- advocates a proper Python package structure that can be shipped and distributed,
- uses a conda environment instead of something virtualenv-based and is thus more suitable for data science projects,
- more default configurations for Sphinx, pytest, pre-commit, etc. to foster clean coding and best practices.
Also consider using dvc to version control and share your data within your team. Read this blogpost to learn how to work with JupyterLab notebooks efficiently by using a data science project structure like this.
The final directory structure looks like:
├── AUTHORS.md <- List of developers and maintainers. ├── CHANGELOG.md <- Changelog to keep track of new features and fixes. ├── LICENSE.txt <- License as chosen on the command-line. ├── README.md <- The top-level README for developers. ├── configs <- Directory for configurations of model & application. ├── data │ ├── external <- Data from third party sources. │ ├── interim <- Intermediate data that has been transformed. │ ├── processed <- The final, canonical data sets for modeling. │ └── raw <- The original, immutable data dump. ├── docs <- Directory for Sphinx documentation in rst or md. ├── environment.yml <- The conda environment file for reproducibility. ├── models <- Trained and serialized models, model predictions, │ or model summaries. ├── notebooks <- Jupyter notebooks. Naming convention is a number (for │ ordering), the creator's initials and a description, │ e.g. `1.0-fw-initial-data-exploration`. ├── pyproject.toml <- Build configuration. Don't change! Use `pip install -e .` │ to install for development or to build `tox -e build`. ├── references <- Data dictionaries, manuals, and all other materials. ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures <- Generated plots and figures for reports. ├── scripts <- Analysis and production scripts which import the │ actual PYTHON_PKG, e.g. train_model. ├── setup.cfg <- Declarative configuration of your project. ├── setup.py <- [DEPRECATED] Use `python setup.py develop` to install for │ development or `python setup.py bdist_wheel` to build. ├── src │ └── PYTHON_PKG <- Actual Python package where the main functionality goes. ├── tests <- Unit tests which can be run with `pytest`. ├── .coveragerc <- Configuration for coverage reports of unit tests. ├── .isort.cfg <- Configuration for git hook that sorts imports. └── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.
Just install this package with
conda install -c conda-forge pyscaffoldext-dsproject
and note that
putup -h shows a new option
Creating a data science project is then as easy as:
putup --dsproject my_ds_project
Making Changes & Contributing
This project uses pre-commit, please make sure to install it before making any changes:
conda install pre-commit cd pyscaffoldext-dsproject pre-commit install
It is a good idea to update the hooks to the latest version:
Please also check PyScaffold's contribution guidelines.
This project has been set up using PyScaffold 3.2. For details and usage information on PyScaffold see https://pyscaffold.org/.