Data visualization 2023-04-25
By unverified author. Claim this AI
Automatic data exploration and visualisation generation.
Generated by ChatGPT

LIDA is a powerful tool that automates data exploration and generates visualizations and infographics using large language models (LLMs) like ChatGPT and GPT4.

It provides a conversational interface for automatic generation of grammar-agnostic visualizations from data. LIDA consists of four modules: the Summarizer, which converts data into a compact natural language summary; the Goal Explorer, which enumerates visualization goals based on the data; the VisGenerator, which generates, refines, executes, and filters visualization code; and the Infographer, which produces data-faithful stylized graphics using image generation models.LIDA is compatible with any programming language or visualization grammar, allowing users to create visualizations in Python (e.g., Altair, Matplotlib, Seaborn), R, C++, and more.

It also offers operations on existing visualizations, such as visualization explanation, self-evaluation, automatic repair, and recommendation.The tool supports various capabilities, including data summarization, automated data exploration, grammar-agnostic visualizations, and infographics generation.

It leverages the language modeling and code-writing capabilities of LLMs, enabling core automated visualization capabilities. LIDA also provides operations on generated visualizations, such as visualization explanation, self-evaluation, visualization repair, and visualization recommendations.LIDA's architecture combines LLMs and image generation models (IGMs) to address the multi-stage generation problem of visualization creation.

It is open-source and offers a Python API and a hybrid user interface for interactive chart, infographic, and data story generation.While LIDA has limitations with visualization grammars not well-represented in the LLM's training dataset and performance variations depending on the choice of visualization libraries and code generation capabilities, it remains a powerful tool for automating the visualization generation process.


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LIDA was manually vetted by our editorial team and was first featured on August 30th 2023.
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32 alternatives to LIDA for Data visualization

Pros and Cons


Automates data exploration
Generates infographics
Conversational interface
Grammar-agnostic visualizations
Comprises four modules
Compatible with any language
Supports various visualizations
Visualization explanation
Self-evaluation feature
Visualization repair
Auto visualization recommendations
LLMs and IGMs integration
Python API provided
Interactive chart creation
Data story generation
Automated data summarization
Visualization in all grammars
Personalized infographic styles
Operations on generated visualizations
Automated improvement of visualizations
User-provided feedback feature
Hybrid user interface
Available via pip install
Auto generates visualization goals
Generates rich, natural language summaries
Safe code execution recommendation
Debugging/sensemaking applications
Supports multi-dimensional evaluation
Generates embellished infographics
Full automated mode available
Offers visualization evaluation scores
Access to visualization best practices
Compact data representation
Supports Altair, Matplotlib, Seaborn
Supports general code writing
Supports brand, style, marketing personalisation
Allows visualization comparison
Supports accessibility
Supports data literacy
Educational applications
Supports GPT3.5, GPT4 models


Limited visualization grammar support
Variable performance on libraries
Requires code execution
Sandbox environment recommended
Possibility of unsafe code
Performance relies on dataset type


What is the purpose of LIDA?
How does LIDA use large language models like ChatGPT and GPT4?
What are the four modules of LIDA and their functions?
Which programming languages does LIDA support?
Can LIDA operate on existing visualizations?
What capabilities does LIDA offer?
What is the role of image generation models in LIDA?
What are some potential limitations of LIDA?
Are there any example visualizations or infographics created with LIDA?
Is LIDA an open-source tool?
How does LIDA enable automated data exploration?
Does LIDA generate visualization code?
Can LIDA create visualizations in Python (e.g., Altair, Matplotlib, Seaborn)?
What is the functionality of LIDA's Summarizer module?
How does LIDA's Goal Explorer module identify visualization goals?
Does LIDA offer a Python API and hybrid user interface?
Can LIDA automatically repair visualizations?
Can LIDA's performance change based on the choice of visualization libraries?
What is LIDA's Infographer module and what does it do?
How does LIDA handle visualization explanations and self-evaluations?

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