Sample CSV With Duplicate Rows — Free Download
60 unique customer rows, plus 12 exact duplicates and 8 near-duplicates that differ only by leading spaces or capitalised email — shuffled together. The file for testing how your dedupe actually behaves.
Download the sample file
CSV · 80 rows · 4.0 KB · synthetic data, free for any use
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| customer_id | name | plan | mrr | |
|---|---|---|---|---|
| C-020 | Sofia Al-Sayed | [email protected] | team | 171 |
| C-053 | Sofia Shah | [email protected] | pro | 110 |
| C-015 | Omar Bennett | [email protected] | free | 336 |
| C-017 | Marco Bianchi | [email protected] | team | 13 |
| C-009 | Sam Tanaka | [email protected] | team | 383 |
| C-007 | Elena Petrov | [email protected] | pro | 305 |
Showing the first 6 of 80 rows.
Columns
| Column | Type | Description |
|---|---|---|
customer_id | string | C-001… — repeated on duplicate rows |
name | string | Full name; near-duplicates add surrounding spaces |
email | string | Near-duplicates uppercase this |
plan | string | free / pro / team |
mrr | integer | Monthly recurring revenue, USD |
What makes this file useful
- 12 rows are byte-for-byte duplicates — a plain "remove duplicate rows" pass should drop exactly these.
- 8 rows are near-duplicates: same customer_id and mrr, but name has padding and email is uppercased. Exact dedupe keeps them; normalised dedupe removes them.
- The ground truth is 60 unique customers (C-001 to C-060), so you can score any dedupe approach precisely.
Use cases
- Count exact duplicate rows and remove them
- Catch near-duplicates by normalising whitespace and case first
- Compare a strict key-based dedupe with a fuzzy match
- Verify your row count: 80 in, 60 unique customers
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Related guides
More sample datasets
Frequently asked questions
How many duplicates are there?
80 rows total: 60 unique, 12 exact duplicates, 8 near-duplicates. The correct deduplicated output has 60 rows.
What is a near-duplicate here?
A row for a customer that already appears, but with extra spaces around the name or the email in a different case. Same identity, different bytes.
Which tool removes exact duplicates?
Use Logical Dedupe for exact and rule-based removal, or Fuzzy Dedupe when the differences are typos and formatting.
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