This repository provides a comprehensive collection of Python implementations for various data structures and algorithms. It is designed to offer clear and concise assistance to learners, developers, and enthusiasts, helping them understand and apply both fundamental and advanced concepts in computer science.
This repository includes Python code for implementing and understanding a wide range of data structures and algorithms. Each folder is dedicated to a specific type of algorithm or data structure, offering practical examples and clear implementations. Here's what you'll find:
- Data Structures: Implementations of essential structures like arrays, linked lists, stacks, queues, trees, graphs, and hashing techniques.
- Algorithms: Detailed code for various algorithms, including sorting, searching, and dynamic programming.
- Text Processing: Tools and techniques for text manipulation, such as text editors and unique word count algorithms.
- Special Techniques: Includes advanced algorithm like backtracking, greedy algorithms, and recursion examples.
This repository is intended to be a useful resource for studying and applying algorithms and data structures in Python. Whether you're a student preparing for exams, a developer looking to sharpen your skills, or just curious about these concepts, you'll find valuable information and code examples here.
The repository is organized into several folders, each containing Python files related to specific topics:
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algorithms/: Contains various algorithmic techniques and strategies.dynamic_programming/: Implementations of dynamic programming problems such as Fibonacci sequence, Knapsack problem, and Longest Common Subsequence.greedy/: Algorithms that use a greedy approach, including Activity Selection and Huffman Coding.sorting/: Sorting algorithms including Bubble Sort, Quick Sort, Merge Sort, and Heap Sort.
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backtracking/: Solutions to problems using the backtracking approach, such as N-Queens and Sudoku Solver. -
data_structures/: Contains implementations of fundamental data structures.arrays/: Array-based operations and utilities.linked_list/: Implementations of various linked list types.stacks/: Stack implementations and related algorithms.queues/: Queue implementations including variations like Queue Using Stacks.trees/: Binary trees and variations including AVL Trees, Red-Black Trees, and Binary Search Trees (BST).bst/: Specific implementations for Binary Search Trees.
heaps/: Implementations of heap data structures like Min Heap and Max Heap.hashing/: Hash table implementations using separate chaining and open addressing.tries/: Trie data structures including Trie Class, Word Search, and Auto-Complete features.
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datatypes/: Contains Python’s core data types and operations.lists/: Manipulations, sorting, nested lists, and built-in list methods.tuples/: Tuple creation, slicing, unpacking, and built-in tuple functions.sets/: Set operations, manipulations, comprehensions, and set functions.dictionaries/: Key-value manipulations, dictionary comprehensions, and nested dictionaries.strings/: String operations, formatting, manipulations, and pattern matching with regular expressions.
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graphs/: Graph algorithms including Graph Class, Dijkstra’s Algorithm, DFS, and BFS. -
parentheses/: Includes algorithms for validating balanced parentheses and brackets, ensuring proper nesting and matching of different types of delimiters. -
recursion/: Recursive algorithms including Factorial Computation, Fibonacci sequence, Tower of Hanoi, and Permutations. -
text_processing/: Algorithms and utilities for text manipulation.text_editor/: Implementations related to text editing functionalities.unique_words/: Algorithms for finding and counting unique words.
To get started with the repository:
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Clone the Repository:
git clone https://github.com/virajbhutada/Python-Data-Structures-Algorithms-Toolkit.git
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Navigate to a Specific Folder:
cd Python-Data-Structures-Algorithms-Toolkit/algorithms/dynamic_programming/ -
Run a Python Script:
python FibonacciDP.py
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Explore and Modify Code: Feel free to explore the scripts, modify them, and use them as references for your own projects.
Reporting Issues: If you encounter any issues or bugs, please open an issue on the GitHub repository. Provide a detailed description of the problem, including steps to reproduce it and any relevant information.
This repository is licensed under the MIT License. See the LICENSE file for more details.