Lambda functions in Python are small, anonymous functions defined with the lambda keyword. They are called anonymous because they don't have a name like regular functions defined with the def keyword. Lambda functions can only contain a single expression, but can take multiple arguments. The expression is evaluated and returned automatically. The basic syntax is lambda followed by arguments, a colon, and then the expression. For example, lambda x, y: x plus y creates a function that adds two numbers together.
Lambda functions are particularly useful when working with built-in Python functions that expect other functions as arguments. Common examples include map, which applies a function to each item in an iterable; filter, which selects items based on a condition; and sorted, which can use a custom key function for ordering. For instance, you can use lambda with map to square each number in a list, with filter to select only even numbers, or with sorted to arrange items by a specific attribute. Lambda functions offer advantages like concise one-liner definitions, improved code readability for simple operations, and eliminating the need to define separate named functions for short-lived operations.
Let's compare lambda functions with regular functions. Lambda functions offer several advantages: they have a concise, one-line syntax; can be defined inline where needed; don't require naming for simple operations; and are great for simple, straightforward tasks. However, they also have important limitations: they can only contain a single expression; cannot include docstrings or type annotations; provide limited debugging information; and can actually reduce code readability if they contain complex logic. In the example shown, we can see both a lambda function and its equivalent regular function for sorting names by last name. The regular function is more verbose but offers better documentation and clarity, while the lambda version is more compact but provides less context.