Skip to main content

Getters and Setters in Python

 


Learning Sections          show

Getters and Setters in Python

Getters and setters in Python are used to ensure that an attribute's value is retrieved and updated in a controlled way. In Python, the @property decorator is used to define getters, setters, and deleters.

1. Using @property

The @property decorator allows you to define methods that behave like attributes. This makes the code more readable and maintainable.

Example:


class Person:
    def __init__(self, name):
        self._name = name

    # Getter method
    @property
    def name(self):
        return self._name

    # Setter method
    @name.setter
    def name(self, value):
        if isinstance(value, str) and value.strip():
            self._name = value
        else:
            raise ValueError("Name must be a non-empty string")

# Create an instance of the class
person = Person("John")

# Access the name attribute using the getter
print(person.name)  # Output: John

# Update the name attribute using the setter
person.name = "Jane"
print(person.name)  # Output: Jane

# Attempt to set an invalid name
try:
    person.name = ""
except ValueError as e:
    print(e)  # Output: Name must be a non-empty string
    

Getters and setters provide a way to control access to an attribute, allowing for validation and other logic to be applied when the attribute is accessed or modified.

Popular posts from this blog

Learn Python

  Learning Sections Introduction to Python Comment, escape sequence and print statement in Python Variables and Data Types in Python Typecasting in Python User input in Python String slicing and operations on string in Python String methods in Python If else conditional statements in Python Match case statement in Python For loops in Python While loops in Python Break and continue statement in Python Functions in Python Function Arguments in Python introduction to lists in Python List methods in Python Tuples in Python Operations on tuple in Python f strings in Python Docstrings in Python Recursion in Python Sets in Python Set methods in Python Dictionaries in Python for Loop with else in Python Exception Handling in Python Finally keyword in Python Raising custom errors in Python Short hand if else statements Enumerate Function in Python Virtual Environment in Python How import works in Python if __nam...

MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...

Conclusion and where to go after this

  Conclusion and Where to Go After This Congratulations on completing your Python learning journey! You've covered a wide array of topics, from the basics of syntax and data types to advanced concepts like multithreading, multiprocessing, and decorators. But learning doesn't stop here. Python is a versatile language with many specialized fields where you can apply your skills. Here are some potential paths you can explore next: Machine Learning Machine Learning (ML) is one of the most exciting fields you can dive into. Python's libraries like TensorFlow, Keras, scikit-learn, and PyTorch make it an ideal language for building ML models. You'll learn about supervised and unsupervised learning, deep learning, neural networks, and more. Start with the basics of linear regression and classification, then move on to more complex models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Data Structures and Algorithms (DSA)...