PandaAI 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.
You can find the full documentation for PandaAI here.
You can either decide to use PandaAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
The library can be used alongside our powerful data platform, making end-to-end conversational data analytics possible with as little as a few lines of code.
Load your data, save them as a dataframe, and push them to the platform
import pandasai as pai
pai.api_key.set("your-pai-api-key")
file = pai.read_csv("./filepath.csv")
dataset = pai.create(path="your-organization/dataset-name",
df=file,
name="dataset-name",
description="dataset-description")
dataset.push()Your team can now access and query this data using natural language through the platform.
Python version 3.8+ <3.12
You can install the PandaAI library using pip or poetry.
With pip:
pip install "pandasai>=3.0.0b2"With poetry:
poetry add "pandasai>=3.0.0b2"import pandasai as pai
# Sample DataFrame
df = pai.DataFrame({
"country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
"revenue": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]
})
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://app.pandabi.ai (you can also configure it in your .env file)
pai.api_key.set("your-pai-api-key")
df.chat('Which are the top 5 countries by sales?')China, United States, Japan, Germany, Australia
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.
You can also ask PandaAI to generate charts for you:
df.chat(
"Plot the histogram of countries showing for each one the gd. Use different colors for each bar",
)
You can also pass in multiple dataframes to PandaAI and ask questions relating them.
import pandasai as pai
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)
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://app.pandabi.ai (you can also configure it in your .env file)
pai.api_key.set("your-pai-api-key")
pai.chat("Who gets paid the most?", employees_df, salaries_df)Olivia gets paid the most.
You can run PandaAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks.
pip install "pandasai-docker"import pandasai as pai
from pandasai_docker import DockerSandbox
# 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)
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://app.pandabi.ai (you can also configure it in your .env file)
pai.api_key.set("your-pai-api-key")
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.
PandaAI 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 PandaAI Cloud or self-hosted Enterprise Offering, contact us.
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 PandaAI team
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.