Python + Chimps

Today, I built a Python project to interact with the Mailchimp API. This post captures the workflow I followed, along with key lessons learned.

1. Setting Up the Environment

Open VS Code and Create the Project Directory

First, I navigated to my GitHub folder on my local machine.

Using the terminal, I created a new directory for my project:

mkdir mailchimp

Then, I set my working directory:

cd mailchimp

Create a Python Virtual Environment

To isolate my project dependencies, I created a virtual environment:

python -m venv mailchimp_env

Prepare the Environment

1. Bash Terminal Setup: I used a bash terminal in VS Code to confirm my file path with cd.
2. Create a .env File:
   I created a blank .env file to securely store my Mailchimp credentials: touch .env
   After adding my Mailchimp credentials, I saved the file (I actually didn't at first but eventually figured out what I'd done wrong).
3. Create a .gitignore File:
   To prevent sensitive and unnecessary files from being added to Git, I created a .gitignore file:touch .gitignore
   Inside .gitignore, I added:
   *.env *.*_env *\data*
   
4. Activate the Python Environment:
   After creating the environment, I activated it and performed a quick test to ensure everything was set up.

2. Version Control with Git and GitHub

Initialize Git Repository

I initialized an empty Git repository locally:

git init

Next, I staged all my project files:

git add .

Finally, I committed the staged files with a meaningful message:

git commit -m "Initial commit"

Line Ending Configuration

During this step, I noticed the line endings were automatically converted from LF to CRLF. This is how new lines are formatted on Windows systems, so it’s not an issue.

Connect to GitHub

I went to GitHub and created a new repository for my project without a README file. Back in my terminal, I linked the local repository to the remote repository:

git remote add origin <link to repository>.git

To push my work to GitHub:

git push -u origin master

The -u flag sets the upstream branch, which makes future pushes simpler.

Master and main are names of branches which can be used interchangeably. This isn't a problem if I have either main or master branch.

3. Branching for Feature Development

To follow good development practices, I created a new branch to work on my Python script:

git checkout -b python-feature

I wrote my Python script in this branch and only published it to master when it was in a stable or meaningful state.

4. Installing Necessary Packages

In the PowerShell terminal, I installed the required packages for the project:

1. python-dotenv: To load environment variables from the .env file.
   
2. Mailchimp Python SDK:
   Mailchimp offers an official Python client library, which I installed directly from their GitHub repository.
   

Note: The os module, which I needed to access environment variables, is a native Python library and doesn’t require pip installation.

5. Mailchimp API Objectives

Here’s an outline of the data I want to extract from Mailchimp via its API:

• Campaigns
• Email Activity (requires campaign IDs)
• Lists
• List Members (requires list IDs from the Lists endpoint)
• Reports
• Unsubscribes (requires campaign IDs)

By the end of this project, I aim to have a set of scripts that can extract, process, and store these datasets for further analysis.

Key Takeaways

• Environment Setup: Setting up a virtual environment and .env file is essential for clean and secure development.
• Git Workflow: Syncing with GitHub and creating feature branches ensures smooth collaboration and version control.
• Mailchimp SDK: The official Python library simplifies interaction with the Mailchimp API.
• Planning API Calls: Identifying dependencies (like needing campaign IDs for certain endpoints) is crucial for efficient API workflows.