Generators in Python

In this lecture, we'll learn the Generators in Python 3.7. See you in next lecture. Happy coding.


What are Generators in Python? Generators are basically functions that return traversable objects or items. These functions do not produce all the items at once, rather they produce them one at a time and only when required. Whenever the for statement is included to iterate over a set of items, a generator function is run. Generators have a number of advantages as well.

Advantages of using Generators : Without Generators in Python, producing iterables is extremely difficult and lengthy. Generators easy to implement as they automatically implement iter(), next() and StopIteration which otherwise, need to be explicitly specified. Memory is saved as the items are produced as when required, unlike normal Python functions. This fact becomes very important when you need to create a huge number of iterators. This is also considered as the biggest advantage of generators. Can be used to produce an infinite number of items. They can also be used to pipeline a number of operations

Generators are iterators that does not occupy space in memory. Yield produces a value to show us a result. And it doesn't keep this value anywhere in RAM. If we want to reach this value again later, we can't. Because it disappears in memory. This is always useful. We use it, when we only want to see a list for the moment and we want it to be lost in memory.