Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/drivendata/cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
https://github.com/drivendata/cookiecutter-data-science
ai cookiecutter cookiecutter-data-science cookiecutter-template data-science machine-learning
Last synced: about 2 months ago
JSON representation
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- Host: GitHub
- URL: https://github.com/drivendata/cookiecutter-data-science
- Owner: drivendataorg
- License: mit
- Created: 2015-10-30T19:19:57.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-06-05T02:41:34.000Z (3 months ago)
- Last Synced: 2024-06-13T11:28:01.235Z (3 months ago)
- Topics: ai, cookiecutter, cookiecutter-data-science, cookiecutter-template, data-science, machine-learning
- Language: Python
- Homepage: https://cookiecutter-data-science.drivendata.org/
- Size: 1.12 MB
- Stars: 7,766
- Watchers: 118
- Forks: 2,379
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- License: LICENSE
Awesome Lists containing this project
- awesome-starred - cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. (Python)
README
# 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](https://cookiecutter-data-science.drivendata.org/).
> 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](https://cookiecutter.readthedocs.io/en/stable/README.html) templating utility. Use the provided `ccds` command-line program instead of `cookiecutter`.
## Installation
Cookiecutter Data Science v2 requires Python 3.8+. Since this is a cross-project utility application, we recommend installing it with [pipx](https://pypa.github.io/pipx/). Installation command options:
```bash
# 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:
```bash
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 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:
```bash
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](https://cookiecutter-data-science.drivendata.org/contributing/).
### Installing development requirements
```bash
pip install -r dev-requirements.txt
```
### Running the tests
```bash
pytest tests
```