Generators in Python

Generator expressions are very similar to list comprehensions. The main difference is that it does not create a full set of results at once; it creates a generator object which can then be iterated over.

For instance, see the difference in the following code:

 List comprehension

[x**2 for x in range(10)]

# Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Generator comprehension

(x**2 for x in range(10))

# Output: <generator object <genexpr> at 0x11b4b7c80>

These are two very different objects:

the list comprehension returns a list object whereas the generator comprehension returns a generator.

generator objects cannot be indexed and makes use of the next function to get items in order.

Use cases

Generator expressions are lazily evaluated, which means that they generate and return each value only when the generator is iterated. This is often useful when iterating through large datasets, avoiding the need to create a duplicate of the dataset in memory:

for square in (x**2 for x in range(1000000)):

Example: Generator Function

def mygenerator():
    print('First item')
    yield 10

    print('Second item')
    yield 20

    print('Last item')
    yield 30 

>>> gen = mygenerator() 
>>> next(gen) 
First item 
10                      
>>> next(gen) 
Second item 
20                      
>>> next(gen) 
Last item 
30