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CSV · 5 files

Sample CSV files - free tabular dataset downloads

Download realistic sample CSV files with diverse row volumes and structured tabular columns. RFC 4180 compliant with UTF-8 encoding, comma delimiters, and escaped fields for spreadsheet import, parser benchmark, and database migration testing.

5 files CSV 10 - 10,000 rows RFC 4180 · UTF-8 Comma Delimited

CSV dataset library

Download sample CSV files by row count and size

5 files

10 rows

1.1 KB

10 rows CSV sample dataset (1.1 KB)

CSV Download CSV

100 rows

10.6 KB

100 rows CSV sample dataset (10.6 KB)

CSV Download CSV

1,000 rows

106.5 KB

1,000 rows CSV sample dataset (106.5 KB)

CSV Download CSV

5,000 rows

536.7 KB

5,000 rows CSV sample dataset (536.7 KB)

CSV Download CSV

10,000 rows

1.05 MB

10,000 rows CSV sample dataset (1.05 MB)

CSV Download CSV

CSV testing guide

How to use sample CSV files for parsers, spreadsheet imports, and database seeding

Comma-Separated Values (CSV) is the universal format for structured tabular data. These safe, synthetic test files let you validate RFC 4180 parsing, memory usage, streaming chunks, and boundary edge cases without risking sensitive data.

What data is included in these sample CSV files?

Every CSV download contains a structured schema: id, first_name, last_name, email, company, city, country, balance, signup_date, and status. Strings with commas (such as company legal names) are properly quoted according to RFC 4180 specifications.

Choose the 10-row or 100-row file for fast UI previews and unit tests. Use the 1,000-row dataset for standard integration tests, and test batch processing limits with the 5,000 and 10,000-row datasets.

Checklist for robust CSV import testing

  • ✓ Verify your parser handles RFC 4180 quotes around fields containing commas or quotes.
  • ✓ Check header row detection and automatic column type inference (integer, string, date, decimal, enum).
  • ✓ Test line terminator compatibility (standard LF \n and CRLF \r\n).
  • ✓ Ensure proper UTF-8 decoding for international characters and city names.

Frequently asked questions

What is a CSV file?

A CSV (Comma-Separated Values) file is a plain text file that stores tabular data. Each line represents a record, and fields within the line are separated by commas according to RFC 4180 standards.

Are these sample CSV files safe to use in automated tests?

Yes. All data records are purely synthetic, containing no real personal identifiable information (PII). They are safe for open-source test suites, CI/CD pipelines, demos, and local development.

What delimiters and character encoding are used?

All samples use standard comma (,) separators and UTF-8 encoding without BOM, ensuring compatibility across all modern programming languages and spreadsheet applications.

Can I open these files in Microsoft Excel or Google Sheets?

Yes. CSV files are natively supported by Microsoft Excel, Google Sheets, Apple Numbers, LibreOffice Calc, and data science environments like Python Pandas and R.

CSV format specifications

Extension

.csv

MIME type

text/csv

Encoding & standard

UTF-8 (RFC 4180)

CSV test datasets: row counts, RFC 4180 compliance, and benchmark practices

Whether verifying a front-end drag-and-drop importer or benchmarking batch processing in a backend pipeline, selecting the right sample CSV volume is critical for realistic test coverage.

Choosing the right CSV dataset size

Use the 10-row or 100-row CSV file for quick smoke tests, UI table rendering, and unit tests. The 1,000-row sample is ideal for end-to-end import testing, while the 5,000 and 10,000-row files are designed to test memory limits, asynchronous worker queues, and streaming parsers.

RFC 4180 standards & edge cases

Real-world CSV data frequently breaks naive split(",") implementations because fields often contain commas, quotes, and newlines. Our sample datasets incorporate properly quoted multi-word strings and corporate entity names to help verify your parser is genuinely RFC 4180 compliant.

Database seeding & performance profiling

Testing database inserts with synthetic CSV files ensures your database triggers, batch size limits, and index rebuild times are tested against realistic relational schemas without leaking production or customer records.