datascience-cookiecutter 0.4.3

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Table of Contents

• Motivation
• Features
• Installation
• Basic Usage
• Default Template
• Customizing Templates
• Makefile
• PDM
• pytest

🍪 Data Science Cookiecutter

The Data Science Cookiecutter 🍪 is an opinionated, yet configurable, Python project that provides a template for organizing and setting up data science projects. It uses the Cookiecutter project structure to create a standardized and reproducible project layout.

🎯 Motivation

Data science projects often require a well-structured project layout to ensure reproducibility and collaboration. The Data Science Cookiecutter aims to solve this problem by providing a project template. While it follows certain opinions about project organization, it also allows for easy customization to fit different project needs.

✨ Features

• Standardized project structure for data science projects
• Automatic generation of project files and folders
• Customizable templates for different project needs
• Customizable for multiple programming languages (currently, the default template currently only has Python)
• Easy project initialization with just a few command-line arguments

⚙️ Installation

The Data Science Cookiecutter is on pypi and can be installed using pip, poetry, pdm or conda.

pdm add datascience-cookiecutter

🚀 Basic Usage

To create a new data science project using the Data Science Cookiecutter, follow these steps:

1. Open a terminal or command prompt.
2. cd to the directory where you want to create the project.
3. Run the following command: cookiecutter myproject where myproject is the name of your project.
4. profit 🎉

📁 Default Template

├── data/
│   ├── processed
│   ├── raw
├── dev/
|    ├── notebooks
│    └── scripts
├── src/
│   ├── __init__.py
│   └── main.py
├── tests/
│   ├── __init__.py
│   └── main.py
├── references/
├── Makefile
├── README.md

🛠️ Customizing Templates

If you want to customize the default template used by cookiecutter, you can create a templates.py file in your $HOME/.config/cookiecutter directory. Follow these steps:

1. Open a text editor and create a new file called templates.py.
2. Import the necessary classes Folder and FileTemplate by adding the following lines to templates.py:

from datascience_cookiecutter import Folder, FileTemplate

Define your custom template using the Folder and FileTemplate classes. Here's a minimal example:

MYTEMPLATE = Folder(
    name="{{name}}",
    subfolders=[
        Folder(name="src", files=[FileTemplate(filename="main.py", content="print('Hello, world!')")]),
        Folder(name="data"),
        Folder(name="docs"),
    ],
    files=[
        FileTemplate(filename="README.md", content="# My {{name}}"),
    ],
)

Occurences of {{name}} will be replaced by the project name as provided with cookiecutter myprojectname. To use your custom template, simply run the cookiecutter command with the --template option followed by the name of your custom template. For example:

$ cookiecutter myproject --template=MYTEMPLATE

Enjoy customizing your templates! ✨🧙‍♂️

🛠️ Makefile

A Makefile is a file containing a set of instructions, known as targets, used to automate tasks in software development. It provides a convenient way to define and organize common commands for building, testing, and managing a project.

In the provided Makefile, you have the following targets:

• install: Installs project dependencies using pdm install.
• test: Runs project tests with pytest
• format: Applies code formatting using isort and black.
• lint: Performs linting and static type checking using ruff and mypy

To use the Makefile, open a terminal or command prompt, navigate to your project directory, and run the desired target using the make command followed by the target name. For example:

make install

🔬 pytest

Pytest is a Python testing framework that allows you to write simple and scalable tests with a clean and expressive syntax. It provides powerful features like fixtures, test discovery, and test selection.

For more information, you can visit the official pytest website: pytest.org