
print (x, y)

Output

islice :

from itertools import *  
print ('Stop at 5:')
for i in islice(count(), 5):
   print (i)
print ('Start at 5, Stop at 10:')
for i in islice(count(), 5, 10):
   print (i)
print ('By tens to 100:')
for i in islice(count(), 0, 100, 10):
   print (i)

Output:

Stop at 5:
0
1
2
3
4
Start at 5, Stop at 10:
5
6
7
8
9
By tens to 100:
0
10
20
30
40
50
60
70
80
90

tee:

tee tool returns iterators which are independent, on the basis of the single

?output.

rom itertools import *
r = islice(count(), 4)
i1, i2 = tee(r)
for i in i1:
print ('i1:', i)
for i in i2:
print ('i2:', i)

Output:

i1: 0
i1: 1
i1: 2
i1: 3
i2: 0
i2: 1
i2: 2
i2: 3

Python Iterators, generators, and the ‘for’ loop

The ‘for’ loop can be used with iterators and generators. The ‘for’ statement is used while looping, as looping is done with the use of iterators in Python along with generators, the ‘for’ loop is important to use. There are many objects that can be used with ‘for’ loop.  All of those objects are called iterable objects.

Difference between Python Generators and Iterators

Although it might seem that the difference between Python iterators and generators is a little confusing, the issue can be resolved with a careful understanding. Here are the differences between Python generators and iterators. Please take a proper look at them to avoid confusion.

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