Improved pipelines for data science projects.

Getting started

Why Recipipe?

It has cool features, like selecting columns using Unix patterns:

or getting beautiful output column names instead of numeric indexed outputs:

or fitting a different transformer per group:

Read the tutorials and other examples to learn more.

Install from PyPI

pip install recipipe

All the dependencies will be installed automatically.

Install from source

Clone the repository and run:

pip install .

Install the package in a dev environment with:

pip install -e .

All the dependencies will be installed automatically.

Tutorials and examples

• Explore a notebook with a
  list of things you can do with Recipipe.
• Learn how to transform Titanic data.
• Learn how to use Recipipe analyzing data from weird creatures from another
  planet: Recipipe getting started tutorial.

Running the tests

Run all the test using:

pytest

Run an specific test file with:

pytest tests/<filename>

Run tests with coverage using:

coverage run --source=recipipe -m pytest

What's the meaning of Recipipe?

It comes from a beautiful R library called recipes and the concept of pipelines.

recipes + pipelines = recipipe

That explains the logo of a muffing (recipes) holding some pipes (pipelines).

License

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

Author

guiferviz, contributions are more than welcome.