Well structured and tested data science project template. You can use this template when creating the data sicence repository.
๐ Organized: The project structure is refereed to Cookiecutter Data Science
๐ Prepared: Major libraries are prepared in environment.yml
. If you are familiar with Colaboratory environment, please use environment-colab.yml
.
โ
Tested: scripts
are checked by common linter when pre-commit.
Here is the notebook link to provide the quick access to your analysis. You can create the conda environment by Right click Build Conda Environment
or conda create -f environment.yml
in Studio Lab.
.
โโโ data
โ โโโ external # data from third party sources.
โ โโโ processed # data after processing
โ โโโ interim # data that transformed
โ โโโ raw # raw data
โโโ models # store models
โโโ notebooks # store notebooks
โโโ docs # documentation for your project
โโโ .gitignore # ignore files that cannot commit to Git
โโโ .pre-commit-config.yaml # configurations for pre-commit
โโโ pyproject.toml # dependencies for poetry
โโโ README.md # describe your project
โโโ scripts # store source code used in notebook
โ โโโ __init__.py # make src a Python module
โโโ tests # store tests
โโโ __init__.py # make tests a Python module
environment.yml
: Please specify the packages and versions. As a default, no version is specified..pre-commit-config.yaml
: Please check therev
to check the code.- Change the Notebook url for
Open in Studio Lab
.