Python project template

• Overview
  • Purpose
• Prototyping
• Environment
  • Pipenv
  • Setting up a project
  • Installing Dependencies

Python has become one of the most widely used programming languages in the developer community.

The purpose of this template is to help you get started with general-purpose projects developed with Python.

During project development with Python, you will need to spend a few hours prototyping functions. The Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. Also, you can divide your code into logical blocks and execute them independently, as well as being able to save those blocks and return to them later. I'm using it because its features happen to align well with prototyping tasks.

Jupyter is not specific to python, so the recommendation is to install it in such a way that it is available for your entire system, outside of isolated development environments.

To install Jupyter on your system run:

python -m pip install --user jupyter

Pipenv has excellent support for managing direct dependencies while keeping track of a full set of dependency versions that are known to work correctly and ensuring that your environment is kept up to date. That makes it good fit for modern pure Python projects.

Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.

If you already have Python and pip, you can easily install Pipenv into your home directory:

pip install --user pipenv

The first step is to go to the directory of the project in which you are going to work. Then declare a new isolated development environment with ipykernel.

pipenv install ipykernel
pipenv run ipython kernel install --user --name=yourprojectname

With this setup, you can start jupyter and see the kernels that you have installed in your system and act as an isolated environment. This allows us to select the kernel without having to activate this isolated environment manually each time.

Installed kernles can be listed with

jupyter kernelspec list

and removed with

jupyter kernelspec remove

With the project setup ready, now you can start prototyping your application.

The pip install command adds the dependency to Pipfile and the particular version that is picked to Pipfile.lock . Files with the extension .lock are often added to the ignore set in version control. You should make an exception for Pipfile.lock as it helps when reconstructing old environments and performing repeatable deployments. When we return to the notebook, we need to restart the kernel to ensure the new dependency is available. Click the Kernel menu, then restart.