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 hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse COMMAND_BLOCK: def validate_email(email): """Check email structure""" errors = [] if "@" not in email: errors.append("Missing @") # More checks... return errors Enter fullscreen mode Exit fullscreen mode 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(...) Enter fullscreen mode Exit fullscreen mode 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