Verified project record

sinaptik-ai/pandas-ai

PandasAI is a Python library that lets developers and data teams query data in DataFrames, CSV, SQL, and parquet formats using natural language. It requires Python knowledge and relies on external large language model APIs to generate responses and visualizations.

Project overview

Project type: RAG · Data Processing
Use cases: Data Analysis
Deployment: Refer to project documentation
License: License pending

Best for

Developers and data teams who know Python and want to speed up routine data analysis tasks using conversational prompts.

Key capabilities

Accepts natural language questions and allows users to interact conversationally with their connected data sources.
Translates natural language prompts into charts and data visualizations.
Allows users to pass multiple dataframes simultaneously and ask questions that relate data across them.
Provides an isolated Docker environment for executing generated code safely, mitigating the risk of malicious attacks.

Limitations and risks

Requires a Python environment versioned between 3.8 and 3.11.
Because the library executes generated code, there are inherent security risks that are mitigated by running the process inside the provided Docker sandbox.

Getting started

Install the library using a simple pip install, configure an external LLM API key via LiteLLM, load a CSV file, and call the chat function to query the data.

Evidence and sources

GitHub project description: Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
README: PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way,…
README: response = df.chat("What is the average revenue by region?")
README: df = pai.read_csv("data/companies.csv")
GitHub repository metadata: ai, csv, data, data-analysis, data-science, data-visualization, database, datalake, gpt-4, llm, pandas, sql, text-to-sql

Reviewed at: 2026-07-21T07:02:43Z