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‎README.md

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#Intro
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# Learn Data Structure and Algorithms by Python
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> You need to have basic understanding of the Python programming language to proceed with the codes from this repository.
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Python 3 is used for the implementation of the data structures and Algorithms
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### Primitive types or data structures in Python
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Integer, Float, Boolean, Char
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## Table of Contents
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- [Introduction to Python](#introduction)
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- [Data Structure](./Data%20Structure/)
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- [Linked List](./Data%20Structure/Linked%20List/)
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- [Stack](./Data%20Structure/Stack/)
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- [Queue](./Data%20Structure/Queue/)
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- Binary Search Tree (BST)
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- Heap
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- Hash Table
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- Disjoint Set Union (Union Find)
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- Trie
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- Suffix Array
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- Segment Tree
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- Binary Indexed Tree (BIT)
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- Heavy Light Decomposition
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- [Searching](./Searching/)
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- [Linear Search](./Searching/Linear%20Search/)
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- [Binary Search](./Searching/Binary%20Search/)
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- [Ternary Search](./Searching/Ternary%20Search/)
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- [Sorting](./Sorting/)
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- Selection Sort
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- [Bubble Sort](./Sorting/Bubble%20Sort/)
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- Insertion Sort
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- Merge Sort
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- Quick Sort
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- Bucket Sort
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- Counting Sort
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- Heap Sort
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- Radix Sort
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- Graph Algorithms
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- Graph Representation
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- Breadth First Search (BFS)
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- Depth First Search (DFS)
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- Topological Sort
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- Strongly Connected Components (SCC)
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- Minimum Spanning Tree (MST)
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- All Pairs Shortest Path (Floyd Warshall's Algorithm)
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- Single Source Shortest Path Algorithm
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- Djkastra's Algorithm
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- Bellman Ford Algorithm
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- Directed Acyclic Graph
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- Bipartite Matching
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- Articulation Point, Bridge
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- Euler Tour/Path
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- Hamiltonian Cycle
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- Stable Marriage Problem
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- Chinese Postman Problem
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- 2-satisfiability
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- Flow Algorithms
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- Maximum Flow
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- Minimum Cut
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- Min-Cost Max Flow
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- Maximum Bipartite Matching
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- Vertex Cover
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- Dynamic Programming
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- Rod Cutting
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- Maximum Sum (1D, 2D)
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- Coin Change
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- Longest Common Subsequence
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- Longest Increasing Subsequence
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- Matrix Multiplication
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- Edit Distance (Levenshtein distance)
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- 0/1 Knapsack
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- Travelling Salesman Problem
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- Optimal Binary Search Tree
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- Greedy Algorithms
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- Activity Selection/Task Scheduling
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- Huffman Coding
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- Knapsack Problem (Fractional Knapsack)
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- String Algorithms
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- Rabin-Karp Algorithm
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- Knuth-Morris-Pratt Algorithm
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- Z Algorithm
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- Aho-Korasick Algorithm
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- Manachers Algorithm
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- Boyr-Moore Algorithm
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- Number Theory
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- Greatest Common Divisor (GCD)
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- Longest Common Multiplier (LCM)
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- Euler Totient (Phi)
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- Primality Testing
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- Prime finding(Sieve of Eratosthenes)
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- Prime factorization
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- Factorial
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- Fibonacci
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- Counting, Permutation, combination
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- Exponentiation
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- Big Mod
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- Euclid, Extended euclid
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- Josephus Problem
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- Farey Sequence
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- Catalan numbers
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- Burnside's lemma/circular permutation
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- Modular inverse
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- Probability
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- Chinese Remainder Theorem
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- Gaussian Elimination method
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- Dilworth's Theorem
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- Matrix Exponentiation
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- Computational Geometry
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- Pick's Theorem
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- Convex hull
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- Line Intersection
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- Point in a polygon
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- Area of a polygon
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- Line Sweeping
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- Polygon intersection
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- Closest Pair
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- Game Theory
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- Take Away Game
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- Nim's Game
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- Sprague-grundy Number
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- Others
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- BackTracking
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- N-Queen's Problem
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- [Tower of Hanoi Problem](./Others/Tower%20of%20Hanoi/)
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---
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## Introduction
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### Big-O Notation and Time Complexity Analysis
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[Algorithms in plain English: time complexity and Big-O notation](https://medium.freecodecamp.com/time-is-complex-but-priceless-f0abd015063c)
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[Big-O Cheat Sheet Link](http://bigocheatsheet.com/)
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### How to Use
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Install Python 3.x in your machine [from here](https://www.python.org/downloads/release/python-343/) (if it's already not installed) and add it to your environment path so that it is accessible in terminal commands.
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Then you can run a Python file like this:
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```python
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python file.py
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```
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or
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```python
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python3 file.py
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```
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### Useful Links:
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* [Algorithms, 4th Edition (book by: Robert Sedgewick and Kevin Wayne)](http://algs4.cs.princeton.edu/home/)
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* [Khan Academy tutorial on Algorithms](https://www.khanacademy.org/computing/computer-science/algorithms)
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* [Topcoder Tutorials](https://www.topcoder.com/community/data-science/data-science-tutorials/)
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* [GeeksforGeeks](http://www.geeksforgeeks.org/)
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* [hackerearth Tutorial](https://www.hackerearth.com/practice/)

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