Python Lambda Functions

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Lambda functions, also known as anonymous functions, are a concise and powerful feature in Python that allows you to create small, unnamed functions on the fly. Despite their brevity, lambda functions play a significant role in functional programming, enabling developers to write more expressive and streamlined code. In this article, we’ll unravel the mysteries of lambda functions, exploring their syntax, applications, and providing real-world examples for hands-on experience. Lambda functions are a versatile tool in the Python programmer’s toolkit. While not suitable for complex tasks, they excel in scenarios that demand concise and ephemeral functionality.

Lambda Function Syntax

The basic syntax of a lambda function is straightforward:

lambda arguments: expression

Lambda functions can take any number of arguments but have a single expression. The result of the expression is implicitly returned.

Simple Example: Squaring a Number

square = lambda x: x**2
# Output: 25

Lambda Functions in Functional Programming

Lambda functions shine in functional programming paradigms, where they are often used with functions like map(), filter(), and reduce().

Using map() with Lambda

numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x**2, numbers))
# Output: [1, 4, 9, 16, 25]

Using filter() with Lambda

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
# Output: [2, 4]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
# Output: [2, 4]

Lambda Functions in Sorting

Lambda functions are handy when sorting data based on custom criteria.

students = [('Alice', 25), ('Bob', 20), ('Charlie', 22)]
sorted_students = sorted(students, key=lambda student: student[1])
# Output: [('Bob', 20), ('Charlie', 22), ('Alice', 25)]

Lambda Functions for Key Functions

Lambda functions are commonly used for defining key functions in functions like max() and min().

max_value = max(numbers, key=lambda x: x**2)
# Output: 5

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Author: Freshers