Sorting Lists of Numbers in Python

This guide provides a variety of techniques for sorting lists of numbers in Python, catering to different scenarios and list sizes. Whether you need to sort small or large lists, Python has efficient built-in tools for the job.

Contents

1. Different Ways to Sort a List of Numbers
   • Approach 1: Using sorted() for Quick Sorting
   • Approach 2: In-Place Sorting with sort()
   • Approach 3: Creating Sorted Copies with Slicing
   • Approach 4: Custom Sorting with Lambda Functions
   • Approach 5: Optimizing for Large Lists with heapq

Different Ways to Sort a List of Numbers

Approach 1: Using sorted() for Quick Sorting

The sorted() function is ideal when you want a new list sorted from the original, leaving the original list unchanged.

numbers = [7, 3, 9, 2]
sorted_list = sorted(numbers)
print(sorted_list)  # Output: [2, 3, 7, 9]

Approach 2: In-Place Sorting with sort()

The sort() method modifies the list directly, which can be more memory-efficient when a new list isn’t needed.

numbers = [7, 3, 9, 2]
numbers.sort()
print(numbers)  # Output: [2, 3, 7, 9]

Approach 3: Creating Sorted Copies with Slicing

Using slicing, you can create a sorted copy of a list without changing the original.

numbers = [7, 3, 9, 2]
sorted_copy = numbers[:]
sorted_copy.sort()
print(sorted_copy)  # Output: [2, 3, 7, 9]

Approach 4: Custom Sorting with Lambda Functions

For cases where sorting requires specific criteria, sorted() or sort() can take a key argument with a lambda function.

numbers = [7, -3, 9, -2]
sorted_custom = sorted(numbers, key=lambda x: abs(x))
print(sorted_custom)  # Output: [-2, -3, 7, 9]

Approach 5: Optimizing for Large Lists with heapq

For large datasets, the heapq module provides efficient sorting functions like nsmallest() and nlargest().

import heapq

numbers = [7, 3, 9, 2]
sorted_large_list = list(heapq.nsmallest(len(numbers), numbers))
print(sorted_large_list)  # Output: [2, 3, 7, 9]