A clean, interactive Streamlit GUI for the Faker library — generate fake datasets easily and export them as CSV.
Try a GUI version of Faker using the Streamlit web app: Launch App
- ⚙️ Configure number of rows and columns
- 🧬 Per-column data type selection from Faker or manual format (e.g.
$###,###.##) - 🔁 Optionally restrict each field to a number of unique values
- 📄 Preview and download data as CSV
- 🧠 Uses the powerful Faker library for realistic fake data generation
git clone https://github.com/zachpinto/faker-streamlit.git
cd faker-streamlit
pip install -r requirements.txt
streamlit run app.py- Python 3.8+
- Streamlit 1.45.0+
- Faker
- pandas
See requirements.txt for specific version constraints.
This app is built on top of the official Faker Python library.
Faker generates fake but realistic-looking data for a wide range of use cases, including:
- Personal identity: names, addresses, emails, phone numbers
- Internet and tech: usernames, IP addresses, domains, MACs
- Financial: credit cards, currencies, prices
- Location and datetime: cities, zip codes, datetimes
- Job and company info: job titles, employers, business jargon
Faker is especially useful for:
- Bootstrapping test databases
- Anonymizing production data
- Stress testing apps with large fake datasets
📚 Full docs here: https://faker.readthedocs.io/
You can define fields in one of two ways:
Choose from 100+ Faker methods, like:
name,address,email,company,jobipv4,iban,phone_number,credit_card_number
Each row will call the selected method once to generate data for that column.
Use # for digits, ? for uppercase letters, and include any literal characters you want.
Examples:
$###,###.##→$452,990.14??-####→AZ-9842Order-####-??→Order-1023-KL
You can also limit each column to a fixed number of unique values (e.g. 10 departments, 50 zip codes).
- Prototyping mock datasets for machine learning
- Populating UI components with realistic data
- Creating sample JSON APIs for demos
- Teaching data pipelines, visualization, or testing
- Filling out dashboards or Excel models quickly
The underlying Faker library is also released under the MIT License.