competitivepython is a library of algorithms and data structures implemented in Python. It is designed to be a useful resource for developers who need to implement common algorithms and data structures in their projects.

• Implements a wide range of algorithms and data structures, including:
  • 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
• Easy to use and understand, with well-documented code
• Portable 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 PyPy in your project, simply import the desired algorithm or data structure and use it as needed. For example:

• searches implementation example

  from competitivepython import searches
  
  result = searches.binary_search([1, 2, 3, 4, 5], 3)
  result2 = searches.linear_search([5, 7, 9, 2, 4, 10], 4)
  txt = "ABABDABACDABABCABAB"
  pat = "ABABCABAB"
  result3 = searches.kmp_search(pat,txt)
  print(result)  # Output: 2
  print(result2)  # Output: 4
  print(result3) # Output: [10]
  

• sorting implementation example

  from competitivepython import sorting
  
  arr = [112, 6, 7, 12, 15]
  
  res = sorting.bubble_sort(arr)
  res1 = sorting.bucket_sort(arr)
  res2 = sorting.heap_sort(arr)
  res3 = sorting.insertion_sort(arr)
  res4 = sorting.merge_sort(arr)
  res5 = sorting.quick_sort(arr)
  res6 = sorting.radix_sort(arr)
  res7 = sorting.selection_sort(arr)
  res8 = sorting.shell_sort(arr)
  res9 = sorting.tim_sort(arr)
  
  print('bubble sort:', res, 'bucket sort:', res1, 'heap sort:', res2, 'insertion sort:', res3, 'merge sort:', res4,
      'quick sort:', res5, 'radix sort:', res6, 'selection sort:', res7, 'shell sort:', res8, 'tim sort:', res9)
  
  ''' Output --- 
   bubble sort: [6, 7, 12, 15, 112] bucket sort: [6, 7, 12, 15, 112] heap sort: [6, 7, 12, 15, 112] 
  insertion sort: [6, 7, 12, 15, 112] merge sort: [6, 7, 12, 15, 112] quick sort: [6, 7, 12, 15, 112] 
  radix sort: [6, 7, 12, # 15, 112] selection sort: [6, 7, 12, 15, 112] shell sort: [6, 7, 12, 15, 112] 
  tim sort: [6, 7, 12, 15, 112]
  '''
  

• graphs implementation example

  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')
  result2 = graphs.depth_first_search(graph, 'C')
  result3 = graphs.dijkstra(graph, start, end)
  print("bfs:",result)
  print("dfs:",result2)
  print("dijikstra:",result3)
  
  ''' Output--
      bfs: {'B', 'D', 'C', 'A'}
      dfs: {'B', 'D', 'C', 'A'}
      dijikstra: {'distance': 4, 'path': ['B', 'C', 'D']}
  '''
  

• trees implementation example

  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.