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‎README.md

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# Competitive Programming Algorithm Library in Python
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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.
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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.
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## Features
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- Implements a wide range of algorithms and data structures, including:
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- Provides implementations for several common algorithms and data structures such as:
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- Searches: Binary Search, Linear Search, KMP Pattern Search
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- Graphs: BFS, DFS, Dijkstra
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- Sorting: Bubble Sort, Insertion Sort, Shell Sort, Selection Sort, Bucket Sort, Merge Sort, Tim Sort, Quick Sort, Heap Sort, Radix Sort
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- Trees: Binary Search Tree
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- Easy to use and understand, with well-documented code
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- Portable and compatible with Python 3
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- Codebase is easy to use, well-documented, and compatible with Python 3.
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- Open source and available under the MIT license
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## Installation
@@ -23,78 +22,259 @@ To install competitivepython library, simply run the following command:
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## Usage
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To use PyPy in your project, simply import the desired algorithm or data structure and use it as needed. For example:
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To use competitivepython in your project, import the desired algorithm or data structure and use it as needed. Below are some example use cases:
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- searches implementation example
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- Implementing searches:
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- Binary Search
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```
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from competitivepython import searches
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result = searches.binary_search([1, 2, 3, 4, 5], 3)
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result2 = searches.linear_search([5, 7, 9, 2, 4, 10], 4)
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arr = [1, 2, 3, 4, 5]
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target = 3
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result = searches.binary_search(arr, target)
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print("Binary Search:",result)
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'''Output:
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Binary Search: 2
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'''
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```
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- Linear Search
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```
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from competitivepython import searches
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arr = [5, 7, 9, 2, 4, 10]
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target = 4
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result = searches.linear_search(arr, target)
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print("Linear Search:",result)
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'''Output:
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Linear Search: 4
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'''
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```
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- Knuth–Morris–Pratt string Search
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```
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from competitivepython import searches
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txt = "ABABDABACDABABCABAB"
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pat = "ABABCABAB"
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result3 = searches.kmp_search(pat,txt)
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print(result) # Output: 2
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print(result2) # Output: 4
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print(result3) # Output: [10]
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result = searches.kmp_search(pat,txt)
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print("KMP Search:",result)
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'''Output:
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KMP Search: [10]
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'''
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```
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- Implementing sorting:
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- Bubble Sort
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```
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from competitivepython import sorting
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- sorting implementation example
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arr = [112, 6, 7, 12, 15]
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result = sorting.bubble_sort(arr)
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print('bubble sort:', result)
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''' Output ---
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bubble sort: [6, 7, 12, 15, 112]
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'''
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```
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- Bucket Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.bucket_sort(arr)
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print('bucket sort:', result)
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''' Output ---
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bucket sort: [6, 7, 12, 15, 112]
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'''
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```
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- Heap Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.heap_sort(arr)
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print('heap sort:', result)
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res = sorting.bubble_sort(arr)
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res1 = sorting.bucket_sort(arr)
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res2 = sorting.heap_sort(arr)
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res3 = sorting.insertion_sort(arr)
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res4 = sorting.merge_sort(arr)
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res5 = sorting.quick_sort(arr)
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res6 = sorting.radix_sort(arr)
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res7 = sorting.selection_sort(arr)
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res8 = sorting.shell_sort(arr)
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res9 = sorting.tim_sort(arr)
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''' Output ---
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heap sort: [6, 7, 12, 15, 112]
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'''
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```
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- Insertion Sort
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```
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from competitivepython import sorting
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print('bubble sort:', res, 'bucket sort:', res1, 'heap sort:', res2, 'insertion sort:', res3, 'merge sort:', res4,
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'quick sort:', res5, 'radix sort:', res6, 'selection sort:', res7, 'shell sort:', res8, 'tim sort:', res9)
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arr = [112, 6, 7, 12, 15]
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result = sorting.insertion_sort(arr)
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print('insertion sort:', result)
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''' Output ---
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insertion sort: [6, 7, 12, 15, 112]
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'''
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```
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- Merge Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.merge_sort(arr)
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print('merge sort:', result)
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''' Output ---
