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# Cookiecutter Data Science - Conda
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# Cookiecutter Data Science - using poetry and justfiles
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_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._
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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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- Python 3.9+
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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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The directory structure of your new project looks like this:
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```
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├── Makefile <- Makefile with commands like `make data` or `make train`
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├── justfile <- justfile with commands like `just data` or `just 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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│ ├── interim <- Intermediate data that has been transformed.
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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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├── nbs <- 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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├── requirements <- The requirements directory for reproducing the analysis environment
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├── pypoetry.toml <- The requirements directory for reproducing the analysis environment
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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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### 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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poetry install
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