base on Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG. #  [](https://pypi.org/project/pandasai/) [](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci-core.yml/badge.svg) [](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/cd.yml/badge.svg) [](https://codecov.io/gh/sinaptik-ai/pandas-ai) [](https://discord.gg/KYKj9F2FRH) [](https://pepy.tech/project/pandasai) [](https://opensource.org/licenses/MIT) [](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing) 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. # 🔧 Getting started You can find the full documentation for PandaAI [here](https://pandas-ai.readthedocs.io/en/latest/). You can either decide to use PandaAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo. ## ☁️ Using the platform 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 ```python 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.  ## 📚 Using the library ### Python Requirements Python version `3.8+ <3.12` ### 📦 Installation You can install the PandaAI library using pip or poetry. With pip: ```bash pip install "pandasai>=3.0.0b2" ``` With poetry: ```bash poetry add "pandasai>=3.0.0b2" ``` ### 💻 Usage #### Ask questions ```python 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: ```python 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 PandaAI to generate charts for you: ```python 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 PandaAI and ask questions relating them. ```python 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. ``` #### Docker Sandbox You can run PandaAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks. ##### Python Requirements ```bash pip install "pandasai-docker" ``` ##### Usage ```python 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](examples) directory. ## 📜 License PandaAI is available under the MIT expat license, except for the `pandasai/ee` directory of this repository, which has its [license here](https://github.com/sinaptik-ai/pandas-ai/blob/main/ee/LICENSE). If you are interested in managed PandaAI Cloud or self-hosted Enterprise Offering, [contact us](https://getpanda.ai/pricing). ## 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](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation - [Examples](examples) for example notebooks - [Discord](https://discord.gg/KYKj9F2FRH) for discussion with the community and PandaAI 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](CONTRIBUTING.md). ### Thank you! [](https://github.com/sinaptik-ai/pandas-ai/graphs/contributors) ", Assign "at most 3 tags" to the expected json: {"id":"8179","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"