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

Generators in Python

  Learning Sections          show Generators in Python Generators are a special type of iterator in Python that allow you to iterate over a sequence of items without storing them all in memory at once. They are useful for generating large sequences of data on-the-fly, or for processing data in a memory-efficient manner. Creating Generators In Python, generators are created using generator functions or generator expressions: # Generator function def my_generator ( n ): for i in range ( n ): yield i # Generator expression my_generator = ( i for i in range ( 10 )) A generator function uses the yield keyword to yield values one at a time, while a generator expression creates an anonymous generator. Iterating Over Generators You can iterate over the values produced by a generator using a for loop: for value in my_generator ( 5 ): print ( value ) This w...

Walrus Operator in Python

  Learning Sections          show Walrus Operator in Python The walrus operator ( := ) is a new assignment operator introduced in Python 3.8. It allows you to assign values to variables as part of an expression, making certain constructs more concise. Basic Usage The basic syntax for the walrus operator is: variable_name := expression Here, variable_name is assigned the value of expression , and the result of the expression is also returned. Example: Simplifying Code Consider the following example where we find and print the length of a list if it's greater than 3: # Without walrus operator my_list = [ 1 , 2 , 3 , 4 ] if len ( my_list ) > 3 : length = len ( my_list ) print ( length ) # With walrus operator my_list = [ 1 , 2 , 3 , 4 ] if ( length := len ( my_list )) > 3 : print ( length ) In the second example, the walrus operator assigns the result of len(my...

Lambda Functions in Python

  Learning Sections          show Lambda Functions in Python Lambda functions, also known as anonymous functions, are small, unnamed functions defined with the lambda keyword. They can have any number of arguments but only one expression. Lambda functions are often used for short, throwaway functions that are not needed elsewhere in the code. 1. Basic Syntax The syntax for a lambda function is: lambda arguments : expression Example: add = lambda x , y : x + y print ( add ( 2 , 3 )) # Output: 5 2. Using Lambda with Built-in Functions Lambda functions are commonly used with built-in functions like map() , filter() , and sorted() . Example with map() : numbers = [ 1 , 2 , 3 , 4 , 5 ] squared = map ( lambda x : x ** 2 , numbers ) print ( list ( squared )) # Output: [1, 4, 9, 16, 25] Example with filter() : numbers = [ 1 , 2 , 3 , 4 , 5 ] even = filter ( lambd...