Welcome to pjdata ‘s documentation!

Install

Requirements

The pjdata package requires the following dependencies:

  • numpy
  • scipy
  • pjdata

Install

The pjdata is available on the PyPi . You can install it via pip as follow:

pip install -U pjdata

It is possible to use the development version installing from GitHub:

pip install -U git@github.com:end-to-end-data-science/pjdata.git

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from Github and install all dependencies:

git clone git@github.com:end-to-end-data-science/pjdata.git
cd pjdata
pip install .

Test and coverage

If you want to test/test-coverage the code before to install:

$ make install-dev
$ make test-cov

Or:

$ make install-dev
$ pytest --cov=pjdata/ tests/

Using pjdata

TODO.

For more examples see The pjdata example gallery.

API Documentation

This is the full API documentation of the pjdata package.

What is new on pjdata package?

The pjdata releases are available in PyPI and GitHub.

Version 0.X

Todo

About us

Contributors

You can find the contributors of this package here.

Citing pjdata

If you use the pjdata in scientific publication, we would appreciate citations to the following paper:

TODO

Getting started

Information to install, test, and contribute to the package.

API Documentation

In this section, we document expected types, functions, classes, and parameters available for AutoML building. We also describe our own AutoML systems.

Examples

A set of examples illustrating the use of pjdata package. You will learn in this section how pjdata works, patter, tips, and more.

What’s new ?

Log of the pjdata history.

About us

If you would like to know more about this project, how to cite it, and the contributors, see this section.