In Python, a generator is a special type of iterator that allows you to iterate over a potentially large sequence of data without loading the entire sequence into memory at once. This is particularly useful when dealing with large datasets or when you want to generate values on-the-fly.

Generators are defined using a function with the yield statement. The yield statement suspends the function's state, allowing the generator to produce a series of values over multiple calls without losing its state. This contrasts with regular functions, which return a value and lose their state once the function exits.

def simple_generator():
yield 1
yield 2
yield 3

Using the generator

gen = simple_generator()

print(next(gen)) # Output: 1
print(next(gen)) # Output: 2
print(next(gen)) # Output: 3

Python course in Pune: https://www.sevenmentor.com/best-python-classes-in-pune.php

Key concepts of generators:

Lazy Evaluation:

Generators use lazy evaluation, meaning they only compute values as needed. This is different from lists, where all elements are computed and stored in memory at once.
Memory Efficiency:

Generators are memory-efficient since they don't store the entire sequence in memory. They generate each value on-the-fly, making them suitable for working with large datasets.
yield Statement:

The yield statement is the key feature of a generator. It not only returns a value to the caller but also saves the state of the function. When the function is called again, it resumes execution from where it left off.
Iterating Over Generators:

Generators are iterable, and you can iterate over them using a loop or by explicitly calling the next() function until there are no more values to yield.