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https://github.com/djeada/numpy-tutorials

Welcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.
https://github.com/djeada/numpy-tutorials

linear-equation-solver matrix-manipulations numpy numpy-arrays system-of-equations vectors

Last synced: about 9 hours ago
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Welcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.

Host: GitHub
URL: https://github.com/djeada/numpy-tutorials
Owner: djeada
License: mit
Created: 2021-01-11T19:24:25.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2024-12-31T11:33:41.000Z (about 1 month ago)
Last Synced: 2024-12-31T12:26:42.644Z (about 1 month ago)
Topics: linear-equation-solver, matrix-manipulations, numpy, numpy-arrays, system-of-equations, vectors
Language: Jupyter Notebook
Homepage:
Size: 274 KB
Stars: 2
Watchers: 4
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # NumPy Tutorials

Welcome to the NumPy Tutorials repository, your one-stop collection of learning materials for mastering NumPy, a fundamental library for scientific computing in Python.

## About NumPy

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.

## Tutorial Index

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 |   |    |   |

## Additional References

For a broader understanding of NumPy, we recommend these resources:

- [NumPy: The Illustrated Guide](https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d) - A visually engaging guide to NumPy's core concepts.

- [NumPy Official Documentation](https://numpy.org/doc/stable/) - The definitive guide to NumPy functions and features.

- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) - Offers a deeper dive into NumPy in the context of data science.

- [SciPy Lecture Notes](https://scipy-lectures.org/) - A comprehensive resource covering scientific computing with Python, including NumPy.

- [Real Python NumPy Tutorials](https://realpython.com/tutorials/numpy/) - A collection of practical tutorials on using NumPy for various applications.

## How to Contribute

We encourage contributions that enhance the repository's value. To contribute:

1. Fork the repository.

2. Create your feature branch (`git checkout -b feature/AmazingFeature`).

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).

4. Push to the branch (`git push origin feature/AmazingFeature`).

5. Open a Pull Request.

   

## License

This project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.