This repository contains my hands-on practice programs while learning NumPy for Data Science and numerical computing.
As a Computer Science Engineering (Data Science) student, I created this repository to strengthen my understanding of array operations, vectorized computation, and statistical functions using NumPy.
🚀 Topics Covered
🔹 Array Creation (1D, 2D, 3D)
🔹 Multi-Dimensional Arrays
🔹 Scalar & Element-wise Arithmetic
🔹 Vectorized Mathematical Functions
🔹 Broadcasting
🔹 Array Slicing & Indexing
🔹 Filtering & Conditional Selection
🔹 Aggregate Statistical Functions
🔹 Random Number Generation
📂 Project Structure NumPy-Practice/ │ ├── main.py # Introduction to NumPy arrays ├── multiarray.py # Multi-dimensional arrays ├── arithmetic.py # Arithmetic & vectorized operations ├── broadcasting.py # Broadcasting examples ├── slicing.py # Slicing and indexing ├── filtering.py # Filtering and conditions ├── aggregateFunction.py # Statistical aggregate functions ├── randomNumber.py # Random number generation 🧠 Key Concepts Learned
Efficient computation using vectorized operations
Broadcasting rules in NumPy
Statistical analysis using built-in functions
Conditional filtering using boolean masks
Differences between Python lists and NumPy arrays
🛠 Technologies Used
Python 3
NumPy Library
🎯 Purpose of This Repository
This repository serves as:
📘 My learning journal for NumPy
🧪 Practice implementation of Data Science fundamentals
🏗 Foundation for future projects in Data Analysis and Machine Learning
📌 Future Improvements
Add mini data analysis project
Add visualization using Matplotlib
Integrate with Pandas
Add real-world dataset example
⭐ Author
Piyush Kumar CSE - Data Science Student