Crate polars_ai

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§Polars AI 📊

Polars AI represents a pioneering utility featuring a command-line interface (CLI) complemented by a sophisticated crate/library. It empowers you to engage in conversational interactions with your Polars DataFrames, harnessing the capabilities of AI for data analysis. Polars AI seamlessly integrates the formidable prowess of OpenAI’s GPT-3.5 Turbo, thereby augmenting and optimizing data exploration and manipulation tasks.

Polars AI allows you to:

  1. Chat with your Polars DataFrames using plain text queries.
  2. Perform data analysis tasks such as filtering, aggregating through AI-generated Rust code.
  3. Visualize data using charts and plots (coming soon).

§Installation 🚀

To use Polars AI, you can also install it using Cargo, the Rust package manager:

  1. Build the project using Rust’s package manager, Cargo:

    $ cargo install polars-ai

  2. Run the CLI:

    $ polars-ai help

§Getting Started 🏁

Before you begin, make sure you have a Polars DataFrame that you want to analyze and interact with. Polars AI works with Polars DataFrames, so ensure that you have the necessary data loaded.

§Usage 🧑‍💻

§Chatting with Your DataFrames

With Polars AI, you can chat with your DataFrames using plain text queries. Simply enter your question or query when prompted by the CLI. For example:

$ polars-ai input -f examples/datasets/flights.csv show

Now, based on the query above, you can run the Rust code.

§Data Analysis Workflow

The generated Rust code follows a structured data analysis workflow:

  1. Prepare: Preprocess and clean the data if required.
  2. Process: Manipulate the data for analysis (e.g., grouping, filtering, aggregating).
  3. Analyze: Conduct the analysis.
  4. Output: Return results in various formats.

You can modify the generated code to customize your analysis.

§Examples 💡

Refer to the examples folder to use Polars AI to analyze your data. Polars AI will generate Rust code to perform eda on the data.

§Contributing 🤝

We welcome contributions to Polars AI! If you’d like to contribute to this project, please follow these steps:

  1. Fork the repository on GitHub:

    • Click the “Fork” button on the top right of the GitHub repository page.

  2. Create a new branch for your feature or bug fix:

    • Use the following Git command to create a new branch:

      $ git checkout -b feature-or-bugfix-branch

  3. Make your changes and commit them:

    • Edit the files in your local repository and use the following Git commands to commit your changes:

      $ git add .
$ git commit -m "Your commit message here"

  4. Create a pull request with a clear description of your changes:

    • Push your branch to your forked repository on GitHub and then create a pull request from there.

      $ git push origin feature-or-bugfix-branch

    • Visit your forked repository on GitHub, and you’ll see an option to create a pull request for the branch you just pushed.

§License 📜

This project is licensed under the MIT License - see the LICENSE file for details.

Modules§

cli
dataframe
error
nlp
utils