datascience/README.md

# Actes princiers -- refactoring datascience

## Project Name

human readable name : `Actes Princiers`

The project name 'Actes Princiers' has been applied to:

- The project title in `datascience/actes-princiers/README.md` 
- The folder created for your project in `datascience/actes-princiers` 
- The project's python package in `datascience/actes-princiers/src/actes_princiers`

A best-practice setup includes initialising git and creating a virtual environment before running 'pip install -r src/requirements.txt'

## Getting started

- Install the virtual environment : `python3 -m venv .venv`
- Enable the virtual environment : `source .venv/bin/activate`
- install kedro `pip install kedro`
- Install the packages and libraries `pip install -r src/requirements.txt`

**go to `actes-princiers`'s folder**

Then open a terminal in the `actes-princiers`'s folder 
and launch jupyter : `kedro jupyter notebook` 
or start the ipython prompt : `kedro ipython`

## Launching the pipelines

Open a terminal in the `actes-princiers`'s folder and launch kedro

`kedro run`

or launch a specific node in the pipeline with:

`kedro run --nodes=preprocess_html`

or a search by tags with: 

`kedro run --tags=xsl`

## Visualizing the pipelines

`kedro viz`

## Building the docs

`./build-docs.sh docs` 

the html built doc is `here <docs/build/html/>`_

## Developper's rules and guidelines

Declare any dependencies in `src/requirements.txt` for `pip` installation.

To install them, run: `pip install -r src/requirements.txt`

## tips

You need to reload Kedro variables by calling `%reload_kedro` in your notebook and re-run the code snippet


Par rapport aux bonnes pratiques kedro 
------------------------------------------

Dans `actes-princiers/.gitignore`, 
- les datas sont mises dans le dépôt git
- le datacatalog en local est placé dans le dépôt git

::

    # ignore all local configuration
    # conf/local/**
    # ignore everything in the following folders
    # data/**

## make a package for deployment 

[package based deployment](https://docs.kedro.org/en/stable/deployment/single_machine.html#package-based)

If you prefer not to use containerisation, you can instead package your Kedro project using kedro package.

Run the following in your project’s root directory:

kedro package

Kedro builds the package into the dist/ folder of your project, and creates a .whl file, which is a Python packaging format for binary distribution.