NumPy Mastery 🚀 Complete NumPy Tutorial & Practice

Hands-on NumPy tutorial with Jupyter notebooks covering array operations, array creation, indexing, broadcasting, mathematical operations, and real-world NumPy examples. Perfect NumPy crash course for data scientists, ML engineers, and Python developers mastering NumPy fundamentals to advanced array operations!

🚀 Quick Start

git clone https://github.com/Wcoder547/Numpy.git
cd Numpy
pip install numpy jupyter matplotlib
jupyter notebook basic.ipynb  # Start learning!

📊 What's Included (Complete NumPy Coverage)

Notebook	Topic	Key Concepts
basic.ipynb	NumPy Fundamentals	Arrays, dtype, shape, size
Array-operations.ipynb	Array Operations	Math ops, broadcasting, ufuncs
practise.ipynb	Practice Exercises	Real-world problems
last_part.ipynb	Advanced Topics	Advanced indexing, views vs copies

Sample NumPy arrays included: array1.npy, array2.npy, array3.npy, numpy-logo.npy

🔥 Core NumPy Skills Mastered

✅ Array Creation: np.array(), np.zeros(), np.ones(), np.arange()
✅ Array Attributes: shape, dtype, ndim, size
✅ Indexing & Slicing: Basic + Advanced (boolean, fancy)
✅ Mathematical Operations: Element-wise, matrix ops
✅ Broadcasting Rules & Universal Functions (ufuncs)
✅ Array Manipulation: reshape, transpose, concatenate
✅ Views vs Copies (Memory management)
✅ Statistical Functions: mean, std, min, max
✅ Real-world datasets & practice problems
✅ Performance optimization techniques

🛠️ Tech Stack

Core: NumPy 2.0+, Python 3.11+
Visualization: Matplotlib (optional)
Format: Jupyter Notebooks (.ipynb) + .npy files

📁 Repository Structure

Numpy/
├── basic.ipynb              # NumPy fundamentals
├── Array-operations.ipynb   # Complete array operations
├── practise.ipynb          # Hands-on exercises
├── last_part.ipynb         # Advanced topics
├── array1.npy              # Sample dataset 1
├── array2.npy              # Sample dataset 2
├── array3.npy              # Sample dataset 3
├── numpy-logo.npy          # NumPy logo array
└── README.md

🎓 Learning Path (2-3 Hours to NumPy Mastery)

1. basic.ipynb (30min)     → Core concepts
2. Array-operations.ipynb  → Math + Broadcasting
3. practise.ipynb         → Apply knowledge
4. last_part.ipynb        → Advanced techniques

💡 Quick NumPy Examples

# Array creation
import numpy as np
arr = np.array([1, 2, 3, 4])
zeros = np.zeros((3, 3))

# Broadcasting
arr + 10  # [11, 12, 13, 14]

# Statistical operations
np.mean(arr), np.std(arr), arr.shape

🎯 Perfect For

• Data Science beginners learning NumPy foundations
• ML engineers needing fast array operations
• Python developers optimizing numerical code
• Interview prep for NumPy questions
• Academic projects requiring array computations

🤝 Contributing

1. Fork → Clone → Branch
2. Add notebook → Test → Commit  
3. Push → PR with description
4. Follow NumPy style guidelines

👨‍💻 Author

Wasim Akram – Data Scientist | ML Engineer | Python Expert
Email: malikwaseemshzad@gmail.com