Python Core – Learning Journey

This repository documents my practical learning of Core Python concepts using Jupyter Notebook in the Anaconda environment.
It includes daily exercises, examples, and notes created while exploring Python fundamentals — from the basics to object-oriented programming and file handling.

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Topics Covered

1. Introduction

• Overview of Python and its key features
• Advantages of Python over other languages
• Installing and setting up Anaconda
• Getting started with Jupyter Notebook

2. Data Types and Data Structures

• Understanding variables and data types
• Using operators for computation and logic
• Working with strings, lists, sets, tuples, and dictionaries

3. Control Flow and Conditional Statements

• Conditional logic with if, elif, and else
• Using comparison and logical operators
• Loops: for, while, and nested loops
• Control keywords: break, continue, and pass
• Understanding the range() function

4. Functions and Modules

• Defining and calling functions
• Function arguments, return values, and scope
• Lambda, map, and filter functions
• Writing nested and higher-order functions
• Importing and using modules
• Common Python modules used:
  • math
  • random
  • datetime
  • os
• Understanding namespace, help(), and dir()

5. Classes and Objects (OOP)

• Creating classes and objects
• Understanding the __init__() method and self parameter
• Instance, class, and static properties
• Modifying and deleting object attributes
• Core OOP principles:
  • Inheritance
  • Encapsulation
  • Polymorphism
  • Abstraction
• Exploring multiple dispatch and abstract base classes

6. File Handling

• Creating, reading, and writing files
• Performing operations on files
• Handling errors and exceptions

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Environment

• Platform: macOS
• Environment: Anaconda
• Editor: Jupyter Notebook

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Purpose

This repository serves as a personal record of my Python learning progress, combining theoretical understanding with hands-on notebook practice.
Each notebook represents a topic I studied and implemented for deeper understanding.

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Future Scope

• Advance to NumPy, Pandas, and Matplotlib for data analytics
• Explore SQL integration and data manipulation workflows
• Develop small automation and analysis projects using Python

Thank you.