Download sample CSV files for spreadsheet imports, upload forms, parser checks, and data-pipeline tests. Choose fixed 10, 100, or 1,000 row examples, or inspect fixtures for quoted values, semicolon delimiters, UTF-8 BOM, and files without a header. Larger 10,000 and 100,000 row datasets are generated locally when requested. Every row uses fictional data, making the files suitable for repeatable demonstrations without including a real customer export.
sample-10-rows.csv
A compact table with a header for a first import check.
440 B · Data records: 10
id,name,email,active
1,Example User 1,user1@example.com,false
2,Example User 2,user2@example.com,true
3,Example User 3,user3@example.com,false
4,Example User 4,user4@example.com,true
5,Example User 5,user5@example.com,falsesample-100-rows.csv
A fixed fictional dataset for import and pagination tests.
4.4 KB · Data records: 100
id,name,email,active
1,Example User 1,user1@example.com,false
2,Example User 2,user2@example.com,true
3,Example User 3,user3@example.com,false
4,Example User 4,user4@example.com,true
5,Example User 5,user5@example.com,falsesample-1000-rows.csv
A larger static table for repeatable pipeline checks.
47.2 KB · Data records: 1,000
id,name,email,active
1,Example User 1,user1@example.com,false
2,Example User 2,user2@example.com,true
3,Example User 3,user3@example.com,false
4,Example User 4,user4@example.com,true
5,Example User 5,user5@example.com,falsequoted-fields.csv
Commas, quotes, and embedded newlines for CSV-aware parsers.
179 B · Data records: 2
id,name,email,notes
1,"Example, Ada",ada@example.com,"Said ""hello""
and continued on a new line."
2,"Example ""Bo""",bo@example.com,"Comma, quote "" and Unicode café 你好"
semicolon.csv
A semicolon-separated table for regional spreadsheet imports.
440 B · Data records: 10
id;name;email;active
1;Example User 1;user1@example.com;false
2;Example User 2;user2@example.com;true
3;Example User 3;user3@example.com;false
4;Example User 4;user4@example.com;true
5;Example User 5;user5@example.com;falseutf8-bom.csv
Multilingual CSV with a BOM for encoding-detection tests.
240 B · Data records: 5
id,name,email,active
5,Example Café 你好,unicode@example.com,true
1,Example User 1,user1@example.com,false
2,Example User 2,user2@example.com,true
3,Example User 3,user3@example.com,false
4,Example User 4,user4@example.com,true
no-header.csv
Data starts in the first record; configure import accordingly.
418 B · Data records: 10
1,Example User 1,user1@example.com,false
2,Example User 2,user2@example.com,true
3,Example User 3,user3@example.com,false
4,Example User 4,user4@example.com,true
5,Example User 5,user5@example.com,falsesample-10000-rows.csv
Generate a fictional table locally for larger import tests.
~501.7 KB · Data records: 10,000
Generated locally in your browser
sample-100000-rows.csv
Generate a large fictional table locally to check volume handling.
~5.3 MB · Data records: 100,000
Generated locally in your browser
Choose a fixture that matches the behavior you need to test.
A CSV record is a row of fields separated by a delimiter, commonly a comma. Many applications treat the first record as column names, but that is a convention rather than a universal requirement. The no-header fixture helps check whether an importer incorrectly drops the first data record. Before comparing row totals, decide whether your application counts the header. The record count displayed here describes data rows, not the number of physical newline characters.
The escaping example contains commas, quotation marks, and embedded line breaks inside fields. A field containing a delimiter or newline is enclosed in double quotes; an internal double quote is represented by two double quotes. Consequently, splitting the text at every comma or newline will misread this valid fixture. Use a CSV parser that understands quoting, and check the recovered field values rather than expecting one physical line to equal one table row.
UTF-8 supports multilingual text. A UTF-8 BOM adds the bytes EF BB BF at the beginning and can help some spreadsheet software detect the encoding. An importer that fails to remove it may see an unexpected character in the first column name. The semicolon fixture supports tests for regional spreadsheet settings where commas may be decimal separators. Choose the delimiter explicitly during import if automatic detection selects the wrong table structure.
Use a small fixed fixture to inspect column names, types, and quoting before generating many rows. The 10,000 and 100,000 row options exercise different workloads from the 10 row example; they are not evidence that a spreadsheet or server can accept every larger dataset. Generation and parsing both consume device memory. Measure actual byte size and elapsed time, and keep other variables constant when comparing performance results.
Start small, inspect the structure, then test larger inputs.
Start with the row count or edge case relevant to your import. Review the card’s filename, bytes, and data-row count, then expand the preview. Choose escaping, semicolon, BOM, or no header deliberately, because a successful import with the simplest comma-separated file does not prove those other cases work.
Download the file and select its encoding, delimiter, and header handling in the destination application. Treat identifier and postal-code columns as text when leading zeros matter. Confirm that dates and numbers were not silently reformatted by regional spreadsheet settings. Keep the original download unchanged so you can distinguish import behavior from an edited fixture.
Compare the imported data-row total, column count, and representative cell values with the fixture. For the escaping example, verify the comma and newline remain inside their intended field. Only after those checks should you generate 10,000 or 100,000 rows to test progress indicators, timeouts, upload limits, and recovery from an interrupted operation.
Answers about the supplied fixtures and their limits.
The supplied data is fictional, with example.com email addresses rather than real contact details. It is intended for development and demonstrations. It does not describe real customers, and it is not a representative business dataset. When adapting a fixture for your own system, avoid replacing the fictional values with sensitive production information in a public bug report.
The selected delimiter may not match the file. Import the comma-separated samples with comma as the delimiter and the semicolon sample with semicolon. Regional defaults and automatic detection can choose differently. Use the import dialog rather than relying only on double-click behavior, then check quoted fields so a quick delimiter fix does not hide another parsing problem.
Not always. A quoted field can contain a line break without ending its record, as the escaping sample demonstrates. A file may also have a header and a final line ending. Count records with a CSV-aware parser and state whether the header is included. A plain text line counter is useful for inspecting bytes but is not an authoritative CSV row counter.
Use the form required by the receiving software. UTF-8 without BOM works well in many developer tools, while a BOM can improve encoding detection in some spreadsheet workflows. Both represent the same text when decoded correctly. Test the BOM fixture specifically if your importer matches column names exactly, because an unhandled BOM can become part of the first header.
The preview is useful for quickly checking the structure, and the copy action copies the complete static file text. Download the actual file for a full import, an exact row count, or a byte-size test. Pasting text into a spreadsheet can also trigger different type inference from importing a file, so keep those test paths distinct.
The CSV file generator creates random test data using a fixed set of columns, with row-count and approximate-size controls. It does not accept a custom business schema. If a transport requires Base64, use File to Base64 and Base64 to File for the reverse operation. Base64 changes the transport representation, not the table schema, and increases data size. It does not validate CSV fields or decide application data types.
Practical ways to use this tool.
Explore another conversion or file tool.