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interactive-data-analysis

An AI-powered interactive data analysis tool leveraging Langgraph and OpenAI. Provides an automated workflow for exploratory data analysis and detailed data exploration with Pandas DataFrame agent, including chart generation.

Overview

This project is an AI-powered data analysis tool utilizing langgraph, langchain agent, and pandas DataFrame to facilitate interactive data analysis.

Motivation

With the advancements in Large Language Models (LLM) and Large Multimodal Models (LMM), AI-driven data analysis has become feasible. Examples include:

  • ChatGPT for analyzing Excel or CSV files
  • Vanna.ai using NL2SQL for RDB-based data analysis

However, there are limitations with these tools:

  • ChatGPT cannot use other data sources. (only uploaded files)
  • Vanna.ai relies on SQL, making it harder to use NoSQL or other data sources. Moreover, data from .xlsx or .csv files must be transferred into an RDB for analysis, which is cumbersome.

To address these issues, this project aims to build an AI data analysis program based on pandas DataFrame, supporting a wide range of data sources.

Usage

Jupyter Notebook Example

For a step-by-step demonstration of how the AI-driven data analysis is performed, you can check out the Jupyter Notebook: dataframe_graph.ipynb.

This notebook provides a detailed walkthrough of the analysis process, including:

  • Loading data from various sources
  • Performing exploratory data analysis (EDA)
  • Utilizing the Langchain agent for generating insights and visualizations

Feel free to explore the notebook to better understand how to leverage this tool for your data analysis tasks.

Project Structure

The project is built around langgraph where each step is defined as a node, with edges connecting nodes based on the analysis sequence. The primary nodes include:

  • load_data: Loads data from various sources, supporting multiple data formats.
  • eda: Performs exploratory data analysis, including info, describe, head, and other functions.
  • agent: Carries out data analysis. Using the agent, the AI can generate charts, perform correlation analysis, and more, delivering insights based on the data.

Benefits

In traditional data analysis, loading data from various sources and conducting comprehensive exploratory data analysis is time-consuming. This tool helps analysts quickly extract insights and make decisions on analysis directions, using:

  • pandas DataFrame: A robust interface for handling diverse data sources.
  • langchain agent: For flexible and scalable AI-driven analysis.

This allows small-scale data analysis teams or individuals to perform fast, diverse analyses across different datasets.

License

This project is licensed under the terms of the MIT License.