🔢 NumPy Workshop

An in-depth guide to mastering NumPy, covering fundamental to advanced array operations for data science and numerical computing.

📖 Table of Contents

📖 Main Notebooks

1. Array Basics
2. Arithmetic Operations
3. Comparative Operations
4. Index & Slice
5. Axes
6. Array Manipulations
7. NdArray properties & methods
8. Array Creation
9. Mathematics
10. Statistics
11. Sort, Search & Count
12. Logic
13. Set
14. Linear Algebra
15. Structured Array
16. Input/Output
17. Random Generator
18. Fourier Transform
19. Efficient Computing
20. Miscellaneous
21. Looking Ahead

📋 Prerequisites

• 👨‍💻 Programming Fundamentals
  • Proficiency in Python (data types, control structures, functions, classes, etc.).
    • My Python Workshop: github.com/mr-pylin/python-workshop
• 🔣 Mathematics for Machine Learning
  • 🔲 Linear Algebra: Vectors, matrices, matrix operations.
    • Linear Algebra Review and Reference written by Zico Kolter.
    • Notes on Linear Algebra written by Peter J. Cameron.
    • MATH 233 - Linear Algebra I Lecture Notes written by Cesar O. Aguilar.
  • 🎲 Probability & Statistics: Probability distributions, mean/variance, etc.
    • MATH1024: Introduction to Probability and Statistics written by Sujit Sahu.

⚙️ Setup

This project requires Python v3.10 or higher. It was developed and tested using Python v3.12.8. If you encounter issues running the specified version of dependencies, consider using this version of Python.

📝 List of Dependencies

Installing matplotlib and pandas is OPTIONAL. They are used exclusively in the Looking Ahead Notebook to show an application of using them.

📦 Installing Dependencies

📦 Method 1: Poetry (Recommended ✅)

Use Poetry for dependency management. It handles dependencies, virtual environments, and locking versions more efficiently than pip.
To install exact dependency versions specified in poetry.lock for consistent environments without installing the current project as a package:

poetry install --no-root

📦 Method 2: Pip

Install all dependencies listed in requirements.txt using pip:

pip install -r requirements.txt

🛠️ Usage Instructions

1. Open the root folder with VS Code (Ctrl/Cmd + K followed by Ctrl/Cmd + O).
2. Open .ipynb files using the Jupyter extension integrated with VS Code.
3. Select the correct Python kernel and virtual environment where the dependencies were installed.
4. Allow VS Code to install any recommended dependencies for working with Jupyter Notebooks.

✍️ Notes:

• It is highly recommended to stick with the exact dependency versions specified in poetry.lock or requirements.txt rather than using the latest package versions. The repository has been tested on these versions to ensure compatibility and stability.
• This repository is actively maintained, and dependencies are updated regularly to the latest stable versions.
• The table of contents embedded in the notebooks may not function correctly on GitHub.
• For an improved experience, open the notebooks locally or view them via nbviewer.

🔗 Useful Links

NumPy

• Official Website:
  • The official website for NumPy, providing information, tutorials, and resources for the NumPy library
  • Official site: numpy.org
• Documentation
  • Comprehensive guide and reference for all functionalities and features of the NumPy library
  • Doc: numpy.org/doc
• Source Code
  • Over 1500 contributors are currently working on NumPy.
  • Link: github.com/numpy/numpy

Looking Ahead

• Pandas
  • A powerful, open-source data analysis and manipulation library built on top of NumPy for Python
  • Official site: pandas.pydata.org
  • My Pandas Workshop: Coming Soon
• Data Visualization
  • A comprehensive collection of Python libraries for creating static, animated, and interactive visualizations: Matplotlib, Seaborn, and Plotly.
  • Official sites: matplotlib.org | seaborn.pydata.org | plotly.com
  • My MatPlotLib Workshop: github.com/mr-pylin/data-visualization-workshop
• PyTorch
  • An open-source machine learning library for Python developed by Meta AI, used for applications such as deep learning and neural networks.
  • Official site: pytorch.org
  • My PyTorch Workshop: github.com/mr-pylin/pytorch-workshop
• Media Processing
  • A comprehensive resource to explore media processing, from fundamental concepts to advanced techniques.
  • Official sites: opencv.org | scikit-image.org | scipy.org
  • My Media Processing Workshop: github.com/mr-pylin/media-processing-workshop

🔍 Find Me

Any mistakes, suggestions, or contributions? Feel free to reach out to me at:

• 📍linktr.ee/mr_pylin

I look forward to connecting with you! 🏃‍♂️

📄 License

This project is licensed under the Apache License 2.0.
You are free to use, modify, and distribute this code, but you must include copies of both the LICENSE and NOTICE files in any distribution of your work.