Interactive Data Cleaning Exercise
This dataset has 4 common data quality issues. Clean them step-by-step or all at once and see the results!
Cleaning Progress
0/41
Duplicate Records
Row 5 is a duplicate of row 1 (John Doe)
2
Missing Values
Age missing for Eve Wilson, City missing for jane smith, Salary missing for Charlie Davis
3
Inconsistent Formatting
Name casing inconsistent (jane smith vs John Doe)
4
Extra Whitespace
Leading/trailing spaces in names and cities
Current Dataset
DuplicateMissingFormattingWhitespace
| Name | Age | City | Salary |
|---|---|---|---|
| John Doe | 25 | NYC | 50000 |
| jane smith | 30 | (empty) | 65000 |
| Bob Johnson | null | Boston | 75000 |
| Alice Brown | 28 | NYC | 60000 |
| john doe | 25 | NYC | 50000 |
| Charlie Davis | 35 | Chicago | null |
| Eve Wilson | (empty) | Seattle | 70000 |
| Frank Miller | 45 | Los Angeles | 85000 |
| Grace Lee | 32 | San Francisco | 90000 |
| Henry Clark | 29 | NYC | 55000 |
💡 Learning Points
- Duplicates: Can inflate counts and skew analysis. Always check for and remove exact or near-duplicate records.
- Missing Values: Decide to delete rows, fill with mean/median, or mark as "Unknown" based on context.
- Formatting: Inconsistent casing makes grouping and searching difficult. Standardize early.
- Whitespace: Leading/trailing spaces cause matching failures. Always trim text fields.