Python | Generators

Python | Generators

Python, a versatile and simple programming language, offers a rich set of tools for developers to well organized their code and make it more efficient. Among these tools, generators stand out as a powerful mechanism for working with sequences of data. In this article, we’ll begin a journey to explore the concept of generators in Python, understanding how they work and how they can elevate your code to new levels of efficiency and elegance.

At the heart of Python’s generator concept lies the idea of lazy evaluation. Unlike traditional data structures, which store all data in memory, generators produce values on-the-fly, as needed. This approach saves memory, enhances performance, and allows developers to work with large datasets or even infinite sequences effortlessly.

Creating Generators:

Generators can be created in two main ways:
1) using functions with the yield keyword
2) using generator expressions

Function-Based Generators:

Function-based generators are created using functions with the yield statement. When a function contains yield, it becomes a generator, and calling it returns a generator object. The function’s state is saved, and it can be resumed from where it left off when values are requested.

def countdown(n):
    while n > 0:
        yield n
        n -= 1

# Using the generator
countdown_gen = countdown(6)
for num in countdown_gen:
    print(num, end = ", ")  # Outputs: 6, 5, 4, 3, 2, 1,

Generator Expressions:

Generator expressions provide a short way to create generators without explicitly defining a function. They follow a similar syntax to list comprehensions but use parentheses() instead of square brackets[].

squares = (x ** 2 for x in range(1, 6))
for sq in squares:
    print(sq, end = ", ")  # Outputs: 1, 4, 9, 16, 25,

Benefits of Generators:


Generators bring several advantages to your Python code:

  • Memory Efficiency: Generators save memory by producing values on-the-fly, suitable for large datasets.
  • Lazy Evaluation: Computations are performed only when values are needed, optimizing resource usage.
  • Readability: Generators abstract away complex iteration logic, making code more readable and concise.
  • Performance: Reduced memory overhead often leads to improved performance, especially with large data.
  • Infinite Sequences: Generators can handle infinite sequences effortlessly, which is impossible with standard data structures.


Python generators are a remarkable feature that express the language’s philosophy of simplicity and efficiency. By creating generators, you can use the power of lazy evaluation, improving memory usage and code readability. Whether you’re working with large datasets, infinite sequences, or need a more efficient way to iterate over collections, generators are your key to writing more elegant, efficient, and Pythonic code.

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