Data Formulator: Create Rich Visualizations with AI

Transform data and create rich visualizations iteratively with AI 🪄. Try Data Formulator now!

Any questions? Ask on the Discord channel!

News 🔥🔥🔥

• [07-10-2025] Data Formulator 0.2.2: Start with an analysis goal

  • Some key frontend performance updates.
  • You can start your exploration with a goal, or, tab and see if the agent can recommend some good exploration ideas for you. Demo

• [05-13-2025] Data Formulator 0.2.1.3/4: External Data Loader

  • We introduced external data loader class to make import data easier. Readme and Demo
    • Current data loaders: MySQL, Azure Data Explorer (Kusto), Azure Blob and Amazon S3 (json, parquet, csv).
    • [07-01-2025] Updated with: Postgresql, mssql.
  • Call for action link:
    • Users: let us know which data source you'd like to load data from.
    • Developers: let's build more data loaders.

• [04-23-2025] Data Formulator 0.2: working with large data 📦📦📦

  • Explore large data by:
    1. Upload large data file to the local database (powered by DuckDB).
    2. Use drag-and-drop to specify charts, and Data Formulator dynamically fetches data from the database to create visualizations (with ⚡️⚡️⚡️ speeds).
    3. Work with AI agents: they generate SQL queries to transform the data to create rich visualizations!
    4. Anchor the result / follow up / create a new branch / join tables; let's dive deeper.
  • Checkout the demos at [https://github.com/microsoft/data-formulator/releases/tag/0.2]
  • Improved overall system performance, and enjoy the updated derive concept functionality.

• [03-20-2025] Data Formulator 0.1.7: Anchoring ⚓︎

  • Anchor an intermediate dataset, so that followup data analysis are built on top of the anchored data, not the original one.
  • Clean a data and work with only the cleaned data; create a subset from the original data or join multiple data, and then go from there. AI agents will be less likely to get confused and work faster. ⚡️⚡️
  • Check out the demos at [https://github.com/microsoft/data-formulator/releases/tag/0.1.7]
  • Don't forget to update Data Formulator to test it out!

• [02-20-2025] Data Formulator 0.1.6 released!

  • Now supports working with multiple datasets at once! Tell Data Formulator which data tables you would like to use in the encoding shelf, and it will figure out how to join the tables to create a visualization to answer your question. 🪄
  • Checkout the demo at [https://github.com/microsoft/data-formulator/releases/tag/0.1.6].
  • Update your Data Formulator to the latest version to play with the new features.

• [02-12-2025] More models supported now!

  • Now supports OpenAI, Azure, Ollama, and Anthropic models (and more powered by LiteLLM);
  • Models with strong code generation and instruction following capabilities are recommended (gpt-4o, claude-3-5-sonnet etc.);
  • You can store API keys in api-keys.env to avoid typing them every time (see template api-keys.env.template).
  • Let us know which models you have good/bad experiences with, and what models you would like to see supported! [comment here]

• [11-07-2024] Minor fun update: data visualization challenges!

  • We added a few visualization challenges with the sample datasets. Can you complete them all? [try them out!]
  • Comment in the issue when you did, or share your results/questions with others! [comment here]

• [10-11-2024] Data Formulator python package released!

  • You can now install Data Formulator using Python and run it locally, easily. [check it out].
  • Our Codespaces configuration is also updated for fast start up ⚡️. [try it now!]
  • New experimental feature: load an image or a messy text, and ask AI to parse and clean it for you(!). [demo]

• [10-01-2024] Initial release of Data Formulator, check out our [blog] and [video]!

Overview

Data Formulator is an application from Microsoft Research that uses large language models to transform data, expediting the practice of data visualization.

Data Formulator is an AI-powered tool for analysts to iteratively create rich visualizations. Unlike most chat-based AI tools where users need to describe everything in natural language, Data Formulator combines user interface interactions (UI) and natural language (NL) inputs for easier interaction. This blended approach makes it easier for users to describe their chart designs while delegating data transformation to AI.

Get Started

Play with Data Formulator with one of the following options:

• Option 1: Install via Python PIP

  Use Python PIP for an easy setup experience, running locally (recommend: install it in a virtual environment).

  # install data_formulator
  pip install data_formulator
  
  # start data_formulator
  data_formulator 
  
  # alternatively, you can run data formulator with this command
  python -m data_formulator
  

  Data Formulator will be automatically opened in the browser at http://localhost:5000.

  Update: you can specify the port number (e.g., 8080) by python -m data_formulator --port 8080 if the default port is occupied.

• Option 2: Codespaces (5 minutes)

  You can also run Data Formulator in Codespaces; we have everything pre-configured. For more details, see CODESPACES.md.

  

• Option 3: Working in the developer mode

  You can build Data Formulator locally if you prefer full control over your development environment and the ability to customize the setup to your specific needs. For detailed instructions, refer to DEVELOPMENT.md.

Using Data Formulator

Once you've completed the setup using either option, follow these steps to start using Data Formulator:

The basics of data visualization

• Provide OpenAI keys and select a model (GPT-4o suggested) and choose a dataset.
• Choose a chart type, and then drag-and-drop data fields to chart properties (x, y, color, ...) to specify visual encodings.

https://github.com/user-attachments/assets/0fbea012-1d2d-46c3-a923-b1fc5eb5e5b8

Create visualization beyond the initial dataset (powered by 🤖)

• You can type names of fields that do not exist in current data in the encoding shelf:
  • this tells Data Formulator that you want to create visualizations that require computation or transformation from existing data,
  • you can optionally provide a natural language prompt to explain and clarify your intent (not necessary when field names are self-explanatory).
• Click the Formulate button.
  • Data Formulator will transform data and instantiate the visualization based on the encoding and prompt.
• Inspect the data, chart and code.
• To create a new chart based on existing ones, follow up in natural language:
  • provide a follow up prompt (e.g., ``show only top 5!''),
  • you may also update visual encodings for the new chart.

https://github.com/user-attachments/assets/160c69d2-f42d-435c-9ff3-b1229b5bddba

https://github.com/user-attachments/assets/c93b3e84-8ca8-49ae-80ea-f91ceef34acb

Repeat this process as needed to explore and understand your data. Your explorations are trackable in the Data Threads panel.

Developers' Guide

Follow the developers' instructions to build your new data analysis tools on top of Data Formulator.

Research Papers

• Data Formulator 2: Iteratively Creating Rich Visualizations with AI

@article{wang2024dataformulator2iteratively,
      title={Data Formulator 2: Iteratively Creating Rich Visualizations with AI}, 
      author={Chenglong Wang and Bongshin Lee and Steven Drucker and Dan Marshall and Jianfeng Gao},
      year={2024},
      booktitle={ArXiv preprint arXiv:2408.16119},
}

• Data Formulator: AI-powered Concept-driven Visualization Authoring

@article{wang2023data,
  title={Data Formulator: AI-powered Concept-driven Visualization Authoring},
  author={Wang, Chenglong and Thompson, John and Lee, Bongshin},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2023},
  publisher={IEEE}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.