Welcome to the Time Complexity Analysis with Python repository! This project provides a comprehensive guide for understanding, visualizing, and analyzing the growth rates of common algorithms and data structures. The primary focus is on demonstrating how time complexity influences the performance of different operations, helping you make informed decisions when designing and optimizing your code.
Jupyter Notebook: A detailed notebook that walks you through the process of plotting and analyzing the time complexity of various algorithms and data structures. You'll find code examples, explanations, and visualizations that illustrate key concepts like constant, logarithmic, linear, linearithmic, quadratic, and exponential time complexities. Python Scripts: Additional Python scripts that showcase examples of different algorithms and data structures, highlighting their time complexities in practice. Sample Data: Test data and input examples used to demonstrate the performance of the algorithms across different input sizes. Getting Started Prerequisites To run the Jupyter Notebook and scripts in this repository, you'll need:
Python 3.x
Jupyter Notebook
Common Python libraries such as matplotlib, numpy, and timeit.
You can install the required packages using pip:
pip install numpy matplotlib jupyter
- Clone the repository to your local machine:
git clone https://github.com/yourusername/time-complexity.git - Navigate to the project directory:
cd time-complexity - Launch Jupyter Notebook:
jupyter notebook - Open the Time_complexity_notebook.ipynb notebook and follow the guide.
Interactive Plots: Visualize the growth rate of different algorithms as you adjust input sizes and analyze the impact on performance. Code Examples: Understand how various algorithms behave by observing their time complexity in real-time with sample code snippets. In-depth Explanations: Each section includes detailed explanations of the concepts and algorithms covered, ensuring that you not only see the results but also understand the underlying principles. Use Cases This repository is ideal for:
Students and Educators: Enhance your understanding of algorithm design and analysis with practical, hands-on examples. Developers and Engineers: Optimize your code by understanding the time complexity of different operations, enabling you to choose the most efficient algorithms for your needs. Data Scientists: Gain insights into the performance of algorithms commonly used in data processing and analysis. Contributing Contributions are welcome! If you have suggestions for improvements, new algorithms to analyze, or any other ideas, feel free to open an issue or submit a pull request.