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})")
    

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...