
cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
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Cookiecutter Data Science (CCDS) is a tool for setting up a data science project template that incorporates best practices. It provides a logical, reasonably standardized but flexible project structure for doing and sharing data science work. The tool helps users to easily start new data science projects with a well-organized directory structure, including folders for data, models, notebooks, reports, and more. By following the project template created by CCDS, users can streamline their data science workflow and ensure consistency across projects.
README:
A logical, reasonably standardized but flexible project structure for doing and sharing data science work.
Cookiecutter Data Science (CCDS) is a tool for setting up a data science project template that incorporates best practices. To learn more about CCDS's philosophy, visit the project homepage.
ℹ️ Cookiecutter Data Science v2 has changed from v1. It now requires installing the new cookiecutter-data-science Python package, which extends the functionality of the cookiecutter templating utility. Use the provided
ccds
command-line program instead ofcookiecutter
.
Cookiecutter Data Science v2 requires Python 3.9+. Since this is a cross-project utility application, we recommend installing it with pipx. Installation command options:
# With pipx from PyPI (recommended)
pipx install cookiecutter-data-science
# With pip from PyPI
pip install cookiecutter-data-science
# With conda from conda-forge (coming soon)
# conda install cookiecutter-data-science -c conda-forge
To start a new project, run:
ccds
The directory structure of your new project will look something like this (depending on the settings that you choose):
├── LICENSE <- Open-source license if one is chosen
├── Makefile <- Makefile with convenience 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 mkdocs project; see www.mkdocs.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`.
│
├── pyproject.toml <- Project configuration file with package metadata for
│ {{ cookiecutter.module_name }} and configuration for tools like black
│
├── 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`
│
├── setup.cfg <- Configuration file for flake8
│
└── {{ cookiecutter.module_name }} <- Source code for use in this project.
│
├── __init__.py <- Makes {{ cookiecutter.module_name }} a Python module
│
├── config.py <- Store useful variables and configuration
│
├── dataset.py <- Scripts to download or generate data
│
├── features.py <- Code to create features for modeling
│
├── modeling
│ ├── __init__.py
│ ├── predict.py <- Code to run model inference with trained models
│ └── train.py <- Code to train models
│
└── plots.py <- Code to create visualizations
By default, ccds
will use the project template version that corresponds to the installed ccds
package version (e.g., if you have installed ccds
v2.0.1, you'll use the v2.0.1 version of the project template by default). To use a specific version of the project template, use the -c/--checkout
flag to provide the branch (or tag or commit hash) of the version you'd like to use. For example to use the project template from the master
branch:
ccds -c master
If you want to use the old v1 project template, you need to have either the cookiecutter-data-science package or cookiecutter package installed. Then, use either command-line program with the -c v1
option:
ccds https://github.com/drivendataorg/cookiecutter-data-science -c v1
# or equivalently
cookiecutter https://github.com/drivendataorg/cookiecutter-data-science -c v1
We welcome contributions! See the docs for guidelines.
pip install -r dev-requirements.txt
pytest tests
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