Lambda Functions and Built-in Functional Tools

Python supports several tools for transforming, filtering, and summarizing collections. These are useful when preparing data for analysis.

Lambda Functions

A lambda function is a small anonymous function written as a single expression.

calculate_tax = lambda price: price * 0.18

print(calculate_tax(1000))

Lambda functions are useful for short operations, especially when another function expects a function as an argument. For more complex calculations, use a regular def function.

Using map()

The map() function applies a function to every element in an iterable.

prices = [100, 200, 300, 400]

taxed_prices = list(

map(lambda price: price * 1.18, prices)

)

print(taxed_prices)

Here, the lambda function calculates the price after adding 18% tax.

Using filter()

The filter() function selects elements that satisfy a condition.

ratings = [2.5, 4.2, 3.8, 4.8, 2.9]

good_ratings = list(

filter(lambda rating: rating >= 4.0, ratings)

)

print(good_ratings)

The result contains only ratings greater than or equal to 4.0.

Using sorted() with a Key

The sorted() function can sort complex collections according to a selected attribute.

products = [

{"name": "Mouse", "price": 700},

{"name": "Keyboard", "price": 1500},

{"name": "Monitor", "price": 12000}

]

sorted_products = sorted(

products,

key=lambda product: product["price"]

)

for product in sorted_products:

print(product["name"], product["price"])

This sorts the products by price in ascending order without changing the original list.