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Beyond querying, PandasAI offers functionalities to visualize data through graphs, cleanse datasets by addressing missing values, and enhance data quality through feature generation, making it a comprehensive tool for data scientists and analysts.

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Features

• Natural language querying: Ask questions to your data in natural language.
• Data visualization: Generate graphs and charts to visualize your data.
• Data cleansing: Cleanse datasets by addressing missing values.
• Feature generation: Enhance data quality through feature generation.
• Data connectors: Connect to various data sources like CSV, XLSX, PostgreSQL, MySQL, BigQuery, Databrick, Snowflake, etc.

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How does PandasAI work?

PandasAI uses a generative AI model to understand and interpret natural language queries and translate them into python code and SQL queries. It then uses the code to interact with the data and return the results to the user.

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Who should use PandasAI?

PandasAI is designed for data scientists, analysts, and engineers who want to interact with their data in a more natural way. It is particularly useful for those who are not familiar with SQL or Python or who want to save time and effort when working with data. It is also useful for those who are familiar with SQL and Python, as it allows them to ask questions to their data without having to write any complex code.

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How to get started with PandasAI?

PandasAI is available as a Python library and a web-based platform. You can install the library using pip or poetry and use it in your Python code. You can also use the web-based platform to interact with your data in a more visual way.

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☁️ Using the platform

The PandasAI platform provides a web-based interface for interacting with your data in a more visual way. You can ask questions to your data in natural language, generate graphs and charts to visualize your data, and cleanse datasets by addressing missing values. It uses FastAPI as the backend and NextJS as the frontend.

If you want to learn more how to start the platform on your local machine, you can check out the platform documentation.

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📚 Using the library

The PandasAI library provides a Python interface for interacting with your data in natural language. You can use it to ask questions to your data, generate graphs and charts, cleanse datasets, and enhance data quality through feature generation. It uses LLMs to understand and interpret natural language queries and translate them into python code and SQL queries.

Once you have installed PandasAI, you can start using it by importing the Agent class and instantiating it with your data. You can then use the chat method to ask questions to your data in natural language.

import os

import pandas as pd

from pandasai import Agent



# Sample DataFrame

sales_by_country = pd.DataFrame({

    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],

    "sales": [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://pandabi.ai (you can also configure it in your .env file)

os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"



agent = Agent(sales_by_country)

agent.chat('Which are the top 5 countries by sales?')

## Output

# China, United States, Japan, Germany, Australia

If you want to learn more about how to use the library, you can check out the library documentation.

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Support

If you have any questions or need help, please join our discord server.

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License

PandasAI is available under the MIT expat license, except for the pandasai/ee directory, which has its license here if applicable.

If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, contact us.

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Analytics

We’ve partnered with Scarf to collect anonymized user statistics to understand which features our community is using and how to prioritize product decision-making in the future. To opt out of this data collection, you can set the environment variable SCARF_NO_ANALYTICS=true.