Datasette

• Fixes an open redirect security issue: Datasette instances would redirect to example.com/foo/bar if you accessed the path //example.com/foo/bar. Thanks to James Jefferies for the fix. #2429
• Upgraded for compatibility with Python 3.14.
• Fixed datasette publish cloudrun to work with changes to the underlying Cloud Run architecture. #2511
• Minor upgrades to fix warnings, including pkg_resources deprecation.

<img src="https://datasette.io/static/datasette-logo.svg" alt="Datasette">

PyPI

Changelog

Python 3.x

Tests

Documentation Status

License

docker: datasette

discord

An open source multi-tool for exploring and publishing data

Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API.

Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world.

Explore a demo, watch a video about the project or try it out on GitHub Codespaces.

• datasette.io is the official project website
• Latest Datasette News
• Comprehensive documentation: https://docs.datasette.io/
• Examples: https://datasette.io/examples
• Live demo of current main branch: https://latest.datasette.io/
• Questions, feedback or want to talk about the project? Join our Discord

Want to stay up-to-date with the project? Subscribe to the Datasette newsletter for tips, tricks and news on what's new in the Datasette ecosystem.

Installation

If you are on a Mac, Homebrew is the easiest way to install Datasette:

brew install datasette

You can also install it using pip or pipx:

pip install datasette

Datasette requires Python 3.8 or higher. We also have detailed installation instructions covering other options such as Docker.

Basic usage

datasette serve path/to/database.db

This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface.

serve is the default subcommand, you can omit it if you like.

Use Chrome on OS X? You can run datasette against your browser history like so:

datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock

Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data:

Downloads table rendered by datasette

metadata.json

If you want to include licensing and source information in the generated datasette website you can do so using a JSON file that looks something like this:

{

"title": "Five Thirty Eight",

"license": "CC Attribution 4.0 License",

"license_url": "http://creativecommons.org/licenses/by/4.0/",

"source": "fivethirtyeight/data on GitHub",

"source_url": "https://github.com/fivethirtyeight/data"

}

Save this in metadata.json and run Datasette like so:

datasette serve fivethirtyeight.db -m metadata.json

The license and source information will be displayed on the index page and in the footer. They will also be included in the JSON produced by the API.

datasette publish

If you have Heroku or Google Cloud Run configured, Datasette can deploy one or more SQLite databases to the internet with a single command:

datasette publish heroku database.db

Or:

datasette publish cloudrun database.db

This will create a docker image containing both the datasette application and the specified SQLite database files. It will then deploy that image to Heroku or Cloud Run and give you a URL to access the resulting website and API.

See Publishing data in the documentation for more details.

Datasette Lite

Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. Read more about that in the Datasette Lite documentation.