Package: pyscaffoldext-dsproject
pyscaffoldext-dsprojectPyScaffoldextension tailored forData Scienceprojects. This extension is inspired bycookiecutter-data-scienceand enhanced in many ways. The main differences are that itadvocates a proper Python package structure that can be shipped and distributed,uses acondaenvironment instead of somethingvirtualenv-based and is thus more suitable
for data science projects,more default configurations forSphinx,pytest,pre-commit, etc. to foster
clean coding and best practices.Also consider usingdvcto version control and share your data within your team.
Readthis blogpostto 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.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── 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.See a demonstration of the initial project structure underdsproject-demoand also check out
the documentation ofPyScaffoldfor more information.UsageJust install this package withconda install -c conda-forge pyscaffoldext-dsprojectand note thatputup -hshows a new option--dsproject.
Creating a data science project is then as easy as:putup--dsprojectmy_ds_projectThe flag--dsprojectcomprises additionally the flags--markdown,--pre-commitand--no-skeletonfor convenience.Making Changes & ContributingThis project usespre-commit, please make sure to install it before making any
changes:condainstallpre-commitcdpyscaffoldext-dsproject
pre-commitinstallIt is a good idea to update the hooks to the latest version:pre-commitautoupdatePlease also check PyScaffold'scontribution guidelines.