Perfect — thanks for the correction, partner.
Let’s align everything with the actual CS50P structure:

• Lab 6 = File I/O
• Lab 7 = Regular Expressions

Your README will now reflect the true course progression and the work you’ve completed.

Below is the corrected and updated README.md, with Labs 6 and 7 added in the right order and with accurate descriptions of each assignment.

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🐍 CS50P — Introduction to Programming with Python

A growing collection of labs completed as I progress through Harvard’s CS50P course.

This repository captures my ongoing work in CS50’s Introduction to Programming with Python — a hands‑on exploration of Python fundamentals, clean coding practices, testing, and problem‑solving. Each completed lab is organized into its own folder with clear structure and documentation.

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🧰 Skills Demonstrated

• Python fundamentals (variables, loops, conditionals)
• Functions and modular program design
• File I/O and exception handling
• Data structures (lists, dicts, sets, tuples)
• Regular expressions
• Object‑oriented programming
• Unit testing with pytest
• Clean code organization and documentation
• Command‑line tools and user interaction

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🗂 Repository Structure

Each lab is self‑contained inside its folder, with its own code and README.

Folder	Topic	Description
lab0/	Basics	Strings, input/output, simple transformations
lab1/	Conditionals	Branching logic, comparisons, text parsing
lab2/	Loops	Iteration, state tracking, string manipulation
lab3/	Exceptions & I/O	Error handling, file reading/writing
lab4/	Libraries	Using external libraries (emoji, figlet, random)
lab5/	Unit Tests	Writing functions + pytest test suites
lab6/	File I/O	Reading, writing, cleaning, and transforming files
lab7/	Regular Expressions	Pattern matching, validation, text extraction

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📘 Lab Summaries

lab0 — Basics

• Playback speed adjustment
• Indoor voice conversion
• Einstein’s mass‑energy equation
• Tip calculator
• Simple string transformations

lab1 — Conditionals

• Bank greeting logic
• Deep thought checker
• File extension detection
• Meal time classification
• Basic interpreter

lab2 — Loops

• CamelCase conversion
• Coke machine simulation
• Nutrition label lookup
• Vanity plate validation
• Twitter vowel removal

lab3 — Exceptions & File I/O

• Fuel gauge with error handling
• Grocery list aggregator
• Date parsing and normalization
• Taqueria order calculator

lab4 — Libraries

• Adieu farewell generator
• Bitcoin price lookup (API)
• Emoji converter
• Figlet text renderer
• Number guessing game
• Professor quiz generator

lab5 — Unit Tests

• Bank greeting tests
• Fuel gauge tests
• Vanity plate tests
• Twitter vowel removal tests

lab6 — File I/O

• Lines of Code Count the number of lines in a Python file,
• Pizza Read a CSV file of pizza orders
• Scourgify Clean and normalize a messy CSV
• CS50 P‑Shirt Overlay text onto an image using PIL

lab7 — Regular Expressions

• NUMB3RS Validate IPv4 addresses using regex
• Watch on YouTube Extract YouTube video IDs from iframe HTML
• Working 9 to 5 Convert 12‑hour time ranges into 24‑hour format
• Regular, um, Expressions Count occurrences of the standalone word "um"
• Response Validation Validate email addresses

🏁 Course Progress

This repository currently includes labs 0 through 7, all of which have been:

• fully implemented
• tested with pytest where applicable
• validated with CS50’s autograder
• organized into a clean, modular structure

Additional labs will be added as I continue progressing through the course.

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📌 Notes

• All work was completed locally on a Linux workstation using virtual environments.
• Each lab folder contains its own README and supporting files.
• This repo is part of a broader academic progression alongside CS50AI and future AI agent projects.

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