12. Clean and Validate CSV Data
1. Hello, World!
2. Rename Photos by Capture Date
3. Organize Downloads Folder by File Type
4. Delete Old Log Files Automatically
5. Find Duplicate Files Using MD5 Hashing
6. Generate Comprehensive File Inventory
7. Parse Web Server Logs for Performance Metrics
8. Extract Emails, URLs, and Phone Numbers from Text
9. Track Daily and Monthly Active Users from Apache Logs
10. Generate Formatted Text Reports with Column Alignment
11. Analyze Sales Transactions and Generate Revenue Reports
12. Clean and Validate CSV Data
13. Convert JSON API Responses to CSV
14. Parse and Validate Configuration Files
15. Build a CLI Calculator with Argument Parsing
16. Parse and Display Weather Data from JSON
17. Build a Simple Inventory System with Classes
18. Generate Markdown Table from CSV Data
19. Aggregate Sales Data by Time Period
20. Recursive Directory Size Calculator with Filtering
21. Parse and Transform Server Configuration
22. Find and Replace Across Multiple Files
23. Build a Simple Template Engine
24. CSV Data Validator with Business Rules
25. Build a Multi-Format Log Analyzer

Challenge

Problem

Web forms produce inconsistent data, and importing customer records from external systems requires normalization. This drill teaches you to clean messy CSV data by stripping whitespace, normalizing phone formats, standardizing emails, validating required fields, removing duplicates, and handling malformed rows. You'll learn data validation patterns essential for preventing SQL errors and data corruption. Note: This drill parses CSV manually using string methods rather than Ruby's CSV library.

Difficulty: Intermediate

Instructions

  1. Read user-submitted CSV file with headers (Name, Email, Phone, Company)
  2. Clean each field:
    • Strip leading/trailing whitespace
    • Normalize emails: lowercase and trim
    • Normalize phone: remove formatting, keep digits only, format as XXX-XXX-XXXX
    • Capitalize company names properly
  3. Validate required fields (Name and Email cannot be empty)
  4. Validate email format (contains @ and .)
  5. Remove duplicate rows (by email)
  6. Print summary: X rows processed, Y cleaned, Z errors
  7. Report duplicates removed and errors found

Files

Editable
Read-only

Hints

Hint 1

String#strip removes leading/trailing whitespace

Hint 2

String#downcase converts to lowercase

Hint 3

String#gsub(/\D/, '') removes all non-digit characters

Hint 4

Use hash to track seen emails: seen_emails[email] = true

Hint 5

Validate email with email.include?('@') && email.include?('.')

Hint 6

Split company name, capitalize each word: .split.map(&:capitalize).join(' ')

Ruby 3.4

Provided Files (Read-only)

1. Correct row counts

Input: 
clean_csv('dirty_data.csv')
Expected Output: 
Processed 5 rows: 2 cleaned, 2 errors
1 duplicate removed
Errors found

2. Detects missing name error

Input: 
clean_csv('dirty_data.csv')
puts 'Validation OK'
Expected Output: 
Processed 5 rows: 2 cleaned, 2 errors
1 duplicate removed
Errors found
Validation OK

3. Detects invalid email format

Input: 
clean_csv('dirty_data.csv')
puts 'Email check OK'
Expected Output: 
Processed 5 rows: 2 cleaned, 2 errors
1 duplicate removed
Errors found
Email check OK
+ 2 hidden test cases