--- title: "pandas-ai" type: "tool" slug: "sinaptik-ai-pandas-ai" canonical_url: "https://www.graphcanon.com/tools/sinaptik-ai-pandas-ai" github_url: "https://github.com/sinaptik-ai/pandas-ai" homepage_url: "https://pandas-ai.com" stars: 23636 forks: 2334 primary_language: "Python" license: "Other" categories: ["data-retrieval", "llm-frameworks"] tags: ["data-visualization", "datalake", "llm", "ai", "data-analysis", "csv", "gpt-4", "database"] updated_at: "2026-07-07T18:25:01.987554+00:00" --- # pandas-ai > Chat with your database or datalake using LLMs PandasAI is a Python library that leverages large language models (LLMs) for conversational data analysis, enabling users to interact with databases and datalakes in natural language. ## Facts - Repository: https://github.com/sinaptik-ai/pandas-ai - Homepage: https://pandas-ai.com - Stars: 23,636 · Forks: 2,334 · Open issues: 19 · Watchers: 169 - Primary language: Python - License: Other - Last pushed: 2025-10-28T10:02:13+00:00 ## Categories - [Data & Retrieval](/categories/data-retrieval.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags data-visualization, datalake, llm, ai, data-analysis, csv, gpt-4, database ## Related tools - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. 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(★ 116,702) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) ## README (excerpt) ```text # 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, and it helps technical users to save time, and effort when working with data. # 🔧 Getting started You can find the full documentation for PandasAI [here](https://docs.pandas-ai.com/). ## 📚 Using the library ### Python Requirements Python version `3.8+ <=3.11` ### 📦 Installation You can install the PandasAI library using pip or poetry. With pip: ```bash pip install pandasai pip install pandasai-litellm ``` With poetry: ```bash poetry add pandasai poetry add pandasai-litellm ``` ### 💻 Usage #### Ask questions ```python import pandasai as pai from pandasai_litellm.litellm import LiteLLM # Initialize LiteLLM with your OpenAI model llm = LiteLLM(model="gpt-4.1-mini", api_key="YOUR_OPENAI_API_KEY") # Configure PandasAI to use this LLM pai.config.set({ "llm": llm }) # Load your data df = pai.read_csv("data/companies.csv") response = df.chat("What is the average revenue by region?") print(response) ``` --- Or you can ask more complex questions: ```python df.chat( "What is the total sales for the top 3 countries by sales?" ) ``` ``` The total sales for the top 3 countries by sales is 16500. ``` #### Visualize charts You can also ask PandasAI to generate charts for you: ```python df.chat( "Plot the histogram of countries showing for each one the gdp. Use different colors for each bar", ) ``` #### Multiple DataFrames You can also pass in multiple dataframes to PandasAI and ask questions relating them. ```python import pandasai as pai from pandasai_litellm.litellm import LiteLLM # Initialize LiteLLM with your OpenAI model llm = LiteLLM(model="gpt-4.1-mini", api_key="YOUR_OPENAI_API_KEY") # Configure PandasAI to use this LLM pai.config.set({ "llm": llm }) employees_data = { 'EmployeeID': [1, 2, 3, 4, 5], 'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'], 'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance'] } salaries_data = { 'EmployeeID': [1, 2, 3, 4, 5], 'Salary': [5000, 6000, 4500, 7000, 5500] } employees_df = pai.DataFrame(employees_data) salaries_df = pai.DataFrame(salaries_data) pai.chat("Who gets paid the most?", employees_df, salaries_df) ``` ``` Olivia gets paid the most. ``` #### Docker Sandbox You can run PandasAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks. ##### Python Requirements ```bash pip install "pandasai-docker" ``` ##### Usage ```python import pandasai as pai from pandasai_docker import DockerSandbox from pandasai_litellm.litellm import LiteLLM # Initialize LiteLLM with your OpenAI model llm = LiteLLM(model="gpt-4.1-mini", api_key="YOUR_OPENAI_API_KEY") # Configure PandasAI to use this LLM pai.config.set({ "llm": llm }) # Initialize the sandbox sandbox = DockerSandbox() sandbox.start() employees_data = { 'EmployeeID': [1, 2, 3, 4, 5], 'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'], 'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance'] } salaries_data = { 'EmployeeID': [1, 2, 3, 4, 5], 'Salary': [5000, 6000, 4500, 7000, 5500] } employees_df = pai.DataFrame(employees_data) salaries_df = pai.DataFrame(salaries_data) pai.chat("Who gets paid the most?", employees_df, salaries_df, sandbox=sandbox) # Don't forget to stop the sandbox when done sandbox.stop() ``` ``` Olivia gets paid the most. ``` You can find more examples in the [examples](examples) directory. ## 📜 License PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory of this repository, which has its [license here](https://github.com/sinaptik-ai/pandas-ai/blob/main/ee/LICENSE). If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://pandas-ai.com). ## Resources - [Do ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/sinaptik-ai-pandas-ai`](/api/graphcanon/tools/sinaptik-ai-pandas-ai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_