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Python Generators
In this tutorial, you'll learn how to create iterations easily using Python generators, how it is different from iterators and normal functions, and why you should use it.
Generators in Python
There is a lot of work in building an iterator in Python. We have to implement a class with __iter__() and __next__() method, keep track of internal states, and raise StopIteration when there are no values to be returned.

This is both lengthy and counterintuitive. Generator comes to the rescue in such situations.

Python generators are a simple way of creating iterators. All the work we mentioned above are automatically handled by generators in Python.

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

Create Generators in Python
It is fairly simple to create a generator in Python. It is as easy as defining a normal function, but with a 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.

Differences between Generator function and Normal function
Here is how a generator function differs from a normal function.

Generator function contains one or more yield statements.
When called, it returns an object (iterator) but does not start execution immediately.
Methods like __iter__() and __next__() are implemented automatically. So we can iterate through the items using next().
Once the function yields, the function is paused and the control is transferred to the caller.
Local variables and their states are remembered between successive calls.
Finally, when the function terminates, StopIteration is raised automatically on further calls.