HOW DECORATORS AND GENERATORS EXPLAINED SIMPLY

How Decorators and Generators Explained Simply

How Decorators and Generators Explained Simply

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Introduction

A decorator is a function that takes another function as input and returns a new function with added behavior. This allows you to extend or modify the behavior of functions or methods without changing their actual code.Imagine you have a basic function, like one that says hello. Python Course in Bangalore Now, what if you wanted to add some extra functionality to it without changing the original function's code directly? That's where decorators come in!

How Decorators Work


Decorators leverage Python's concept of functions as first-class objects, meaning functions can be passed around and used as arguments just like any other object (string, int, float, list, etc.).Python Training in Bangalore

  1. A decorator is itself a function.  

  2. You "apply" a decorator to another function using a special @ syntax right before the function definition.  Best Python Course in Bangalore

  3. When you call the decorated function, the decorator's code gets executed before and/or after the original function's code.  


Generators: Producing Values on Demand

Imagine you need to work with a very large sequence of numbers, maybe even an infinite one. Creating and storing the entire sequence in memory at once could be very inefficient or even impossible.Python Course Training in Bangalore

Generators solve this by producing values one at a time, only when you need them. Think of it like an iterator that knows how to produce the next value in the sequence.

How they work (in simple terms):

  1. Generators are functions that use the yield keyword instead of return.  

  2. When a generator function is called, it doesn't execute the whole function at once. Instead, it returns a generator object.Python Training in Bangalore

  3. When you iterate over this generator object (e.g., using a for loop or the next() function), the generator function runs until it encounters a yield statement.

  4. The value after yield is produced, and the function's state is saved.  

  5. The next time you ask for a value, the function resumes from where it left off after the yield.Best Python Course in Bangalore

  6. The generator continues to produce values until the function finishes or encounters a return statement (without a value, which signals the end).


Benefits of Generators:

  • Memory Efficiency: They only produce one item at a time, so they use significantly less memory, especially when dealing with large or infinite sequences.  .Python Course Training in Bangalore

  • Lazy Evaluation: Values are generated only when they are needed, which can save computation time if you don't need to process the entire sequence.

  • Cleaner Code for Iteration: They provide a concise way to define custom iterators.


Conclusion


In 2025,Python will be more important than ever for advancing careers across many different industries. As we've seen, there are several exciting career paths you can take with Python , each providing unique ways to work with data and drive impactful decisions., At Nearlearn is the Top Python Training in Bangalore  we understand the power of data and are dedicated to providing top-notch training solutions that empower professionals to harness this power effectively. One of the most transformative tools we train individuals on is Python.

 

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