NumPy Practice 🐍📊

This repository contains my practice work on NumPy, one of the most popular Python libraries for numerical computing.
It includes examples of array creation, indexing, slicing, broadcasting, random number generation, mathematical operations, and visualization with Matplotlib.

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📌 Contents

The file demonstrates:

• Array Creation & Properties
  • 1D, 2D, 3D arrays
  • shape, ndim, size, dtype, itemsize
  • zeros, ones, identity, arange, linspace
• Indexing & Slicing
  • Row & column selection
  • Fancy indexing
  • Boolean indexing
• Mathematical Operations
  • Element-wise operations
  • Scalar multiplication
  • Dot product
  • Aggregate functions (sum, mean, std, min, max, etc.)
• Broadcasting
  • Explained with practical scenarios
• Random Number Generation
  • randint, uniform, random, seed
• Problem-Solving with NumPy
  • Replace odd numbers with -1
  • Sorting arrays
  • Percentile calculations
• Visualization with Matplotlib
  • Sine curve
  • Quadratic function

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🚀 Getting Started

Clone the repository:

https://github.com/Sidra-009/Numpy-Practice.git
Navigate into the repo:

bash
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cd numpy-practice
Run the Python file (or open in Jupyter Notebook):

bash
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python numpy_practice.py
📷 Preview
Some examples from the code:

✅ Sin curve plotted using np.linspace + matplotlib

✅ Quadratic function visualization

✅ Boolean indexing to filter values

✅ Broadcasting examples explained with code

📚 Requirements
Python 3.x

NumPy

Matplotlib

Install dependencies:

bash
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pip install numpy matplotlib
✨ Author
Sidra Saqlain

🌟 If you find this helpful, don’t forget to give the repo a ⭐!