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Sample CSV Files — Free Download in Various Sizes and Row Counts

Download free sample CSV files from 100 to 1 million rows. Includes realistic data: users, products, transactions, geolocation, and timeseries. Perfect for testing data imports, pandas, databases, and spreadsheets. No registration needed.

Data disclaimer: All records are randomly generated with faker.js. Email addresses use RFC 2606 reserved domains (example.com, example.org, example.net) that are guaranteed never to be real. Any resemblance of names, cities, or other fields to real persons is coincidental.

sample-100-rows.csv

7.7 KB

100 rows

csv
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sample-1000-rows.csv

77.7 KB

1,000 rows

csv
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sample-10000-rows.csv

783 KB

10,000 rows

csv
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sample-100000-rows.csv

7.74 MB

100,000 rows

csv
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sample-1million-rows.csv

78.35 MB

1,000,000 rows

csv
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users-sample.csv

59.4 KB

1,000 rows

csv
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products-sample.csv

19.5 KB

500 rows

csv
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transactions-sample.csv

73.9 KB

2,000 rows

csv
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employees-sample.csv

21.8 KB

500 rows

csv
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geolocation-sample.csv

44.2 KB

1,000 rows

csv
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timeseries-sample.csv

142 KB

5,000 rows

csv
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semicolon-separated.csv

59.4 KB

1,000 rows

csv
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tab-separated.tsv

59.2 KB

1,000 rows

tsv
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Common use cases for sample CSV files

  • Testing CSV parsers and data import pipelines
  • Seeding databases with realistic test data
  • Pandas / data science testing and prototyping
  • Testing spreadsheet applications with large datasets
  • Load testing ETL pipelines and data warehouses
  • Verifying file upload size limits with large CSVs
  • Testing delimiter handling (comma, semicolon, tab)

CSV file structure

Standard CSV (comma-separated)

id,name,email,city,age
1,Alice Johnson,alice@example.com,New York,28
2,Bob Smith,bob@example.com,London,34

Semicolon-separated (European format)

id;name;email;city;age
1;Alice Johnson;alice@example.com;New York;28

Tab-separated (TSV)

id	name	email	city	age
1	Alice Johnson	alice@example.com	New York	28

Loading in Python (pandas)

import pandas as pd
df = pd.read_csv('users-sample.csv')
print(df.head())

Frequently Asked Questions

Other document formats