PandasAI is a Python platform 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>=3.0.0b2"

With poetry:

poetry add "pandasai>=3.0.0b2"

💻 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 gd. 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

Beta Notice
Release v3 is currently in beta. The following documentation and examples reflect the features and functionality in progress and may change before the final release.

• 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.

Thank you!