Cookiecutter Data Science
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 of cookiecutter.
Installation
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
Starting a new project
To start a new project, run:
ccds
The resulting directory structure
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
Using unreleased changes
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
Using v1
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
Contributing
We welcome contributions! See the docs for guidelines.
Installing development requirements
pip install -r dev-requirements.txt
Running the tests
pytest tests
这是 Cookiecutter Data Science(CCMS,即 CCDS),一个用来搭建数据科学项目骨架的工具。它基于一个内置了最佳实践的模板,让每个项目都拥有合理、规范而又灵活的结构。