NumPy tutorials — NumPy Tutorials (original) (raw)

Contents

• Content
  • Non-executable articles
• Useful links and resources

NumPy tutorials#

This set of tutorials and educational materials is being developed in thenumpy-tutorials repository, and is not a part of the NumPy source tree. The goal of this repository is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with. If you’re interested in adding your own content, check the Contributing section.

To open a live version of the content, click the launch Binder button above. To open each of the .md files, right click and select “Open with -> Notebook”. You can also launch individual tutorials on Binder by clicking on the rocket icon that appears in the upper-right corner of each tutorial. To download a local copy of the .ipynb files, you can eitherclone this repositoryor use the download icon in the upper-right corner of each tutorial.

Content#

• NumPy Features
  • Linear algebra on n-dimensional arrays
  • Saving and sharing your NumPy arrays
  • Masked Arrays
• NumPy Applications
  • Determining Moore’s Law with real data in NumPy
  • Deep learning on MNIST
  • X-ray image processing
  • Determining Static Equilibrium in NumPy
  • Plotting Fractals
  • Analyzing the impact of the lockdown on air quality in Delhi, India
• Contributing
  • Why Jupyter Notebooks?
  • Adding your own tutorials

Non-executable articles#

Help improve the tutorials!

Want to make a valuable contribution to the tutorials? Consider contributing to these existing articles to help make them fully executable and reproducible!

• Articles
  • Deep reinforcement learning with Pong from pixels
  • Sentiment Analysis on notable speeches of the last decade

Useful links and resources#

The following links may be useful:

• NumPy Code of Conduct
• Main NumPy documentation
• NumPy documentation team meeting notes
• NEP 44 - Restructuring the NumPy documentation
• Blog post - Documentation as a way to build Community

Note that regular documentation issues for NumPy can be found in the main NumPy repository (see the Documentationlabels there).