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

0. Array
   • Introduction to NumPy arrays
1. Arithmetic Operations
   • Arithmetic operations
   • Operations between (array & scalar) and (array & array)
2. Comparative Operations
   • Comparative operations
   • Operations between (array & scalar) and (array & array)
3. Index & Slice
   • Indexing and slicing arrays
   • Advanced indexing techniques
   • Mask & Filters
   • Integer & Boolean array indexing
4. Axes
   • Understanding axes in NumPy
5. Modifications
6. ndarray
7. Array Creation
8. Mathematics
9. Statistics
10. Sort, Search & Count
11. Logic
12. Set
13. Linear Algebra
14. Structured Array
15. Input/Output
16. Random Generator
17. Fourier Transform
18. Efficient Computing
19. Miscellaneous
20. Looking Ahead

You can install all dependencies listed in requirements.txt using pip.

pip install -r requirements.txt

Note: This project was developed using Python v3.12.3. If you encounter issues running the dependencies or code, consider using this specific Python version.

• Open the root folder with VS Code
  • Windows/Linux: Ctrl + K followed by Ctrl + O
  • macOS: Cmd + K followed by Cmd + O
• Open .ipynb files using Jupyter extension integrated with VS Code
• Allow Visual Studio Code to install any recommended dependencies for working with Jupyter Notebooks.
• Jupyter is integrated with both VS Code & Google Colab

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

• 📍linktr.ee/mr_pylin

I look forward to connecting with you!

• NumPy Website:
  • The official website for NumPy, providing information, tutorials, and resources for the NumPy library
  • Link: numpy.org
• NumPy Documentation:
  • Comprehensive guide and reference for all functionalities and features of the NumPy library
  • Link: numpy.org/doc
• NumPy Source Code:
  • Over 1500 contributers are currently working on NumPy.
  • Link: github.com/numpy/numpy
• Looking Ahead:
  • Pandas
    • A powerful, open-source data analysis and manipulation library for Python
    • Pandas is built on top of NumPy
    • Link: pandas.pydata.org/
  • MatPlotLib
    • A comprehensive library for creating static, animated, and interactive visualizations in Python
    • Link: matplotlib.org