Dummy Data Generator
Generate up to 100,000 rows of realistic fake data as CSV, JSON, NDJSON or SQL. Seeded and reproducible, 100% in your browser. No signup.
CSV · JSON · NDJSON · SQL — up to 100,000 rows, generated in your browser. Set a seed to get the same data every time.
Saved in this browser only — clearing site data or switching devices removes it. Use Export to keep a backup.
Columns
6 / 30 columnsGeneration
Clamping moves the real mean and standard deviation away from μ and σ.
Some columns fell back to placeholder values.
ML options — distributions, correlation, target label, missing values
Chained correlation multiplies: corr(A, C) = ρ(A, B) × ρ(B, C).
Names, addresses and companies are fictional and may combine real place names.
Preview
First 20 rows| id | name | age | signup_date | is_active | |
|---|---|---|---|---|---|
| 1 | sample_1 | coralranger36@example.com | 23 | 2021-03-03 | true |
| 2 | sample_2 | storm2140@gmail.com | 18 | 2025-08-25 | true |
| 3 | sample_3 | orbit3790@example.com | 22 | 2021-07-02 | true |
| 4 | sample_4 | jolly3297@hotmail.com | 43 | 2023-03-14 | true |
| 5 | sample_5 | query4288@gmail.com | 55 | 2022-07-17 | true |
| 6 | sample_6 | modulelantern57@yahoo.com | 43 | 2020-03-08 | true |
| 7 | sample_7 | pixelcloud9@yahoo.com | 36 | 2025-11-19 | true |
| 8 | sample_8 | ottertoken2@yahoo.com | 26 | 2020-05-28 | true |
| 9 | sample_9 | deltaharbor3@gmail.com | 52 | 2024-09-08 | true |
| 10 | sample_10 | wittyhelix33@hotmail.com | 19 | 2022-02-12 | true |
| 11 | sample_11 | river2473@outlook.com | 56 | 2025-02-13 | true |
| 12 | sample_12 | violet8035@example.com | 34 | 2025-03-21 | true |
| 13 | sample_13 | shade676@hotmail.com | 28 | 2023-09-02 | true |
| 14 | sample_14 | jade4728@hotmail.com | 32 | 2021-11-24 | false |
| 15 | sample_15 | cloudhalo12@yahoo.com | 40 | 2020-04-20 | true |
| 16 | sample_16 | flint1444@example.com | 49 | 2021-06-02 | true |
| 17 | sample_17 | wolfstone24@hotmail.com | 33 | 2025-06-10 | false |
| 18 | sample_18 | halo1340@yahoo.com | 41 | 2023-10-25 | false |
| 19 | sample_19 | scalar774@outlook.com | 27 | 2022-01-14 | true |
| 20 | sample_20 | vector4769@hotmail.com | 38 | 2020-04-29 | true |
∅ marks a missing value — the file itself uses the missing-value style below.
Output
The BOM is what stops Excel from garbling UTF-8 — leave it on for spreadsheets.
Values starting with = + - @ can be executed as formulas when opened in a spreadsheet.
import pandas as pd
df = pd.read_csv("dummy-data.csv")
df.head()How to use
- Add columns and pick a data type for each.
- Set the row count and the language (English or Korean).
- Optional: open ML options for distributions, correlation, missing values and a target label.
- Choose a format and download or copy.
- Optional: set a seed and share the address bar — the other person gets the same data.
FAQ
Is the generated data sent to a server?
No. The schema, the seed and every generated row stay in your browser — there is no upload step and no API call. That matters more than it sounds: column names are often a sketch of an internal database design, and services that generate data on their servers see both the schema and the result.
How many rows can I generate for free?
Up to 100,000 rows, with no account. Tools that generate on a server usually stop at 1,000–10,000 rows because every row costs them CPU time; here the work happens on your machine, so the limit is only about keeping the page responsive.
Why is my Korean CSV garbled in Excel?
Excel does not auto-detect UTF-8 in a plain .csv file; it falls back to the system code page, which turns Korean into mojibake. A UTF-8 BOM at the start of the file tells Excel what the encoding is. The BOM option is on by default here, and it is added to CSV and TSV only — never to JSON, NDJSON or SQL, where it would break parsers.
Can I get exactly the same data again?
Yes — type a seed and share the address bar. The schema travels in the link too, so anyone opening it downloads a byte-identical file. Each column also has its own random stream, which means adding, removing or renaming a column leaves the other columns untouched.
Can I use this for machine-learning practice?
That is what the ML options are for: normal and log-normal distributions, a correlation ρ between numeric columns, per-column missing values, and a binary target label whose intercept is solved so the positive rate you ask for is the rate you get — label noise included. It covers most of what scikit-learn’s make_classification is used for, but with readable column names and a CSV you can open.
What is NDJSON (JSONL)?
One JSON object per line, with no wrapping array. It streams line by line, so it is the usual format for LLM fine-tuning datasets, BigQuery and Bag-style data loaders, and pandas reads it with pd.read_json(path, lines=True).
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