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.
📚 Using the library
Python Requirements
Python version 3.8+ <=3.11
📦 Installation
You can install the PandasAI library using pip or poetry.
With pip:
pip install pandasai pip install pandasai-litellm
With poetry:
poetry add pandasai poetry add pandasai-litellm
💻 Usage
Ask questions
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:
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:
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.
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
pip install "pandasai-docker"Usage
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 directory.
📜 License
PandasAI is available under the MIT expat license, except for the pandasai/ee directory of this repository, which has its license here.
If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, contact us.
Resources
- Docs for comprehensive documentation
- Examples for example notebooks
- Discord for discussion with the community and PandasAI team
🤝 Contributing
Contributions are welcome! Please check the outstanding issues and feel free to open a pull request. For more information, please check out the contributing guidelines.