Skip to main content

Match Case Statement in Python

 


Learning Sections     show

Match-Case Statements

In Python 3.10 and later, the match-case statement was introduced as a way to perform pattern matching, similar to switch-case statements in other programming languages. It allows you to check the value of a variable against multiple patterns and execute corresponding blocks of code.

Basic Match-Case Statement

The match statement is followed by an expression and several case clauses. Each case specifies a pattern and an action to be taken if the pattern matches the value of the expression.


# Basic match-case statement example
command = "start"

match command:
    case "start":
        print("Starting...")
    case "stop":
        print("Stopping...")
    case "pause":
        print("Pausing...")
    case _:
        print("Unknown command")

Matching Multiple Patterns

Using the pipe symbol |, you can match multiple patterns in a single case clause.


# Matching multiple patterns example
status = "open"

match status:
    case "open" | "opened":
        print("The status is open.")
    case "closed" | "shut":
        print("The status is closed.")
    case _:
        print("Unknown status")

Using Guards in Case Clauses

Guards allow you to add additional conditions to a case clause using the if keyword.


# Using guards in case clauses example
number = 42

match number:
    case x if x >> 0:
        print("The number is positive")
    case x if x == 0:
        print("The number is zero")
    case x if x < 0:
        print("The number is negative")

Pattern Matching with Data Structures

The match-case statement can also be used to match patterns in data structures like lists and tuples.


# Pattern matching with lists example
data = [1, 2, 3]

match data:
    case [1, 2, 3]:
        print("Matched [1, 2, 3]")
    case [4, 5, 6]:
        print("Matched [4, 5, 6]")
    case _:
        print("No match")
    

# Pattern matching with tuples example
point = (4, 5)

match point:
    case (0, 0):
        print("The point is at the origin")
    case (x, y):
        print(f"The point is at ({x}, {y})")
Labels:

Popular posts from this blog

Inheritance in Python

  Learning Sections          show Inheritance in Python Inheritance is a fundamental concept in object-oriented programming (OOP) that allows a class to inherit attributes and methods from another class. The class that inherits is called the child class or subclass, and the class being inherited from is called the parent class or superclass. Basic Inheritance In Python, a child class inherits from a parent class by specifying the parent class in parentheses after the child class name. Example: class Animal : def __init__ ( self , name ): self . name = name def speak ( self ): raise NotImplementedError ( "Subclass must implement this method" ) class Dog ( Animal ): def speak ( self ): return "Woof!" class Cat ( Animal ): def speak ( self ): return "Meow!" # Create instances of Dog and Cat dog = Dog ( "Buddy" ) cat = Cat ( "Whiskers" ...
Read more

Introduction to OOPs in Python

  Learning Sections          show Introduction to Object-Oriented Programming (OOP) Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around objects rather than actions and data rather than logic. It revolves around the concept of "objects", which are instances of classes. These objects encapsulate data, in the form of attributes or properties, and behaviors, in the form of methods or functions. OOP promotes modularity, reusability, and extensibility in software development. Key Concepts of OOP: Class: A class is a blueprint or template for creating objects. It defines the attributes (data) and methods (functions) that will characterize any object instantiated from that class. Object: An object is an instance of a class. It is a concrete realization of the class blueprint, containing actual values instead of placeholders for attributes. Encapsulation: Encapsulation is ...
Read more

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...
Read more