Python Lambda Functions

1. Basic Lambda Function

Create a simple anonymous function that receives a number and returns its square. Lambda is useful when the operation is short enough to fit into a single expression.

Python

square = lambda number: number * number

result = square(8)

print("Square:", result)

Output: Square: 64

Real-Time Example 1: Calculate Product Discount

Online shopping applications frequently calculate discounts. A lambda function can perform a small pricing calculation without creating a separate named function.

Python

discount_price = lambda price, discount: price - (price * discount / 100)

product_price = 2500
discount = 20

final_price = discount_price(product_price, discount)

print("Original Price: ₹", product_price)
print("Discount: ", discount, "%")
print("Final Price: ₹", final_price)

Output: Original Price: ₹ 2500 Discount: 20 % Final Price: ₹ 2000.0

Real-Time Example 2: Sorting Products by Price

E-commerce applications often sort products based on price. The key parameter of sorted() can use a lambda function to tell Python which value should be used for sorting.

Python

products = [
    {"name": "Laptop", "price": 65000},
    {"name": "Mouse", "price": 1200},
    {"name": "Keyboard", "price": 2500},
    {"name": "Monitor", "price": 18000}
]

sorted_products = sorted(
    products,
    key=lambda product: product["price"]
)

for product in sorted_products:
    print(product["name"], "→ ₹", product["price"])

Output: Mouse → ₹ 1200 Keyboard → ₹ 2500 Monitor → ₹ 18000 Laptop → ₹ 65000

Real-Time Example 3: Filtering Active Users

Applications often need to display only active users. A lambda function can be combined with filter() to select records that satisfy a condition.

Python

users = [
    {"name": "Rahul", "active": True},
    {"name": "Priya", "active": False},
    {"name": "Amit", "active": True},
    {"name": "Sneha", "active": False}
]

active_users = list(
    filter(lambda user: user["active"], users)
)

print("Active Users:")

for user in active_users:
    print(user["name"])

Output: Active Users: Rahul Amit

Real-Time Example 4: Transforming Data with map()

The map() function applies an operation to every item in a collection. Lambda is commonly used when that operation is short and does not need a separate function.

Python

prices = [1000, 1500, 2000, 2500]

gst_prices = list(
    map(lambda price: price * 1.18, prices)
)

print("Prices including 18% GST:")

for price in gst_prices:
    print("₹", price)

Output: Prices including 18% GST: ₹ 1180.0 ₹ 1770.0 ₹ 2360.0 ₹ 2950.0

Real-Time Example 5: Sorting Employees by Salary

HR systems frequently sort employee records according to salary. A lambda function can directly select the salary field used by sorted().

Python

employees = [
    {"name": "Anita", "salary": 55000},
    {"name": "Vikram", "salary": 72000},
    {"name": "Rohan", "salary": 48000},
    {"name": "Meera", "salary": 90000}
]

employees_by_salary = sorted(
    employees,
    key=lambda employee: employee["salary"],
    reverse=True
)

for employee in employees_by_salary:
    print(
        employee["name"],
        "→ ₹",
        employee["salary"]
    )

Output: Meera → ₹ 90000 Vikram → ₹ 72000 Anita → ₹ 55000 Rohan → ₹ 48000

Real-Time Example 6: Find Even Numbers

Data-processing programs often need to select values based on a condition. A lambda expression can be used with filter() to keep only even numbers.

Python

numbers = [10, 15, 22, 31, 40, 55, 64]

even_numbers = list(
    filter(lambda number: number % 2 == 0, numbers)
)

print("Even Numbers:", even_numbers)

Output: Even Numbers: [10, 22, 40, 64]

Real-Time Example 7: Convert Names to Uppercase

Data received from forms, files, or APIs may need formatting before being displayed. Lambda with map() can quickly transform every name into uppercase.

