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Python Programming for beginners

Welcome to the Python-Basics repository!! 🎉 For Blogs - https://medium.com/@sudhamsr/python-for-data-science-introduction-to-python-ace25b97bea9

This repository is a beginner-friendly collection of Python programming concepts, designed to provide a strong foundation in Python for new learners.


📘 Repository Overview

  • Contains Jupyter notebooks explaining key Python concepts.
  • Each notebook focuses on a single topic for step-by-step learning.
  • Includes examples, notes, and practice code.
  • Covers everything from Python basics to intermediate topics.

🎯 What You’ll Learn

  • Python basics: variables, operators, data types
  • Control flow: conditions, loops, and iterations
  • Functions, modules, and scope
  • Data structures: lists, tuples, sets, and dictionaries
  • Object-Oriented Programming (OOP)
  • File handling and error handling
  • Useful modules like math, re (regex), and package management with pip
  • Virtual environments for project management
  • Extra notes: f-strings, lambdas, operator summary, comparisons, and more

📘 Contents

🟢 Basics of Python

  1. Basics of Python
  2. Variables & Operators
  3. Arithmetic Operators
  4. Primitive Data Types
  5. Type Casting
  6. Unicode and Literals
  7. Strings
  8. Booleans
  9. Lists
  10. Tuples
  11. Sets
  12. Dictionary
  13. Taking Input in Python
  14. Conditional Flow Statements
  15. Loops and Iterations
  16. Functions
  17. Variable Scope & Annotations
  18. Modules & Built-in Functions
  19. Arrays
  20. NumPy Arrays
  21. Object-Oriented Programming (OOP)

🟡 Intermediate Topics

  1. Regular Expressions
  2. Math Module
  3. PIP – Package Manager
  4. Try, Except, Finally
  5. Errors & Exceptions
  6. File Handling
  7. Virtual Environment

🔵 Extra Notes

  • Comparison of List, Tuple, Set, and Dictionary
  • Formatted Strings (f-strings)
  • Lambda Functions (More about Lambda)
  • More about Loops
  • More about OOP
  • Summary of Operators
  • Examples – Python Numbers

🚀 How to Use

  1. Clone or download this repository.
  2. Open the .ipynb files in Jupyter Notebook or Google Colab.
  3. Run the examples and try modifying them for practice.

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