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 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
- 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!
1 maintainer
Metadata
1 maintainer
Release files for pandasai 3.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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| pandasai-3.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 138.4 kB
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