Appendix A — Field Guide to Python

In this appendix, we focus on how to get up-and-running in Python, and how you can make your Python environment reprodicible. Once you have established this, RStudio have a useful guide for how to “think” in Python, knowing R.

A.1 Python installation

The first order of business is to make sure that you have a recent version of Python installed. By recent, I mean one of the last two minor version; as of February 2022, these are versions 3.10 and 3.9.

To check your default Python version, just type python -V (note the captial) at the terminal command line (not the R command line). This is what I see:

> python -V
Python 3.10.2

There is a variety of strategies for managing Python on your computer, perhaps the simplest is to go to the Python downloads page, then go from there.

A.2 Project management

A.2.1 Git

If you are creating your project directory from scratch, you will likely want to initialize a git repository in your newly-created directory:

> git init

You will also want a .gitignore file for your project. Here’s my .gitignore file for the Dash demo app; I include some RStudio stuff for if/when I open the project in RStudio.

# RStudio stuff
.Rproj.user
*.Rproj
.Rhistory
.Rdata

# virtual environment
venv

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

You may wish to adapt the virtual-environment entry to your situation.

A.2.2 Virtual Environment

Python virtual environments are used to manage dependencies so that they are local to a project. This is a different from the classic R idea of having a single library of packages used for all projects.

The idea of a project-based is also used in JavaScript (e.g. npm, yarn) and is gaining popularity in R with the renv package. In fact, this book is build using renv.

The goal here is to show you how to establish and manage a virtual environment for a Dash project.

In your newly-created project directory, from the terminal command-line:

> python -m venv ./venv

This creates your Python virtual environment by creating a directory in the root of your project called venv. The name of the directory is determined by the last argument, in this case ./venv. There are a number of “standard” ways to name virtual environments; "venv" is one of them. It’s really up to you and your collaborators.

The important thing is to make sure that you have a .gitignore entry for the virtual-environment directory.

Next, let’s activate the environment. This tells your terminal that this is what you want to run when you invoke python.

In your project directory, from the terminal command-line:

> source ./venv/bin/activate

At this point, you might want to install packages into your virtual enviromment. Which packages will depend on the particulars of your project, but you can start with Dash:

> pip install dash

Every so often, you will want to catpure which packages have been installed into your virtual environment:

> pip freeze > requirements.txt

You will want to commit requirements.txt to your git repository, as this contains the instructions for someone to reproduce your virtual environment.

To reproduce it, a colleague (perhaps you!) will have to create and activate a virtual environment, then:

> pip install -r requirements.txt

These are the very basics for how to set up and maintain a Python project. As you gain experience, you will likely adapt these ideas to your evolving needs.