Fastest Way to Sort in Python
Last Updated :
03 Dec, 2024
Sorting is a fundamental operation in Python. Whether we are working with numbers, strings or complex data structures knowing how to sort efficiently is key to optimizing performance. In this article, we will explore the fastest way to sort in Python breaking down the built-in sorting techniques and optimizing strategies you can use to sort your data quickly.
Sorting Lists in Python
In Python, lists are the most commonly used data structure for storing ordered data. Lists come with built-in sorting functions that are fast and efficient.
Using sorted() Function or Timsort
Timsort is a hybrid sorting algorithm derived from merge sort and insertion sort. It is the default sorting algorithm in Python for lists and is highly efficient for real-world data. sorted() function is a built-in Python function that returns a new list containing all elements from the original list in sorted order. It uses Timsort algorithm which has a time complexity of O(n log n) in the worst case.
Python
a = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
b = sorted(a)
print(b)
Output[1, 1, 2, 3, 3, 4, 5, 5, 5, 6, 9]
Using list.sort() Method
The sort() method is available directly on list objects. It sorts the list in place meaning it changes the original list rather than returning a new one. Like sorted() it uses the Timsort algorithm with a time complexity of O(n log n).
Python
a = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
a.sort()
print(a)
Output[1, 1, 2, 3, 3, 4, 5, 5, 5, 6, 9]
Sorting Arrays in Python
Python also has support for arrays through the array module (a more memory efficient alternative to lists when storing homogenous data). However, sorting arrays is slightly different from sorting lists.
Using sorted() for Arrays
Just like lists, arrays can be sorted using the sorted() function. sorted() function uses timsort algorithm which is most efficient for sorting in python.
Python
import array
arr1 = array.array('i', [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5])
arr2 = sorted(arr1)
print(arr2)
Output[1, 1, 2, 3, 3, 4, 5, 5, 5, 6, 9]
Let's take a look at other sorting algo and their efficiency:
- Quicksort: This algorithm is suitable for large datasets with average case performance focus. Time Complexity: O(n log n) on average, O(n^2) worst-case
- Heapsort: Best for cases when constant space sorting is needed. Time Complexity: O(n log n)
- Mergesort: Best for cases where stability is required and it also works well with linked lists. Time Complexity: O(n log n)
- Insertion Sort: Suitable for small datasets or nearly sorted data. Time Complexity: O(n^2), O(n) best-case
- Bubble Sort: Primarily used for educational purposes, not practical for large datasets. Time Complexity: O(n^2)
- Selection Sort: Simple and intuitive but inefficient for large datasets. Time Complexity: O(n^2)
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