competitivepython is an open-source library of algorithms and data structures implemented in Python. It offers a collection of frequently used algorithms and data structures that can be directly used in any Python-based project.
- Checkout the blog regarding this library Click Here
- Provides implementations for several common algorithms and data structures such as:
- Searches: Binary Search, Linear Search, KMP Pattern Search
- Graphs: BFS, DFS, Dijkstra
- Sorting: Bubble Sort, Insertion Sort, Shell Sort, Selection Sort, Bucket Sort, Merge Sort, Tim Sort, Quick Sort, Heap Sort, Radix Sort
- Trees: Binary Search Tree
- Codebase is easy to use, well-documented, and compatible with Python 3.
- Open source and available under the MIT license
To install competitivepython library, simply run the following command:
pip install competitivepython
To use competitivepython in your project, import the desired algorithm or data structure and use it as needed. Below are some example use cases:
-
Implementing searches:
- Binary Search
from competitivepython import searches arr = [1, 2, 3, 4, 5] target = 3 result = searches.binary_search(arr, target) print("Binary Search:",result) '''Output: Binary Search: 2 ''' - Linear Search
from competitivepython import searches arr = [5, 7, 9, 2, 4, 10] target = 4 result = searches.linear_search(arr, target) print("Linear Search:",result) '''Output: Linear Search: 4 ''' - Knuth–Morris–Pratt string Search
from competitivepython import searches txt = "ABABDABACDABABCABAB" pat = "ABABCABAB" result = searches.kmp_search(pat,txt) print("KMP Search:",result) '''Output: KMP Search: [10] '''
- Binary Search
-
Implementing sorting:
- Bubble Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.bubble_sort(arr) print('bubble sort:', result) ''' Output --- bubble sort: [6, 7, 12, 15, 112] ''' - Bucket Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.bucket_sort(arr) print('bucket sort:', result) ''' Output --- bucket sort: [6, 7, 12, 15, 112] ''' - Heap Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.heap_sort(arr) print('heap sort:', result) ''' Output --- heap sort: [6, 7, 12, 15, 112] ''' - Insertion Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.insertion_sort(arr) print('insertion sort:', result) ''' Output --- insertion sort: [6, 7, 12, 15, 112] ''' - Merge Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.merge_sort(arr) print('merge sort:', result) ''' Output --- merge sort: [6, 7, 12, 15, 112] ''' - Quick Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.quick_sort(arr) print('quick sort:', result) ''' Output --- quick sort: [6, 7, 12, 15, 112] ''' - Radix Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.radix_sort(arr) print('radix sort:', result) ''' Output --- radix sort: [6, 7, 12, # 15, 112] ''' - Selection Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.selection_sort(arr) print('selection sort:', result) ''' Output --- selection sort: [6, 7, 12, 15, 112] ''' - Shell Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.shell_sort(arr) print('shell sort:', result) ''' Output --- shell sort: [6, 7, 12, 15, 112] ''' - Tim Sort
from competitivepython import sorting arr = [112, 6, 7, 12, 15] result = sorting.tim_sort(arr) print('tim sort:', result) ''' Output --- tim sort: [6, 7, 12, 15, 112] '''
- Bubble Sort
-
Implementing graphs:
- Breadth First Search (or Breadth First Traversal)
from competitivepython import graphs graph = { 'A': {'B': 1, 'C': 4}, 'B': {'A': 1, 'C': 2, 'D': 5}, 'C': {'A': 4, 'B': 2, 'D': 1}, 'D': {'B': 5, 'C': 1}, } start = 'A' end = 'D' result = graphs.breadth_first_search(graph, 'C') print("bfs:",result) ''' Output-- bfs: {'B', 'D', 'C', 'A'} ''' - Depth First Search(or Depth First Traversal)
from competitivepython import graphs graph = { 'A': {'B': 1, 'C': 4}, 'B': {'A': 1, 'C': 2, 'D': 5}, 'C': {'A': 4, 'B': 2, 'D': 1}, 'D': {'B': 5, 'C': 1}, } start = 'A' end = 'D' result = graphs.depth_first_search(graph, 'C') print("dfs:",result) ''' Output-- dfs: {'B', 'D', 'C', 'A'} ''' - Dijkstra’s Shortest Path
from competitivepython import graphs graph = { 'A': {'B': 1, 'C': 4}, 'B': {'A': 1, 'C': 2, 'D': 5}, 'C': {'A': 4, 'B': 2, 'D': 1}, 'D': {'B': 5, 'C': 1}, } start = 'A' end = 'D' result = graphs.dijkstra(graph, start, end) print("dijikstra:",result) ''' Output-- dijikstra: {'distance': 4, 'path': ['B', 'C', 'D']} '''
- Breadth First Search (or Breadth First Traversal)
-
Implementing trees:
from competitivepython import trees # Create an instance of the BinarySearchTree bst = trees.BinarySearchTree() # Insert some values into the tree bst.insert(50) bst.insert(30) bst.insert(20) bst.insert(40) bst.insert(70) bst.insert(60) bst.insert(80) # Check if a value is present in the tree print(bst.search(50)) # Output: True print(bst.search(35)) # Output: False # Get the values in the tree in in-order traversal order print(bst.get_in_order_traversal()) # Output: [20, 30, 40, 50, 60, 70, 80]
If you would like to contribute to the competitivepython project, please refer to the contributing guidelines in CONTRIBUTING.md. We welcome contributions of all types, including bug reports, feature requests, and code contributions.
competitivepython is open source software released under the MIT license. Refer to the LICENSE file for more information.