DataAgent leverages advanced AI agents built on top of LLMs to explore datasets, generate insights, and assemble machine learning pipelines automatically. Users point DataAgent at a CSV, SQL table, or Pandas DataFrame and pose questions in natural language. The agent interprets queries, executes analysis code, visualizes results, and even writes modular Python scripts for ETL and modeling tasks. It streamlines the entire data science workflow by reducing boilerplate coding and accelerating experimentation.

Data Analysis Agent wraps an LLM-based agent around a Pandas DataFrame to let users perform exploratory data analysis via natural language. When a user asks a question, the agent generates the required Python code, executes it, and returns results or charts. If a query is ambiguous, it asks clarifying questions before proceeding. It supports filtering, grouping, aggregation, summary statistics, and visualization libraries like Matplotlib or Seaborn for immediate insights, streamlining the analytics workflow and reducing the need to write boilerplate code.

PandasAI is a Python library that enhances the functionality of Pandas by integrating generative AI capabilities. It allows users to query, filter, and manipulate dataframes using natural language. This diminishes the learning curve for non-technical users, making data analysis more intuitive and user-friendly. By leveraging large language models (LLMs) like GPT-3.5/4, PandasAI transforms complex data operations into simple conversational interactions.