This gist gives a template for python projects oriented towards data science.

python-project-template.txt

|-- LICENSE (if relevant)
|
|-- README.md			<- the top-level README for developers using this project
|
|-- CHANGELOG.md		<- a changelog for tracking changes over the history of a project.
|						   Example: https://github.com/bmmalone/pyllars/blob/dev/CHANGELOG.md
|
|-- setup.cfg			<- project-specific configuration for python.
|
|-- .gitignore			<- avoid uploading data, credentials, outputs, system files, etc.
|
|-- conf
|	|-- base			<- shared, not-secret configuration options
|	|-- .env			<- local/secret configuration options, such as credentials
|
|-- data				<- data for tests should generally _not_ be stored here, but rather in the `data` module of the package
|	|-- raw				<- original data from publications. This could be symlinked.
|	|-- intermediate	<- cleaned version of raw data
|	|-- processed		<- parquet files to load into feature store
|
|-- docker				<- dockerfiles, docker-compose.yml files, etc.
|
|-- docs				<- base location for Sphinx documentation
|	|-- formal			<- requirements and validation plan, etc., documentation
|
|-- models				<- trained and serialized models
|
|-- notebooks			<- naming conventions: s<step>.v<version>-<short-description>.
|
|-- references			<- data dictionaries, manuals, and other external explanatory material
|
|-- results				<- intermediate and final analysis results. These could be prediction files, polished
|	|					   notebooks, latex, etc.
|	|
|	|-- figures			<- generated graphics, plots, etc.
|	|-- reports			<- generated analysis intended for external stakeholders
|
|-- requirements.txt	<- the requirements file for reproducing the analysis environment
|
|-- setup.py			<- ensure we can install the project code using `pip install -e .`
|
|-- <prj>				<- the source code package. Many sources are available online for developing python packages.
|	|					   Example: https://python-packaging-tutorial.readthedocs.io/en/latest/setup_py.html
|	|
|	|-- <prj>_utils.py	<- a module containing functions used across the package
|	|
|	|-- data			<- reading and writing data files. Additionally, this folder will contain data for running tests.
|	|
|	|-- features		<- transforming raw data into processed features
|	|
|	|-- models			<- building and training models, and then making predictions with the trained models
|	|
|	|-- evaluation		<- evaluating the predictions. N.B. This is _separate_ from training models and making
|	|					   predictions. This ensures that data processing, prediction, etc., steps do not need to be
|	|					   repeated each time a different evaluation is required.
|	|
|	|-- reporting		<- producing visualizations, tables, latex documents, etc.
|
|-- tests				<- pytest test files. Many tutorials are available online for pytest.
|						   Example: https://realpython.com/pytest-python-testing/
|
|-- wdl					<- workflow description language (pipeline) files