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🔢 NumPy Workshop

License Python Version Codacy Badge Code Style Repo Size Last Updated PRs Welcome

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
    Introduction to NumPy arrays
  2. Arithmetic Operations
    Arithmetic operations between (array & scalar) or (array & array)
  3. Comparative Operations
    Comparative operations between (array & scalar) or (array & array)
  4. Index & Slice
    Basic and advanced indexing and slicing arrays including Mask & Filters
  5. Axes
    Understanding use of axes in multi-dimensional arrays combined with methods [the hardest part in NumPy for newbies in my opinion]
  6. Array Manipulations
    Techniques for updating values, appending, inserting, reshaping, concatenating, ...
  7. NdArray properties & methods
    Comprehensive overview of properties and methods associated with NumPy arrays.
  8. Array Creation
    Various methods to create NumPy arrays (e.g., numpy.array, numpy.zeros, etc.).
  9. Mathematics
    Mathematical functions and operations available in NumPy
  10. Statistics
    Statistical functions for data analysis (mean, median, variance, etc.).
  11. Sort, Search & Count
    Methods for sorting, searching, and counting elements in arrays.
  12. Logic
    Logical operations and boolean indexing with NumPy arrays.
  13. Set
    Set operations for array elements (union, intersection, difference).
  14. Linear Algebra
    Fundamental linear algebra operations using NumPy (matrix multiplication, determinants).
  15. Structured Array
    Creating and manipulating structured arrays with custom data types.
  16. Input/Output
    Techniques for reading from and writing to files using NumPy.
  17. Random Generator
    Generating random numbers and distributions with NumPy's random module.
  18. Fourier Transform
    Understanding and applying Fourier transforms in NumPy.
  19. Efficient Computing
    Strategies for optimizing performance and memory usage in NumPy operations.
  20. Miscellaneous
    Additional topics and advanced features in NumPy.
  21. Looking Ahead
    Introduction to Pandas for data manipulation and Matplotlib for data visualization.

📋 Prerequisites

⚙️ 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.

ipykernel ipywidgets matplotlib numpy pandas

📦 Install Dependencies

📦 Method 1: Poetry (Recommended)

Use Poetry for dependency management. It handles dependencies, virtual environments, and locking versions more efficiently than pip. To install dependencies using Poetry:

  • Option 1 [Recommended]: Install exact dependency versions specified in poetry.lock for consistent environments:

    poetry install

  • Option 2: Install the latest compatible dependency versions from pyproject.toml and regenerate the poetry.lock file:

    poetry install --no-root

📦 Method 2: Pip

Install all dependencies listed in requirements.txt using pip:

pip install -r requirements.txt

🌐 Connection Issues

If you encounter connection issues during installation, you can try extending the timeout and increasing the number of retries with the following:

  • For Poetry: Use the following command to set the retries and timeout directly in the terminal before running the install:

    • Windows:

      • PowerShell:

        $env:POETRY_HTTP_TIMEOUT=300
$env:POETRY_HTTP_RETRIES=10

      • Command Prompt:

        set POETRY_HTTP_TIMEOUT=300
set POETRY_HTTP_RETRIES=10

    • Linux/macOS:

      • Terminal:

        export POETRY_HTTP_TIMEOUT=300
export POETRY_HTTP_RETRIES=10

  • For Pip: Use the --retries and --timeout flags directly in your pip command:

    pip install -r requirements.txt --retries 10 --timeout 300

🛠️ 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

Looking Ahead

🔍 Find Me

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

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.