Quick Answer
Duplicate rows and bad columns can quietly damage reports, imports, customer lists, and product feeds. A quick profile pass helps catch the problems before the CSV moves downstream.
Step-by-Step
- Profile the CSV to understand row count, columns, blank values, and likely data types.
- Check for duplicate rows or repeated keys such as email, SKU, order ID, or product ID.
- Look for columns with mixed data types, strange date formats, or unexpected empty values.
- Normalize field names so later mapping and formulas are easier.
- Create a cleaned sample and compare it against the original before import.
Recommended Workflow
Open the most relevant calculator or utility first, enter a realistic starting point, then use the supporting tools to check assumptions, clean inputs, or prepare the final output.
FAQs
What counts as a duplicate row?
It depends on the job. Sometimes the entire row must match; other times a key field like email or SKU should be unique.
What is a bad column?
A bad column may have inconsistent types, missing required values, unclear headers, mixed formats, or values that do not match the destination system.
Should I delete duplicates automatically?
Not before reviewing them. Some repeated rows may represent legitimate separate transactions.