mirror of
https://github.com/wassname/cookiecutter-data-science.git
synced 2026-06-27 18:42:27 +08:00
Isaac Slavitt
08d357c5fa
Official support for Python 2 ended on Jan 1, 2020 (see https://www.python.org/doc/sunset-python-2/ for more info). This change does not prevent anybody from using Python 2, but we should not be encouraging new projects to use it.
99 lines
3.4 KiB
Markdown
99 lines
3.4 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 3.6+
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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/244658)
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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.readthedocs.io
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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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