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bubble sort: [6, 7, 12, 15, 112] bucket sort: [6, 7, 12, 15, 112] heap sort: [6, 7, 12, 15, 112]
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insertion sort: [6, 7, 12, 15, 112] merge sort: [6, 7, 12, 15, 112] quick sort: [6, 7, 12, 15, 112]
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radix sort: [6, 7, 12, # 15, 112] selection sort: [6, 7, 12, 15, 112] shell sort: [6, 7, 12, 15, 112]
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tim sort: [6, 7, 12, 15, 112]
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merge sort: [6, 7, 12, 15, 112]
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'''
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```
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- Quick Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.quick_sort(arr)
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print('quick sort:', result)
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''' Output ---
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quick sort: [6, 7, 12, 15, 112]
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'''
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```
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- Radix Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.radix_sort(arr)
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print('radix sort:', result)
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- graphs implementation example
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''' Output ---
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radix sort: [6, 7, 12, # 15, 112]
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'''
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```
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- Selection Sort
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```
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from competitivepython import graphs
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from competitivepython import sorting
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graph = {
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'A': {'B': 1, 'C': 4},
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'B': {'A': 1, 'C': 2, 'D': 5},
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'C': {'A': 4, 'B': 2, 'D': 1},
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'D': {'B': 5, 'C': 1},
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}
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start = 'A'
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end = 'D'
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arr = [112, 6, 7, 12, 15]
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result = sorting.selection_sort(arr)
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print('selection sort:', result)
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result = graphs.breadth_first_search(graph, 'C')
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result2 = graphs.depth_first_search(graph, 'C')
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result3 = graphs.dijkstra(graph, start, end)
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print("bfs:",result)
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print("dfs:",result2)
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print("dijikstra:",result3)
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''' Output ---
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selection sort: [6, 7, 12, 15, 112]
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'''
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```
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- Shell Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.shell_sort(arr)
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print('shell sort:', result)
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''' Output--
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bfs: {'B', 'D', 'C', 'A'}
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dfs: {'B', 'D', 'C', 'A'}
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dijikstra: {'distance': 4, 'path': ['B', 'C', 'D']}
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''' Output ---
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shell sort: [6, 7, 12, 15, 112]
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'''
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```
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- Tim Sort
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```
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from competitivepython import sorting
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arr = [112, 6, 7, 12, 15]
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result = sorting.tim_sort(arr)
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print('tim sort:', result)
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''' Output ---
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tim sort: [6, 7, 12, 15, 112]
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'''
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```
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- Implementing graphs:
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- Breadth First Search (or Breadth First Traversal)
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```
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from competitivepython import graphs
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graph = {
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'A': {'B': 1, 'C': 4},
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'B': {'A': 1, 'C': 2, 'D': 5},
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'C': {'A': 4, 'B': 2, 'D': 1},
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'D': {'B': 5, 'C': 1},
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}
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start = 'A'
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end = 'D'
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result = graphs.breadth_first_search(graph, 'C')
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print("bfs:",result)
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''' Output--
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bfs: {'B', 'D', 'C', 'A'}
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'''
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```
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- Depth First Search(or Depth First Traversal)
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```
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from competitivepython import graphs
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graph = {
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'A': {'B': 1, 'C': 4},
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'B': {'A': 1, 'C': 2, 'D': 5},
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'C': {'A': 4, 'B': 2, 'D': 1},
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'D': {'B': 5, 'C': 1},
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}
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start = 'A'
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end = 'D'
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result = graphs.depth_first_search(graph, 'C')
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print("dfs:",result)
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''' Output--
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dfs: {'B', 'D', 'C', 'A'}
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'''
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```
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- Dijkstra’s Shortest Path
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```
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from competitivepython import graphs
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graph = {
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'A': {'B': 1, 'C': 4},
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'B': {'A': 1, 'C': 2, 'D': 5},
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'C': {'A': 4, 'B': 2, 'D': 1},
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'D': {'B': 5, 'C': 1},
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}
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start = 'A'
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end = 'D'
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result = graphs.dijkstra(graph, start, end)
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print("dijikstra:",result)
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''' Output--
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dijikstra: {'distance': 4, 'path': ['B', 'C', 'D']}
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'''
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```
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- trees implementation example
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- Implementing trees:
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```
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from competitivepython import trees

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