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

Ahmed Jaber Choudhury — Tue, 24 Feb 2026 12:25:01 +0000

Background

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.

The Challenge

Build a production-ready CSV cleaner that:

Never loses data (even invalid entries)
Provides detailed error reports
Handles real-world messy data
Uses quality-first principles

What I Built

[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

The Journey (Day by Day)

Day 1-2: Python Fundamentals

Variables, strings, functions
Dictionaries and lists
CSV file handling
Hardest part: Understanding loops and data flow

Day 3: Building the Core

Wrote 8 cleaning & validation functions
Implemented error handling
Breakthrough moment: Realizing each function should return errors as a list

Day 4: Integration & Testing

Combined all functions
Added file writing
Tested with messy data
Key learning: Separation of concerns (cleaning vs validation)

Key Code Sections

The Validation Pattern

def validate_email(email):
    """Check email structure"""
    errors = []

    if "@" not in email:
        errors.append("Missing @")

    # More checks...

    return errors

This pattern:

Returns a list (can collect multiple errors)
Clear error messages
Easy to extend

The Main Loop

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(...)

What I Learned

Technical Skills:

Python fundamentals
CSV processing
Error handling patterns
Function design for reusability

Meta-Skills:

How to learn efficiently (fundamentals before frameworks)
How to debug systematically
How to write readable code
How to document your work

QA Mindset Applied to Code:

Test edge cases (empty strings, None values)
Detailed error reporting
Data integrity (never lose information)
Clear documentation

Mistakes I Made

Initially tried to do everything in one function
- Solution: Split into cleaning and validation
Forgot error handling on type conversions
- Solution: try/except blocks everywhere
Wanted to make it "perfect" before shipping
- Solution: Ship working version, iterate later

The Results

Project Stats:

~200 lines of code
8 functions
4 days start to finish
100% written by myself (with learning resources)

Real-World Performance:

1,000 rows: < 1 second
10,000 rows: ~3 seconds
Handles all edge cases gracefully

What's Next

Short term:

Build n8n workflow automation
Learn Pandas (see how professionals do this)
Add more validation features

Medium term:

4-6 portfolio projects
First freelance automation work
Technical blog (weekly updates)

Long term:

Full-time automation engineer role
Specialize in workflow automation
Help others transition to tech

Resources That Helped

Python documentation
Stack Overflow for specific syntax
ChatGPT for explaining concepts
Key insight: Learn fundamentals BEFORE frameworks

Takeaways for Aspiring Developers

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

The Code

Full project on GitHub: https://github.com/jaber17/csv-contact-cleaner/tree/main

Feel free to:

Use it for your projects
Suggest improvements
Ask questions in comments

DEV Community: Ahmed Jaber Choudhury