Python

names = [
    "rahul",
    "priya",
    "amit",
    "sneha"
]

formatted_names = list(
    map(lambda name: name.upper(), names)
)

print("Formatted Names:", formatted_names)

Output: Formatted Names: ['RAHUL', 'PRIYA', 'AMIT', 'SNEHA']

Real-Time Example 8: Filter Products Above a Price

A shopping application may allow users to display products above a selected budget. Lambda and filter() provide a compact way to implement this condition.

Python

products = [
    {"name": "Phone", "price": 30000},
    {"name": "Laptop", "price": 75000},
    {"name": "Tablet", "price": 25000},
    {"name": "Monitor", "price": 18000}
]

budget = 25000

expensive_products = list(
    filter(lambda product: product["price"] > budget, products)
)

for product in expensive_products:
    print(product["name"], "→ ₹", product["price"])

Output: Phone → ₹ 30000 Laptop → ₹ 75000

Real-Time Example 9: Lambda with Multiple Arguments

Lambda functions can accept multiple arguments. This is useful for small calculations such as calculating an employee's monthly salary after adding a bonus.

Python

calculate_total_salary = lambda salary, bonus: salary + bonus

salary = 60000
bonus = 10000

total = calculate_total_salary(salary, bonus)

print("Base Salary: ₹", salary)
print("Bonus: ₹", bonus)
print("Total Salary: ₹", total)

Output: Base Salary: ₹ 60000 Bonus: ₹ 10000 Total Salary: ₹ 70000

Real-Time Example 10: Lambda with sorted() and Multiple Fields

Real applications sometimes need to sort records using more than one field. A lambda can return a tuple so Python can sort by multiple fields.

Python

students = [
    {"name": "Rahul", "department": "IT", "marks": 82},
    {"name": "Priya", "department": "CS", "marks": 91},
    {"name": "Amit", "department": "IT", "marks": 91},
    {"name": "Sneha", "department": "CS", "marks": 82}
]

sorted_students = sorted(
    students,
    key=lambda student: (
        student["department"],
        -student["marks"]
    )
)

for student in sorted_students:
    print(
        student["department"],
        "→",
        student["name"],
        "→",
        student["marks"]
    )

Output: CS → Priya → 91 CS → Sneha → 82 IT → Amit → 91 IT → Rahul → 82

Real-Time Example 11: Lambda for API/Data Transformation

API responses commonly contain lists of dictionaries. Lambda can extract only the required field before sending the data to another part of an application.

Python

api_response = [
    {
        "id": 101,
        "name": "Laptop",
        "category": "Electronics"
    },
    {
        "id": 102,
        "name": "Chair",
        "category": "Furniture"
    },
    {
        "id": 103,
        "name": "Mobile",
        "category": "Electronics"
    }
]

product_names = list(
    map(lambda item: item["name"], api_response)
)

print("Products received from API:")

for name in product_names:
    print(name)

Output: Products received from API: Laptop Chair Mobile

Real-Time Example 12: Lambda with reduce()

The reduce() function repeatedly combines values into a single result. It is useful for operations such as calculating totals from a collection of values.

Python

from functools import reduce

cart_prices = [500, 1200, 750, 300]

total = reduce(
    lambda first, second: first + second,
    cart_prices
)

print("Shopping Cart Total: ₹", total)

Output: Shopping Cart Total: ₹ 2750

Summary of Python Lambda Functions

Anonymous Function: A lambda creates a small function without giving it a traditional function name.
Single Expression: Lambda functions contain one expression whose result is returned automatically.
map(): Use lambda with map() to transform every item in a collection.
filter(): Use lambda with filter() to select items that satisfy a condition.
sorted(): Use lambda with sorted() to specify the field or calculation used for sorting.
reduce(): Use lambda with reduce() when multiple values need to be combined into one result.
Best Use: Lambda functions are most useful for short, simple operations. For complex business logic, a normal def function is generally easier to read and maintain.

⚡ What is a Lambda Function in Python?

Summary of Python Lambda Functions