https://github.com/alanmenchaca/numpy-notes
NumPy a package that defines a multi-dimensional array object and associated fast math functions that operate on it.
https://github.com/alanmenchaca/numpy-notes
arrays jupyer-notebook matrices numpy python vectors
Last synced: 2 months ago
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NumPy a package that defines a multi-dimensional array object and associated fast math functions that operate on it.
- Host: GitHub
- URL: https://github.com/alanmenchaca/numpy-notes
- Owner: alanmenchaca
- Created: 2020-10-06T07:03:49.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2020-12-05T05:29:29.000Z (over 4 years ago)
- Last Synced: 2025-02-09T17:14:41.147Z (4 months ago)
- Topics: arrays, jupyer-notebook, matrices, numpy, python, vectors
- Language: Jupyter Notebook
- Homepage:
- Size: 398 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# numpy-notes
NumPy is the fundamental package needed for scientific computing with Python.
------------------------------------
- **Website:** https://www.numpy.org
- **Documentation:** https://numpy.org/doc
- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
- **Source code:** https://github.com/numpy/numpy
- **Contributing:** https://www.numpy.org/devdocs/dev/index.html
### Main Features:
- **Powerful N-dimensional arrays:** Fast and versatile, the NumPy vectorization, indexing, and
broadcasting concepts are the de-facto standards of array computing today.
- **Numerical computing tools:** NumPy offers comprehensive mathematical functions, random number
generators, linear algebra routines, Fourier transforms, and more.
- **Interoperable:** NumPy supports a wide range of hardware and computing platforms, and plays well
with distributed, GPU, and sparse array libraries.
- **Performant:** The core of NumPy is well-optimized C code.
- **Easy to use:** NumPy’s high level syntax makes it accessible and productive for programmers from
any background or experience level.
- **Open source:** Distributed under a liberal BSD license, NumPy is developed and maintained
publicly on [GitHub](https://github.com/numpy/numpy) by a
vibrant, responsive, and diverse [community](https://numpy.org/community/).
### [Hyperlinks](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/tree/main/) to view notebooks
* [Introduction to Numpy](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/blob/main/Introduction_to_NumPy.ipynb)
* [Creating Arrays](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/blob/main/Creating_Arrays.ipynb)
* [Indexing and Slicing](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/blob/main/Indexing_and_Slicing.ipynb)
* [Reshaping and Resizing](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/blob/main/Reshaping_and_Resizing.ipynb)
* [Vectorized Expressions](https://nbviewer.jupyter.org/github/alanmenchaca/numpy-notes/blob/main/Vectorized_Expressions.ipynb)
### Where to get it?
The source code is currently hosted on GitHub at: https://github.com/numpy/numpy
```sh
# Conda
conda install numpy
```
```sh
# or PyPI
pip install numpy
```
### References
Idris, I. Learning NumPy Array. Mumbai: Packt, 2014.
—. Numpy Beginner’s Guide. 3rd. Mumbai: Packt, 2015.
—. NumPy Cookbook. Mumbai: Packt, 2012.
McKinney, Wes. Python for Data Analysis. Sebastopol: O’Reilly, 2013.
**Notes based on the book:**
Robert, J. (2019). Numerical Python, 2nd Edition. Chiba: Apress