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https://github.com/jvelezmagic/cookiecutter-conda-data-science


https://github.com/jvelezmagic/cookiecutter-conda-data-science

conda cookiecutter cookiecutter-python3 cookiecutter-template data-science mamba python python3 template-project

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README 

        
# Cookiecutter Conda Data Science



_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._



## Requirements



- [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html)

- [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html): This can be installed with pip by or conda depending on how you manage your Python packages:



``` bash

pip install cookiecutter

```



or



``` bash

conda install -c conda-forge cookiecutter

```



## Create a new project



In a folder where you want your project generated:



```bash

cookiecutter https://github.com/jvelezmagic/cookiecutter-conda-data-science

```



## Resulting directory structure



    ├── LICENSE

    ├── tasks.py           <- Invoke with commands like `notebook`.

    ├── README.md          <- The top-level README for developers using this project.

    ├── install.md         <- Detailed instructions to set up 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.

    │

    ├── 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.

    │

    ├── environment.yml    <- The requirements file for reproducing the analysis environment.

    │

    ├── .here              <- File that will stop the search if none of the other criteria

    │                         apply when searching head of project.

    │

    ├── setup.py           <- Makes project pip installable (pip install -e .)

    │                         so {{ cookiecutter.project_module_name }} can be imported.

    │

    └── {{ cookiecutter.project_module_name }}               <- Source code for use in this project.

        ├── __init__.py    <- Makes {{ cookiecutter.project_module_name }} 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

        │

        ├── utils          <- Scripts to help with common tasks.

            └── paths.py   <- Helper functions to relative file referencing across project.

        │

        └── visualization  <- Scripts to create exploratory and results oriented visualizations.

            └── visualize.py



## Contributing guide



All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.



## Credits



This project is heavily influenced by [drivendata's Cookiecutter Data Science](https://github.com/drivendata/cookiecutter-data-science), [andfanilo's Cookiecutter for Kaggle Conda projects](https://github.com/andfanilo/cookiecutter-kaggle), and julia's package [DrWatson](https://juliadynamics.github.io/DrWatson.jl/dev/).



Other links that helped shape this cookiecutter :



- [Write less terrible code with Jupyter Notebook](https://blog.godatadriven.com/write-less-terrible-notebook-code)

- [Cookiecutter DataScience Opinions](http://drivendata.github.io/cookiecutter-data-science/#opinions)