Python Templates

Kick-start your next Python project with the right template and establish good coding practices from day 1.

Using a template can help you quickly bootstrap a new project by eliminating the need to write common boilerplate framework/language setup from scratch. Templates are also a good place to include starting points for good practices like linting, unit-testing, documentation, formatting and shared standard configuration. Here are 5 templates that I’ve evolved for my Python projects that you might find useful for yours.

Using the templates

Each of these are available on Github as a template repository. This makes it easy to create a new project by selecting the template you want to use and clicking the Use this template button to create a new repository using the template as a basis. Note that this is not the same as forking a repository (but you can still do that too).

Once you’ve created your new repository, you can tailor it to your needs by adding your own functionality and stripping out anything you don’t need.

About the templates

I’ve tried to balance each of these templates by providing enough of a starting point to be useful while not throwing in a bunch you probably won’t need. All of the templates include a few standard items that can promote consistency and better coding practices, including:

  • An .editorconfig file. This is a configuration file that is recognized by most popular IDEs and text editors that standardizes things like end-of-line handling, margins, tab styles and other common editor settings. By including this file, everyone who edits the code will automatically inherit the same settings to help promote consistency.
  • A .gitignore file to avoid adding generated and other files to the repository that should not be checked in.
  • PyLint and a .pylintrc file to encourage clean Python code.
  • Black for standardized formatting.
  • PyTest and sample unit-tests.

Packaging and dependency management

The templates use one of two approaches for packaging and dependency management:

  • setuptools - for packages that can be published and installed with pip.
  • pipenv - for applications, utilities and other types of programs.

setuptools is best suited for shared package projects such as libraries or frameworks that you intend to publish on PyPI or another artifact repository.

pipenv is best suited for other types of projects such applications, command-line utilities and scripts that will not be published. It automatically creates your project’s virtual environment and tracks installed dependencies so that your app can be installed elsewhere reliably.

See Pipfile vs. for more discussion on these two approaches.


setuptools-based templates include a Sphinx documentation scaffold for generating formal package docs.

pipenv-based templates simply use markdown for documentation that can be viewed in Github or Gitlab.

The templates

1) Python App Template

Python App Template


This is a general-purpose application template that features:

  • Predictable installation and package management with Pipenv.
  • PyTest unit-test support
  • PyLint and Black
  • An .editorconfig file
  • Command-line argument parsing and color output support with Click

2) Flask Quick Start Template

Flask Template


A template for creating a stand-alone Flask-based web application that serves HTML and/or RESTful API endpoints. It features:

  • Predictable installation and package management with Pipenv.
  • Flask 2.x and blueprints
  • Jinja2 HTML templates
  • SQLAlchemy database models
  • Marshmallow for object marshalling
  • JSON Web Token (JWT) authentication
  • PyTest unit-tests
  • Serve in production with waitress
  • Pylint and Black

3) Flask Service Quick Start Template

Flask Service Template


A template for creating Python microservices based on Flask. Featuring:

  • Predictable installation and package management with Pipenv.
  • Flask 2.x and blueprints
  • JSON Web Token (JWT) authentication using public key token validation
  • PyTest unit-tests
  • Serve in production with waitress
  • Pylint and Black
  • Sample Docker container

4) Python Package Boilerplate


Boilerplate template for creating installable Python packages. Featuring:

  • setuptools packaging
  • Support for installing development dependencies through
  • PyTest unit-test support
  • PyLint
  • An .editorconfig file
  • Sphinx documentation generation
  • pyproject.toml PEP 517/518 support

5) Python CLI Package Boilerplate


A variation of the Python Package Boilerplate template that includes a command-line interface and color output using Click.