# Cookiecutter Data Science - using poetry and justfiles

_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._


#### [Project homepage](http://drivendata.github.io/cookiecutter-data-science/)


### Requirements to use the cookiecutter template:
-----------
 - Python 3.9+
 - [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html) >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

``` bash
$ pip install cookiecutter
```

### To start a new project, run:
------------

    cookiecutter https://github.com/wassname/cookiecutter-data-science


[![asciicast](https://asciinema.org/a/244658.svg)](https://asciinema.org/a/244658)


### The resulting directory structure
------------

The directory structure of your new project looks like this: 

```
├── justfile           <- justfile with commands like `just data` or `just train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── nbs          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pypoetry.toml   <- The requirements directory for reproducing the analysis environment
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
├── .env               <- Environment variables, not commited
```

## Contributing

We welcome contributions! [See the docs for guidelines](https://drivendata.github.io/cookiecutter-data-science/#contributing).

### Installing development requirements
------------

    poetry install