Tools: How I Built a CSV Data Cleaner in 4 Days (Python Beginner Working Project)

Background ## The Challenge ## What I Built ## The Journey (Day by Day) ## Day 1-2: Python Fundamentals ## Day 3: Building the Core ## Day 4: Integration & Testing ## Key Code Sections ## The Validation Pattern ## The Main Loop ## What I Learned ## Mistakes I Made ## The Results ## What's Next ## Resources That Helped ## Takeaways for Aspiring Developers ## The Code After 2+ years in QA (Meta, Microsoft) and RPA consulting, I decided to transition to automation engineering. This is my first Python project, built in 4 days, documented completely. Build a production-ready CSV cleaner that: [Screenshot of your terminal output] A Python script that: ✅ Cleans 1000+ contacts in seconds ✅ Validates emails, phones, names, ages ✅ Separates valid from invalid data ✅ Generates detailed error reports QA Mindset Applied to Code: Initially tried to do everything in one function Forgot error handling on type conversions Wanted to make it "perfect" before shipping Real-World Performance: Full project on GitHub: https://github.com/jaber17/csv-contact-cleaner/tree/main Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to ? It will become hidden in your post, but will still be visible via the comment's permalink. as well , this person and/or COMMAND_BLOCK: def validate_email(email): """Check email structure""" errors = [] if "@" not in email: errors.append("Missing @") # More checks... return errors COMMAND_BLOCK: def validate_email(email): """Check email structure""" errors = [] if "@" not in email: errors.append("Missing @") # More checks... return errors COMMAND_BLOCK: def validate_email(email): """Check email structure""" errors = [] if "@" not in email: errors.append("Missing @") # More checks... return errors COMMAND_BLOCK: for row_num, row in enumerate(reader, start=2): all_errors = [] # Clean cleaned_name = clean_name(row.get("Name", "")) # Validate all_errors.extend(validate_name(cleaned_name)) # Decide if all_errors: error_contacts.append(...) else: clean_contacts.append(...) COMMAND_BLOCK: for row_num, row in enumerate(reader, start=2): all_errors = [] # Clean cleaned_name = clean_name(row.get("Name", "")) # Validate all_errors.extend(validate_name(cleaned_name)) # Decide if all_errors: error_contacts.append(...) else: clean_contacts.append(...) COMMAND_BLOCK: for row_num, row in enumerate(reader, start=2): all_errors = [] # Clean cleaned_name = clean_name(row.get("Name", "")) # Validate all_errors.extend(validate_name(cleaned_name)) # Decide if all_errors: error_contacts.append(...) else: clean_contacts.append(...) - Never loses data (even invalid entries) - Provides detailed error reports - Handles real-world messy data - Uses quality-first principles - Variables, strings, functions - Dictionaries and lists - CSV file handling - Hardest part: Understanding loops and data flow - Wrote 8 cleaning & validation functions - Implemented error handling - Breakthrough moment: Realizing each function should return errors as a list - Combined all functions - Added file writing - Tested with messy data - Key learning: Separation of concerns (cleaning vs validation) - Returns a list (can collect multiple errors) - Clear error messages - Easy to extend - Python fundamentals - CSV processing - Error handling patterns - Function design for reusability - How to learn efficiently (fundamentals before frameworks) - How to debug systematically - How to write readable code - How to document your work - Test edge cases (empty strings, None values) - Detailed error reporting - Data integrity (never lose information) - Clear documentation - Initially tried to do everything in one function Solution: Split into cleaning and validation - Solution: Split into cleaning and validation - Forgot error handling on type conversions Solution: try/except blocks everywhere - Solution: try/except blocks everywhere - Wanted to make it "perfect" before shipping Solution: Ship working version, iterate later - Solution: Ship working version, iterate later - Solution: Split into cleaning and validation - Solution: try/except blocks everywhere - Solution: Ship working version, iterate later - ~200 lines of code - 8 functions - 4 days start to finish - 100% written by myself (with learning resources) - 1,000 rows: < 1 second - 10,000 rows: ~3 seconds - Handles all edge cases gracefully - Build n8n workflow automation - Learn Pandas (see how professionals do this) - Add more validation features - 4-6 portfolio projects - First freelance automation work - Technical blog (weekly updates) - Full-time automation engineer role - Specialize in workflow automation - Help others transition to tech - Python documentation - Stack Overflow for specific syntax - ChatGPT for explaining concepts - Key insight: Learn fundamentals BEFORE frameworks - Start ugly, refine later - Working code beats perfect code - Build in public - Accountability and feedback accelerate growth - QA/testing experience is valuable - Quality mindset transfers to code - 4 days is enough - You don't need months to build something real - Use it for your projects - Suggest improvements - Ask questions in comments