| title | NumPy Tutorial |
|---|---|
| layout | default |
This tutorial provides a hands-on introduction to NumPy and to essential programming practices in Python. It is structured around conceptual explanations, worked examples, and practical exercises designed to strengthen confidence in numerical computing. Throughout the material, learners will develop familiarity with NumPy arrays, fundamental linear algebra operations, code validation using assert statements and NumPy testing tools, and debugging strategies for identifying and correcting common programming errors. The tutorial is intended to be completed in Google Colab, thereby allowing users to focus exclusively on coding and problem solving without the need for local installation. It should therefore be understood as a structured pedagogical resource for progressive skill development rather than as an exhaustive reference text.
- Python Basics
- Using Google Colab for This Tutorial
- Basic Structure of Codes Used in This Tutorial
- NumPy Arrays
- Shape of NumPy Arrays
- Accessing NumPy Arrays
- Operations on NumPy Arrays
- Linear Algebra
- Saving and Loading Data with
.npyand.npzFiles
- Tutorial PDF. This is the most up-to-date version of the tutorial and may continue to receive minor revisions, including the correction of typographical errors and other small improvements.
To complete the training and obtain credit, participants are required to engage with the tutorial materials in the prescribed sequence and to complete the two mandatory assessment notebooks:
The content of this tutorial itself is licensed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license, and the underlying source code used to format and display that content is licensed under the MIT license. See the LICENSE files for full details.
If you use or adapt this material, please provide appropriate credit to the original authors and repository.