Generators

A generator is a function that returns an object (iterator) which we can iterate over (one value at a time).

Creating a Generator

It is fairly simple to create a generator in Python. It is as easy as defining a normal function with yield statement instead of a return statement.

If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function. Both yield and return will return some value from a function.

The difference is that, while a return statement terminates a function entirely, yield statement pauses the function saving all its states and later continues from there on successive calls.

Let us now see a few examples of generator functions:

# A simple generator function
def myGen():
    n = 1
    print('This is printed first')
    # Generator function contains yield statements
    yield n

    n += 1
    print('This is printed second')
    yield n

    n += 1
    print('This is printed at last')
    yield n

# Using for loop
for item in myGen():
    print(item)

This is printed first
1
This is printed second
2
This is printed at last
3

def revStr(myStr): 
    for i in range(len(myStr) - 1,-1,-1):
        yield myStr[i]

# For loop to reverse the string
for char in revStr("hello"):
     print(char)

o
l
l
e
h

def fibonacciGen(n):
  a = 0
  b = 1
  while a <= n:
    yield a
    a, b = b, a + b

for i in fibonacciGen(100):
  print i,

0 1 1 2 3 5 8 13 21 34 55 89

The generator can also be used to return a list of values

lst = list(fibonacciGen(100))
print lst

[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

Advantages of using generator

• These functions are better w.r.t. memory utilization and code performance, as they allow function to

  avoid doing all work at a time.

• They provide a way to manually save the state between iterations. As the variables in function scope

  are saved and restored automatically.