Lab 2 — Output Validator

This tool checks the cleaned-dataset CSV your script produced in Lab 2, Part 2. Upload your lab_02_cleaned.csv and it looks for the mistakes a cleaning script most often makes: leftover header rows, page-break junk, an Amount column that is still text, stray characters in the user column. Then it gives you a short report.

It runs entirely in your browser. Your file is never uploaded anywhere. The report is shown as an image: read it, then go fix your script or your specification yourself. Handing the report to an AI to fix for you skips the part of the lab that is actually graded.

Drop your lab_02_cleaned.csv here

or

Your report

What this checks

Eight checks, in this order. Each one comes back as either a pass or an issue with a short note on the likely cause. Nothing here grades your work, and none of it looks at your specification or your script. It reads the cleaned data only.

CheckWhat passesWhat it usually means when it fails
File reads The file parses as CSV and has at least one data row under the header The file was saved as something other than CSV, or only the header survived
Column count Exactly 10 columns More than 10 usually means the Amount column got split or a stray delimiter was kept. Fewer means a column was dropped during cleaning
Row count Between 23,000 and 24,000 data rows High means junk rows were not removed. Low means real data rows were deleted, usually by removing too many top rows
Header rows No row inside the data repeats the column-header line The export repeats its header every page, and those repeats were left in
Page-break junk No page-break or near-empty rows left The report's page furniture was treated as data
Amount column Every value is a clean number Amounts are still text, usually with a currency symbol, a comma, or parentheses for negatives still attached
ID column No stray characters in the ID values Whitespace or punctuation came along with the ID and will break a per-user count
Blank rows No completely empty rows Usually trailing rows at the end of the file, which you can see for yourself by scrolling to the bottom

The row count is the one worth knowing before you start, because it is the check that tells you whether you cut too much or too little.

Related: pandas primer · encoding primer · back to the Lab 2 page

ACCTG 6155, Week 2. The dataset and cleaning task adapt the Spring 2026 Lab 2, originally authored by Mac Gaulin.