99 lines
3.5 KiB
Markdown
99 lines
3.5 KiB
Markdown
# Cookiecutter Data Science
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_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._
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#### [Project homepage](http://drivendata.github.io/cookiecutter-data-science/)
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### Requirements to use the cookiecutter template:
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-----------
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- Python 2.7 or 3.5
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- [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:
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``` bash
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$ pip install cookiecutter
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```
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or
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``` bash
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$ conda config --add channels conda-forge
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$ conda install cookiecutter
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```
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### To start a new project, run:
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------------
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cookiecutter https://github.com/drivendata/cookiecutter-data-science
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[](https://asciinema.org/a/9bgl5qh17wlop4xyxu9n9wr02)
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### The resulting directory structure
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------------
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The directory structure of your new project looks like this:
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```
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├── LICENSE
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├── Makefile <- Makefile with commands like `make data` or `make train`
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├── README.md <- The top-level README for developers using this project.
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├── data
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│ ├── external <- Data from third party sources.
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│ ├── interim <- Intermediate data that has been transformed.
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│ ├── processed <- The final, canonical data sets for modeling.
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│ └── raw <- The original, immutable data dump.
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│
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├── docs <- A default Sphinx project; see sphinx-doc.org for details
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│
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├── models <- Trained and serialized models, model predictions, or model summaries
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│
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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│ the creator's initials, and a short `-` delimited description, e.g.
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│ `1.0-jqp-initial-data-exploration`.
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│
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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│
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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│ └── figures <- Generated graphics and figures to be used in reporting
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│
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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│ generated with `pip freeze > requirements.txt`
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│
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├── src <- Source code for use in this project.
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│ ├── __init__.py <- Makes src a Python module
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│ │
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│ ├── data <- Scripts to download or generate data
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│ │ └── make_dataset.py
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│ │
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│ ├── features <- Scripts to turn raw data into features for modeling
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│ │ └── build_features.py
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│ │
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│ ├── models <- Scripts to train models and then use trained models to make
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│ │ │ predictions
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│ │ ├── predict_model.py
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│ │ └── train_model.py
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│ │
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│ └── visualization <- Scripts to create exploratory and results oriented visualizations
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│ └── visualize.py
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│
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└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
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```
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## Contributing
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We welcome contributions! [See the docs for guidelines](https://drivendata.github.io/cookiecutter-data-science/#contributing).
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### Installing development requirements
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------------
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pip install -r requirements.txt
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### Running the tests
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------------
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py.test tests
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