Welcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.
NumPy, or Numerical Python, is the cornerstone of scientific computation in Python. It offers powerful tools and features for:
- Handling high-performance array operations.
- Working with a wide range of mathematical tasks including linear algebra, Fourier transform, and matrices.
- A platform that's open-source and free, fostering an inclusive scientific computing environment.
Our tutorials are categorized for ease of access. Each tutorial comes in three formats: Notes (Markdown), Python scripts, and Jupyter notebooks.
| Number | Notes | Python | Jupyter |
|---|---|---|---|
| 01 |  |
 |
 |
| 02 | |
|
|
| 03 | |
|
|
| 04 | |
|
|
| 05 | |
|
|
| 06 | |
|
|
| 07 | |
|
|
| 08 | |
|
|
| 09 | |
|
|
For a broader understanding of NumPy, we recommend these resources:
- NumPy: The Illustrated Guide - A visually engaging guide to NumPy's core concepts.
- NumPy Official Documentation - The definitive guide to NumPy functions and features.
- Python Data Science Handbook - Offers a deeper dive into NumPy in the context of data science.
- SciPy Lecture Notes - A comprehensive resource covering scientific computing with Python, including NumPy.
- Real Python NumPy Tutorials - A collection of practical tutorials on using NumPy for various applications.
We encourage contributions that enhance the repository's value. To contribute:
- Fork the repository.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -m 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.