How to Sort With Lambda in Python

1. Understanding sorted() vs. sort()
2. Sorting with the key Parameter
3. Using lambda Functions for Custom Sorting
4. Comparison Table: When to Use Each Sorting Method
5. Conclusion

Sorting is a fundamental operation in Python, essential for organizing data efficiently. Python provides two built-in functions for sorting lists: sorted() and sort(). These functions allow sorting in ascending or descending order, and they support custom sorting logic using the key parameter.

In this guide, we will explore the differences between sorted() and sort(), demonstrate how to use the key parameter for custom sorting, and show practical examples using lambda functions.

Understanding sorted() vs. sort()

Before diving into sorting with lambda functions, it’s important to understand how sorted() and sort() differ.

Example: Difference Between sorted() and .sort()

# Using sorted() (returns a new list)
numbers = [5, 3, 8, 1]
new_sorted_list = sorted(numbers)
print(new_sorted_list)  # [1, 3, 5, 8]
print(numbers)  # [5, 3, 8, 1] (unchanged)

# Using sort() (modifies the original list)
numbers.sort()
print(numbers)  # [1, 3, 5, 8] (original list is modified)

When to Use Each Method?

• Use sorted() when you need a sorted copy without modifying the original list.
• Use .sort() when sorting in place is preferred to save memory.

Sorting with the key Parameter

The key parameter allows us to define custom sorting logic. Instead of sorting based on default order (e.g., alphabetical or numerical), we can extract specific values before sorting.

Example: Sorting Words by Length

words = ["banana", "apple", "cherry", "blueberry"]
sorted_words = sorted(words, key=len)
print(sorted_words)

Output:

['apple', 'banana', 'cherry', 'blueberry']

Why? This sorts words by their length, not alphabetically.

Using lambda Functions for Custom Sorting

lambda functions provide a concise way to define custom sorting logic inline. Let’s explore different use cases.

1. Sorting Strings with Numerical Parts

lst = ["id01", "id10", "id02", "id12", "id03", "id13"]
lst_sorted = sorted(lst, key=lambda x: int(x[2:]))
print(lst_sorted)

Output:

['id01', 'id02', 'id03', 'id10', 'id12', 'id13']

Explanation: This ignores the first two characters (id) and sorts based on the numeric value.

2. Sorting a List of Tuples by a Specific Element

lst = [("Mark", 1), ("Jack", 5), ("Jake", 7), ("Sam", 3)]
lst_sorted = sorted(lst, key=lambda x: x[1])
print(lst_sorted)

Output:

[('Mark', 1), ('Sam', 3), ('Jack', 5), ('Jake', 7)]

Explanation: This sorts by the second element in each tuple (the number).

3. Sorting in Descending Order

Use the reverse=True argument to sort in descending order.

students = [("Alice", 85), ("Bob", 78), ("Charlie", 92)]
sorted_students = sorted(students, key=lambda x: x[1], reverse=True)
print(sorted_students)

Output:

[('Charlie', 92), ('Alice', 85), ('Bob', 78)]

Why? This sorts students by score in descending order.

4. Sorting a Dictionary by Values

scores = {"Alice": 85, "Bob": 78, "Charlie": 92}
sorted_scores = sorted(scores.items(), key=lambda x: x[1])
print(sorted_scores)

Output:

[('Bob', 78), ('Alice', 85), ('Charlie', 92)]

Why? Sorting a dictionary by values is a common use case.

5. Sorting by Last Name (Using split())

names = ["John Doe", "Jane Smith", "Alice Brown"]
sorted_names = sorted(names, key=lambda name: name.split()[-1])
print(sorted_names)

Output:

['Alice Brown', 'John Doe', 'Jane Smith']

Why? Sorting a list of full names by last name is useful in employee lists, contact apps, and databases.

6. Sorting a List of Objects

class Product:
    def __init__(self, name, price):
        self.name = name
        self.price = price

products = [Product("Laptop", 1200), Product("Phone", 800), Product("Tablet", 600)]
sorted_products = sorted(products, key=lambda p: p.price)

for p in sorted_products:
    print(p.name, p.price)

Output:

Tablet 600
Phone 800
Laptop 1200

Why? Sorting objects is essential in real-world applications like e-commerce or inventory management.

Comparison Table: When to Use Each Sorting Method

Conclusion

Sorting lists efficiently is an essential skill in Python. We explored the differences between sorted() and .sort(), learned how to use the key parameter for custom sorting, and demonstrated real-world examples using lambda functions.

By mastering these techniques, you can handle complex sorting tasks effectively and write more optimized, readable, and Pythonic code.