Find missing rows between two CSV files
Find rows present in a source CSV but missing from a target CSV (or vice versa) by matching on a key column such as email, id, or sku.
Open the tool, then paste the sample input below. Everything runs locally in your browser.
The problem
After a migration, export, or sync you often need to answer one question: which records did not make it across? Matching on a key column isolates exactly the rows that exist on one side and not the other.
Sample input
email,name
asha@example.com,Asha
ravi@example.com,Ravi
meera@example.com,Meera
email,name
asha@example.com,Asha
meera@example.com,Meera
Expected output
email,name
ravi@example.com,Ravi
ravi@example.com exists in the source but not the target, so it is reported as missing.
How to do it
- Paste the source CSV.
- Paste the target CSV.
- Select the key column, such as
email. - Run the comparison.
- Export or copy the missing rows.
Common mistakes
- Key values with leading or trailing spaces that prevent matches.
- Case differences in emails or IDs.
- Different delimiters between the two files.
- Header names that do not match across files.
- Duplicate records that hide genuinely missing rows.
Related tools
Related guides
FAQ
How do I find missing rows in CSV?
Load both files, choose the key column, and run the comparison. Rows present under a key in one file but not the other are the missing rows.
Can I compare by email or SKU?
Yes. Any column with stable, unique values works as the key, including email, SKU, id, or order number.
What if the column names are different?
Map the matching column on each side as the key. Non-key columns are compared by header name.
Can I export only missing rows?
Yes. Filter to the only-in-source (or only-in-target) result and copy or download just those rows.
How do I handle duplicate keys?
Resolve or deduplicate them first; duplicates can hide missing rows and distort the comparison.
Finding missing rows runs locally in your browser. Your CSV files are not uploaded.
Compare CSV files, find missing rows, clean lists and build SQL filters — grouped in one place.