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https://github.com/iterative/cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
https://github.com/iterative/cookiecutter-data-science
Last synced: 3 months ago
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A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- Host: GitHub
- URL: https://github.com/iterative/cookiecutter-data-science
- Owner: iterative
- License: mit
- Fork: true (drivendata/cookiecutter-data-science)
- Created: 2019-02-07T02:17:49.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-09-01T11:24:37.000Z (about 1 year ago)
- Last Synced: 2024-05-15T04:35:23.799Z (6 months ago)
- Language: Python
- Homepage: http://drivendata.github.io/cookiecutter-data-science/
- Size: 620 KB
- Stars: 26
- Watchers: 5
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cookiecutter Data Science
_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._
#### [Project homepage](http://drivendata.github.io/cookiecutter-data-science/)
### Requirements to use the cookiecutter template:
-----------
- Python 2.7 or 3.5+
- [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html) >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:
``` bash
$ pip install cookiecutter
```
or
``` bash
$ conda config --add channels conda-forge
$ conda install cookiecutter
```
### To start a new project, run:
------------
cookiecutter -c v1 https://github.com/drivendata/cookiecutter-data-science
[](https://asciinema.org/a/244658)
### New version of Cookiecutter Data Science
------------
Cookiecutter data science is moving to v2 soon, which will entail using
the command `ccds ...` rather than `cookiecutter ...`. The cookiecutter command
will continue to work, and this version of the template will still be available.
To use the legacy template, you will need to explicitly use `-c v1` to select it.
Please update any scripts/automation you have to append the `-c v1` option (as above),
which is available now.
### The resulting directory structure
------------
The directory structure of your new project looks like this:
```
├── LICENSE
├── Makefile <- Makefile with 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.
├── data.dvc <- A data version control file (optional); see dvc.org for details
│
├── docs <- A default Sphinx project; see sphinx-doc.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`.
│
├── 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.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── 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
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
```
## Contributing
We welcome contributions! [See the docs for guidelines](https://drivendata.github.io/cookiecutter-data-science/#contributing).
### Installing development requirements
------------
pip install -r requirements.txt
### Running the tests
------------
py.test tests