This repository showcases lab assignments completed in an Introduction to Python course. It provides examples of working with various functionalities in Python, including data manipulation, control flow, functions, object-oriented programming (OOP) principles, and the NumPy library.
Welcome! This repository contains my lab assignments from the Introduction to Python course. All the labs are done in collaboration with my classmate Dinesh Sundaramoorthy since it is group task.
What will you find here?
- Five Jupyter Notebook files (.ipynb): Each notebook represents a different lab assignment, containing the code for:
- Exercises and tasks: Addressing various Python concepts and challenges, including:
- Python scripting principles: Working with variables, data types, operators, and control flow statements (if/else, for/while loops) to develop simple scripts.
- Object-oriented programming (OOP): Defining classes, creating objects, utilizing methods and attributes to structure code and promote reusability.
- NumPy library: Employing NumPy arrays for efficient data manipulation, numerical computations, and linear algebra operations.
- Data manipulation and analysis: Working with data structures like lists, dictionaries, and performing calculations using NumPy functionalities.
- Defining and using functions: Creating reusable code blocks to enhance modularity and organization.
- Exercises and tasks: Addressing various Python concepts and challenges, including:
Disclaimer: This repository serves as a personal learning portfolio and does not claim to provide complete solutions or code for specific assignments. It is intended to showcase learning progress and applied skills in Python programming fundamentals, including scripting, OOP, and NumPy integration.