Michael J Clark 922a0deeec uv
2025-01-10 15:32:45 +08:00
2023-01-10 13:20:16 +08:00
uv
2025-01-10 15:32:45 +08:00
2024-06-09 16:10:28 +08:00
2015-10-30 15:47:00 -04:00
2024-04-28 13:30:04 +08:00
2016-07-12 11:46:44 -04:00
2024-06-09 15:51:45 +08:00
2017-01-28 23:01:56 -05:00

Cookiecutter Data Science - using poetry and justfiles

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

Project homepage

Requirements to use the cookiecutter template:


  • Python 3.9+
  • Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:
$ pip install cookiecutter

To start a new project, run:


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

asciicast

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.

Installing development requirements


poetry install
S
Description
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Readme MIT 1 MiB
Languages
Jupyter Notebook 71.3%
Python 21.8%
Just 6.9%