🔢 Learning NumPy – A Hands-on Journey

Welcome to my NumPy learning repository, where I document everything I’m mastering about NumPy — from the absolute basics to practical hands-on exercises.

Whether you're just starting or brushing up your skills, this repo will guide you through every core NumPy concept using organized Python scripts and notes.

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📁 Folder Structure

Fundamentals of NumPy/ │ ├── Array operations/ ├── Copying of Array/ ├── Reshaping Arrays/ ├── Stacking/ │ ├── Array attributes.py ├── Array functions.py ├── Creating Arrays.py ├── Random arrays.py ├── Range and sequences.py ├── fancy indexing.py ├── indexing slicing.py ├── rounding.py ├── splitting.py │ ├── NumPy_roadmap.md ├── NumpyExercises.md

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📘 What’s Inside?

Each script focuses on a specific concept and is full of clear examples and in-code comments.

🧱 Core Scripts:

• Creating Arrays.py: All ways to create arrays — manual, zeros, ones, arange, linspace, etc.
• Array attributes.py: Learn .shape, .ndim, .dtype, .size, etc.
• Array functions.py: Useful built-in functions like sum, mean, argmax, etc.
• Range and sequences.py: Sequences using arange, linspace, and range-like behavior in NumPy.
• Random arrays.py: Random number generation and working with np.random.
• indexing slicing.py: Standard indexing, slicing, negative indexing.
• fancy indexing.py: Boolean and fancy indexing examples.
• rounding.py, splitting.py: Utility functions for data manipulation.

🗂️ Topic-wise Folders:

• Array operations/: Arithmetic, comparison, logical operations
• Copying of Array/: Deep copy vs shallow copy concepts
• Reshaping Arrays/: Reshape, flatten, ravel, transpose
• Stacking/: Vertical and horizontal stacking, concatenation

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🛤️ Learning Guide

Check out NumPy_roadmap.md for a step-by-step roadmap of topics to follow.
Use NumpyExercises.md to test your understanding with practical challenges and coding problems.

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/Learning-NumPy.git
   cd Learning-NumPy

Open files in any code editor (like VS Code) or run them via terminal or Jupyter Notebook for interactive execution.

🎯 Goal The goal of this repo is to:

Build intuition for NumPy’s core mechanics

Enable faster and more efficient data processing

Lay a solid foundation for Machine Learning, Data Analysis, and AI

🙌 Contributions Feel free to fork, improve, or suggest additions via pull requests. Let's make this a shared learning space!

🔗 Connect 📍 GitHub: yourusername

📍 LinkedIn: yourname

“The foundation of any AI or data-driven solution is efficient computation — and that begins with NumPy.” — Aniket (Aniketwa)

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Let me know if you'd like:

• a custom banner/logo
• auto-generated table of contents
• GitHub profile or LinkedIn links inserted
• a badge section (e.g., Python version, license, repo stars)

Want me to generate NumPy_roadmap.md and NumpyExercises.md too?