A file structure template, development environment and rule set for python data analytics projects on the data analytics team
Change the name of folder that contains this whole repo: python-project-template -> {your project name}
From within the repo directory, first remove git tracking from the project
rm -rf .git
The project template uses a placeholder name of 'da-project'. Change that name in the following files/directories (relative to the repo root):
da-project/(change the name of the folder)./docker/run/./docker/build/
If you have not already done so, build the Docker image (you will only need to do this once)
docker/build
Run a Docker container:
docker/run
This will open a bash shell within the Docker container. Within the container the 'project' directory on the host machine (as specified as a parameter of run above) will map to /opt/src/ within the container. You can now access the full file structure of this template from within the container.
Run a Jupyter Notebook within Docker container:
docker/jupyter
You will need to open the link that is displayed in your terminal.
To exit:
exit
Initialize a new git repository:
git init
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── interm <- Intermediate data that has been transformed
│ ├── processed <- The final, canonical data sets for modeling
│ └── raw <- The original, immutable data dump
│
├── guide <- A set of markdown files with documented best practices, guidelines and rools for collaborative projects
│
├── 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
│
└── da-project <- Source code for use in this project.
│
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience