A demonstration Python data project showing:
- The cookiecutter project layout https://github.com/drivendata/cookiecutter-data-science,
- a packaged API wrapper https://semaphoreci.com/community/tutorials/building-and-testing-an-api-wrapper-in-python,
- some tests https://docs.pytest.org/en/latest/getting-started.html and
- a unicode sandwich http://johnbachman.net/building-a-python-23-compatible-unicode-sandwich.html
Use a virtual environment, so...
pip install virtualenv
pip install virtualenvwrapper-win
or if you are not on Windows
sudo apt install virtualenv
sudo apt install virtualenvwrapper
Create a virtual enviroment for the project
mkvirtualenv yearlet
Install the supporting packages
pip install -r requirements.txt
Run the tests (the pytests don't run in Visual Studio which is a shame)
pytest
When you are done deactivate the virtual environment with
deactivate
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── 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 <- A default Sphinx project; see sphinx-doc.org for details
│
├── 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 short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── 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
│
├── src <- Unit tests for the package.
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org