There are two terms involved when we discuss generators.

Generator-Function The definition of a generator function is similar to that of a normal function, but whenever a value needs to be generated, it will be generated using the yield keyword instead of return. If the body of def contains yield, the function will automatically become a generator function.

In Python, a generator is a special type of function that produces a sequence of values on-the-fly, rather than returning a single value and terminating. Generators can be used to generate a large amount of data that would otherwise occupy a lot of memory, or to create an infinite sequence of values.

A generator function is defined using the yield keyword instead of return. When a generator function is called, it returns an iterator object that can be used to iterate over the sequence of values produced by the generator. Each time the yield keyword is encountered in the generator function, the current value is "yielded" to the iterator, and the function's state is saved. The next time the iterator's __next__() method is called, the generator function resumes execution from where it left off, and the next value in the sequence is produced.

Generators can also be defined using generator expressions, which are similar to list comprehensions but create a generator instead of a list. A generator expression is enclosed in parentheses instead of square brackets.

Generators are useful in situations where you need to iterate over a large or infinite sequence of values, but don't want to generate all the values at once. They can also be used to create pipelines of data processing, where each step in the pipeline is a generator function that produces the next step's input.