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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 libraryClick 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 searchesarr = [1, 2, 3, 4, 5]target = 3result = searches.binary_search(arr, target)print("Binary Search:",result)  '''Output: Binary Search: 2'''

  • Linear Search

    from competitivepython import searchesarr = [5, 7, 9, 2, 4, 10]target = 4result = searches.linear_search(arr, target)print("Linear Search:",result)  '''Output: Linear Search: 4'''

  • Knuth–Morris–Pratt string Search

    from competitivepython import searchestxt = "ABABDABACDABABCABAB"pat = "ABABCABAB"result = searches.kmp_search(pat,txt)print("KMP Search:",result) '''Output: KMP Search: [10]'''

• 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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [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 sortingarr = [112, 6, 7, 12, 15]result = sorting.tim_sort(arr)print('tim sort:', result)''' Output --- tim sort: [6, 7, 12, 15, 112]'''

• Implementing graphs:

  • Breadth First Search (or Breadth First Traversal)

    from competitivepython import graphsgraph = {    '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 graphsgraph = {    '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 graphsgraph = {    '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']}'''

• Implementing trees:

  from competitivepython import trees# Create an instance of the BinarySearchTreebst = trees.BinarySearchTree()# Insert some values into the treebst.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 treeprint(bst.search(50)) # Output: Trueprint(bst.search(35)) # Output: False# Get the values in the tree in in-order traversal orderprint(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.