base on Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG. #  [](https://pypi.org/project/pandasai/) [](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci.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/kF7FqH2FwS) [](https://pepy.tech/project/pandasai) [](https://opensource.org/licenses/MIT) [](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing) 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. # 🚀 Deploying PandasAI PandasAI can be used in a variety of ways. You can easily use it in your Jupyter notebooks or Streamlit apps, or you can deploy it as a REST API such as with FastAPI or Flask. If you are interested in the managed PandasAI Cloud or our self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8). # 🔧 Getting started You can find the full documentation for PandasAI [here](https://pandas-ai.readthedocs.io/en/latest/). You can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo. ## ☁️ Using the platform [](https://www.youtube.com/watch?v=kh61wEy9GYM) ### 📦 Installation PandasAI platform is uses a dockerized client-server architecture. You will need to have Docker installed in your machine. ```bash git clone https://github.com/sinaptik-ai/pandas-ai/ cd pandas-ai docker-compose build ``` ### 🚀 Running the platform Once you have built the platform, you can run it with: ```bash docker-compose up ``` This will start the client and server, and you can access the client at `http://localhost:3000`. ## 📚 Using the library ### 📦 Installation You can install the PandasAI library using pip or poetry. With pip: ```bash pip install pandasai ``` With poetry: ```bash poetry add pandasai ``` ### 🔍 Demo Try out the PandasAI library yourself in your browser: [](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing) ### 💻 Usage #### Ask questions ```python 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"], "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://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?') ``` ``` China, United States, Japan, Germany, Australia ``` --- Or you can ask more complex questions: ```python agent.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: ```python agent.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. ```python import os import pandas as pd from pandasai import Agent 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 = pd.DataFrame(employees_data) salaries_df = pd.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://pandabi.ai (you can also configure it in your .env file) os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY" agent = Agent([employees_df, salaries_df]) agent.chat("Who gets paid the most?") ``` ``` Olivia gets paid the most. ``` You can find more examples in the [examples](examples) directory. ## 🔒 Privacy & Security In order to generate the Python code to run, we take some random samples from the dataframe, we randomize it (using random generation for sensitive data and shuffling for non-sensitive data) and send just the randomized head to the LLM. If you want to enforce further your privacy you can instantiate PandasAI with `enforce_privacy = True` which will not send the head (but just column names) to the LLM. ## 📜 License PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable. If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8). ## Resources - [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation - [Examples](examples) for example notebooks - [Discord](https://discord.gg/kF7FqH2FwS) 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](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"