# data-formulator
**Repository Path**: headfirst545/data-formulator
## Basic Information
- **Project Name**: data-formulator
- **Description**: forked from microsoft/data-formulator
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: Chenglong-MS-patch-1
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-16
- **Last Updated**: 2025-01-16
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
Data Formulator: Create Rich Visualizations with AI
[](https://arxiv.org/abs/2408.16119)
[](https://opensource.org/licenses/MIT)
[](https://youtu.be/3ndlwt0Wi3c)
[](https://github.com/microsoft/data-formulator/actions/workflows/python-build.yml)
Transform data and create rich visualizations iteratively with AI 🪄. Try Data Formulator now in GitHub Codespaces!
[](https://codespaces.new/microsoft/data-formulator?quickstart=1)
## News 🔥🔥🔥
- [10-11-2024] Data Formulator python package released!
- You can now install Data Formulator using Python and run it locally, easily. [[check it out]](#get-started).
- Our Codespace configuration is also updated for fast start up ⚡️. [[try it now!]](https://codespaces.new/microsoft/data-formulator?quickstart=1)
- New exprimental feature: load an image or a messy text, and ask AI parsing and cleaning it for you(!). [[demo]](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717)
- [10-01-2024] Initial release of Data Formulator, check out our [[blog]](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) and [[video]](https://youtu.be/3ndlwt0Wi3c)!
## 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).
```bash
# install data_formulator
pip install data_formulator
# start data_formulator
data_formulator
# alternatively, you can run data formualtor with this command
python -m data_formulator
```
Data Formulator will be automatically opened in the browser at [http://localhost:5000](http://localhost:5000).
- **Option 2: Codespaces (5 minutes)**
You can also run Data Formualtor in codespace, we have everything pre-configured. For more details, see [CODESPACES.md](CODESPACES.md).
[](https://codespaces.new/microsoft/data-formulator?quickstart=1)
- **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](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 visualizions that require computation or transformation from existing data,
- you can optionally provide a natural language prompt to explain your intent to 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](DEVELOPMENT.md) to build your new data analysis tools on top of Data Formulator.
## Research Papers
* [Data Formulator 2: Iteratively Creating Rich Visualizations with AI](https://arxiv.org/abs/2408.16119)
```
@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](https://arxiv.org/abs/2309.10094)
```
@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](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
or contact [opencode@microsoft.com](mailto: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](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
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.
以下是您提供的链接中的文档翻译成中文的内容：
---
# Data Formulator
## 简介
Data Formulator 是一个用于数据处理的工具，旨在帮助用户快速、高效地处理和转换数据。它提供了多种功能，包括数据清洗、格式转换、数据合并等，适用于各种数据分析和处理场景。
## 功能特性
- **数据清洗**：自动识别并处理数据中的缺失值、重复值和异常值。
- **格式转换**：支持多种数据格式的转换，如 CSV、JSON、XML 等。
- **数据合并**：将多个数据源的数据合并为一个统一的数据集。
- **数据筛选**：根据条件筛选数据，方便用户快速获取所需信息。
- **数据导出**：处理后的数据可以导出为多种格式，便于进一步分析或使用。
## 安装
要安装 Data Formulator，请按照以下步骤操作：
1. 克隆仓库：
```bash
git clone https://gitee.com/headfirst545/data-formulator.git
```
2. 进入项目目录：
```bash
cd data-formulator
```
3. 安装依赖：
```bash
pip install -r requirements.txt
```
## 使用示例
以下是一个简单的使用示例，展示如何使用 Data Formulator 进行数据清洗和格式转换：
```python
from data_formulator import DataFormulator
# 初始化 DataFormulator
df = DataFormulator()
# 加载数据
data = df.load_data('data.csv')
# 数据清洗
cleaned_data = df.clean_data(data)
# 格式转换
json_data = df.convert_to_json(cleaned_data)
# 保存处理后的数据
df.save_data(json_data, 'output.json')
```
## 贡献
欢迎贡献代码！如果您有任何建议或发现任何问题，请提交 Issue 或 Pull Request。
## 许可证
本项目采用 MIT 许可证。详情请参阅 [LICENSE](LICENSE) 文件。
---
希望这个翻译对您有帮助！如果有其他问题，请随时告诉我。