cookiecutter-python-project

A template for python projects made using cookiecutter.

Structure

.
├── LICENSE
├── environment.yml
├── .gitignore
├── README.md
├── setup.py
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── figures
├── notebooks
├── references
├── reports
└── src
    ├── __init__.py
    ├── analysis
    ├── etl
    ├── utils
    └── viz

• LICENSE
  • License file for the project.
  • Availiable options include MIT and BSD-3-Clause.
• environment.yml
  • The requirements file to reproduce the analysis environment.
• .gitignore
  • Ignores python user profile temporary files.
• README.md
  • Project specific readme.
• setup.py
  • Makes project pip installable with pip install -e ../.
• data
  • This is the directory used to store all of the project's data. All files should go into one of the following folders.
  • data/external
    • Data from third party sources.
  • data/interim
    • Intermediate data that has been transformed.
  • data/processed
    • The final, canonical data sets for analysis.
  • data/raw
    • The original, inmutable data dump.
• figures
  • Generated graphics and figures to be used in reporting.
• notebooks
  • Any Jupyter Notebooks go here.
• references
  • Data dictionaries, manuals, and all other exploratory materials.
• reports
  • Generated analysis as HTML, PDF, LaTeX, etc.
• src
  • All the scripts in the project go here.
  • src/__init__.py
    • Makes src a Python module.
  • src/analysis
    • Code that involves analysis on already-cleaned data. Code for cleaning data should go in src/etl.
    • Multiple analysis files are numbered sequentially.
  • src/etl
    • ETL (extract, transform, load) scripts for reading in source data, cleaning and standardizing it to prepare for analysis go here.
    • Joins are included in ETL process.
    • Multiple ETL files are numbered sequentially.
  • src/utils
    • Miscellaneous code goes here.
  • src/viz
    • Graphics and visualization development specific work should go here.
    • Multiple viz files are numbered sequentially.

Requirements

• Conda
• Cookiecutter Python package

This can be installed using either

pip install cookiecutter

or

conda install -c conda-forge cookiecutter

Installation

In the folder where you want to generate the project, run:

cookiecutter https://github.com/camartinezbu/cookiecutter-python-project

If you wish to create a conda environment based on the environment.yml file, run:

# Go to the project's directory:
cd DIRECTORY_NAME

# Create a conda environmenT:
conda env create --file environment.yml

# Activate said environment:
conda activate ENVIRONMENT_NAME

Both DIRECTORY_NAME and ENVIRONMENT_NAME correspond to the project's slug name, defined when creating the template.

Set up project's module

In order to set up the project's module, in the terminal run:

pip install -e ../

Or if you are in a notebook, run in a code cell:

! pip install -e ../

To use the module inside the notebook, add the following to the first cell:

%load_ext autoreload
%autoreload 2

Credits

This template was designed based on jvelesmagic's Cookiecutter Conda Data Science.

Extra

Check out a similar template for R here.