Python Package Best Practices: Setup

This setup tutorial will walk you through installing the software you will need for this workshop.

For this workshop, you will need to have Python installed. We recommend and assume you will have Python installed using Anaconda, and will be talking about package management using conda and Anaconda (see instructions below). You will also need to download workshop materials, and configure Git.

We will cover the following topics. Click on a particular topic to skip to that section.

  1. Downloading workshop materials
  2. Installing Python using Anaconda
  3. Downloading a text editor
  4. Creating a Python environment using conda
  5. Installation of cookiecutter
  6. Installing and Configuring Git
  7. Creating a GitHub Account

Workshop Preparation

In this workshop, we will be moving code from a Jupyter notebook into a Python package that we can install and import into other scripts.

Installing Python Using Anaconda

If you already have a Python 3 version Anaconda or MiniConda installed, you can skip this step.

Python is a popular language for scientific computing, and great for general-purpose programming as well. We recommend using Python with the conda package manager. You can obtain Python and conda by either installing Miniconda (minimal installer) or Anaconda. Anaconda comes bundled with many more Python packages than miniconda. Anaconda is more user-friendly. If you are uncomfortable navigating the terminal, or are unsure of which you need, install Anaconda.

The installer for Anaconda can be found on this page. Choose the appropriate version for your Operating System.

Throughout the rest of this set-up, we will assume that you are using the conda package manager.

Please set up your Python environment at as far in advance as possible. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance, so you are ready to go as soon as the workshop begins.

Windows Subsytem for Linux (WSL)

If you plan to continue developing in the future and you utilize the Windows Operating system, you may want to consider installing the WSL. The WSL will allow you to run a Linux based terminal from your Windows machine, which can aid in the development and running of code. The WSL can connect to VS code for easy development. You can find instructions to install the WSL on the Windows Documentation.

Choosing a text editor

You will need an editor for Python files for this workshop. If you do not have a preferred text editor, we recommend Visual Studio Code. You can enable use Visual Studio Code from WSL by installing the Remote Development Extension. If you are on Mac, you may also configure VS Code to open from the command line. If you have another preferred text editor, you should use that for the workshop.

Using Anaconda and conda

Use of Anaconda with its package manager, conda, greatly simplifies package installation and environment management.

conda is a general package manager, meaning that it can install dependencies and packages in languages besides Python, unlike pip (which is Python’s package manager). Both pip and conda can be used to install packages.

Python environments

A conda environment contains a specific collection of packages you have installed. This means that packages are isolated, and installed only for a specific environment – you can have several environments each with different installed packages, or different versions of installed packages in different environments.

It’s considered a best practice to create a new Python environment for each project you work on.

This section uses the command line interface (CLI) to create an environment using conda. If you are on Mac or Linux, you will type these commands into your terminal. If you are on Windows, you should use the Anaconda Prompt.

To create an environment for this project using conda,

$ conda create --name molssi_best_practices python=3.9

For other projects, you should replace molssi_best_practices with a descriptive name for your project. conda also allows you to specify the Python version to use with the environment. Here, python=3.9 specifies that we want to use Python 3.9 in this environment. Executing this command will list the environment location and a list of Python packages to be installed. Choose y(es) when prompted.

Activate the environment using the command

$ conda activate molssi_best_practices

Once you’ve activated an environment, the name of the environment will be in parentheses at the front of your command line prompt.

If you wanted to create an environment for testing your code in Python 3.6, for example, you could use the command (Do not execute this, it’s just an example.)

$ conda create --name molssi_36 python=3.6

When this environment is activated, Python 3.6 will be used instead of Python 3.9.

To see a list of all your environments

$ conda info --envs

To deactivate an environment, type

$ conda deactivate

Package installation using conda

Using conda, we can install packages to our environments. Note: Make sure you have activated the environment where you want to install packages.

$ conda activate molssi_best_practices

To list all the Python packages installed in an environment, first activate it, then type

$ conda list

Packages can be installed using the conda install package_name command. For example, to install NumPy (do not execute this, we will install NumPy later),

$ conda install numpy

Further, the desired version of NumPy can be specified:

$ conda install numpy=1.15

For this workshop, you will need to install the following packages into your environment

Packages available to Conda are stored within channels. Some packages are not stored in the default Conda channel, so we need to specify where Conda can find the package with -c followed by a channel name in our installation command. The NumPy and Matplotlib packages are both stored within the default Conda channel, so we do not need to include a -c. The jupyter notebook package is in the conda-forge channel, so we include the syntax -c conda-forge in our installation command.

$ conda install numpy matplotlib
$ conda install -c conda-forge notebook

CookieCutter Installation

For this workshop, we will create the structure of our Python package using the CMS CookieCutter. Please have this package installed in your molssi_best_practices environment.

First, switch to your environment for this workshop if you are not in it.

$ conda activate molssi_best_practices

Install the general cookiecutter with the following commands.

$ conda config --add channels conda-forge
$ conda install cookiecutter

Installing and configuring Git


Install Git. You can install using conda

$ conda install -c conda-forge git

Configuring Git

The first time you use Git on a particular computer, you need to configure some things.

First, you should set your identity. One of the most important things that version control like Git does is to keep track of who changes what. This helps repository maintainers coordinate the efforts of all the people who contribute to the project. Most importantly, it makes it easier to figure out who to blame when something goes wrong. You can provide Git your name and contact information with the following commands:

$ git config --global "YOUR NAME"
$ git config --global "YOUR_EMAIL"

You should next set the default branch name you will work on. Branches will be explained in detail later. This configuration setting makes the default branch name match the default branch name on GitHub

git config --global init.defaultBranch main

Next, you might want to change the Git text editor. As we will see later, certain Git commands will open text files. When this happens, Git will use your environment’s default text editor, which might not be the editor you are most comfortable using. Using configuration commands, you can tell Git to use your favorite editor.

A popular chose is Vim. To use Vim, do

$ git config --global core.editor "vim"

A more complete list of possible editors is available here.

To use VSCode as your core editor, do

$ git config --global core.editor "code --wait"

You can check the configuration commands that you have set using:

$ git config --list

Create GitHub Account

Create an account on if you do not have one already. Remember the username and password. If you are making a GitHub account, please remember that your username should be recognizable and professional.

The next step is to verify your email address through GitHub. If you do not verify your email address, you will not be able to perform many of the actions covered in this workshop.

Accessing GitHub through the Command Line

To utilize GitHub from the command line, you will need to provide GitHub with an SSH public key. An SSH key is a means to authenticate remote servers. With an SSH key, you will be able to access your GitHub repositories through the command line without the need to enter a username and password. The linked documentation provides information about SSH keys, how to find out if you already have an SSH key, and how to generate and add one to your GitHub account. You should click the link and follow the instructions to add an SSH key to your GitHub account.


At the end of this set-up, you should have created a Python environment (molssi_best_practices) which has Python 3.9, numpy, matplotlib, jupyter, and cookiecutter installed. You should also have downloaded starting material, installed and created an account on GitHub, and configured Git.