Using Git for Version Control

This chapter aims to prepare you for interview questions related to Git, a version control system integral to collaborative projects and data management.

Throughout these sections, you’ll delve into the basics of creating and managing repositories and common Git operations, such as config, status, push, pull, ignore, commit, and diff. We will also highlight the common workflow patterns for a data scientist using Git and the crucial role of branches in this workflow.

The goal is to equip you with practical knowledge that you can leverage during your technical interviews, enabling you to demonstrate not only your data science acumen but also your adeptness at utilizing essential collaboration tools. Understanding these concepts is pivotal in today’s data science landscape, as efficient version control and collaboration are as critical to a project’s success as the scientific methods employed.

In this chapter, we will cover the...

Introducing repositories (repos)

Repos are a version control system in a centralized storage location, holding all the files, directories, and version history of a project. A repository allows multiple developers to collaborate on a project, keeping track of changes made to the project’s files over time, which is useful for projects with multiple data scientists and developers. It stores all the different versions of the files, along with metadata such as the author, timestamp, and description of each change.

There are many version control options that organizations might use. Some popular options include GitHub, BitBucket, GitLab, Azure DevOps repositories, and AWS CodeCommit.

It’s important to note that there are multiple phases of version control. The major three are repos, a working directory, and a staging area. We’ve already explained what a repo is, but what are the other two?

A working directory is the directory on your local machine where you...

Creating a repo

In this section, we’ll cover the essential steps for creating a GitHub repository from an existing remote repository, as well as creating a local repository without an existing remote repository. Then, we will look at linking a local and remote repository. Let’s begin!

Cloning an existing remote repository

When working as a part of a project team, a central repository has likely already been created. If you are working with a project that already exists, use the clone command to make a local copy of the repository. Cloning allows you to have a local copy of the project on your own computer, where you can work on it offline, experiment with it, and contribute your changes back to the project if you wish.

Here’s how to clone a repository:

1. Retrieve a copy of the remote repository URL. If GitHub is your remote repository, then this can be found under the green Code button, currently on the Code tab of a project.
2. Open the terminal...

Detailing the Git workflow for data scientists

Understanding Git workflows is a key competency for data scientists. As we’ve discussed before, Git allows you to track changes, revert to previous versions, and collaborate with others. In this section, we’ll describe a typical Git workflow for a data scientist and explain the concept of a branch, an important feature in Git.

A branch in Git is essentially a unique set of code changes with a unique name. Each repository has one default branch (usually called master or main) and can have multiple other branches. The branches are used to develop features isolated from each other. When you want to create a new feature or experiment with something without disturbing the main line of development, you create a new branch. If the experiment is successful, you can merge these changes into the main branch. If it’s unsuccessful, you can discard the branch, and it won’t affect your main branch or repository.

Here...

Using Git tags for data science

Tagging in Git is a way to mark specific points in your repository’s history as being important. Typically, people use this functionality to mark release points (v1.0, v2.0, and so on). In this section, we’ll cover the concept of tagging and how it can benefit data scientists.

Understanding Git tags

There are two types of tags that Git recognizes, lightweight and annotated. A lightweight tag is similar to a branch that doesn’t change. It’s just a pointer to a specific commit. Annotated tags, however, are stored as full objects in the Git database. Using the annotated tag is generally recommended because it is fully tracked and contains more info than the lightweight tag.

To create an annotated tag in Git, you can use the git tag -a command, followed by the tag name (usually the version), and then the message, such as the following:

git tag -a v1.0 -m "my version 1.0"

To view the tags in your repository...

Understanding common operations

Understanding the basic commands of Git is paramount for anyone working in the field of data science. In the previous section, we delved into how to set up a GitHub repository, either by cloning an existing repository or starting a new one from scratch. In this section, we will explore common Git operations that will help you manage your repositories more effectively.

So, let’s take a look at some operations:

• Configuring Git (config): Git’s configuration settings can be found in the .gitconfig file, which is usually located in the user’s home directory. To modify these settings, use the git config command. Set your name and email address, which will be attached to each commit you make:

  git config --global user.name "Your Name"
  git config --global user.email "[e-mail]"

  Check your settings:

  git config --list

• Checking the status (status): The git status command provides information about the...

Summary

In this chapter, we explored the core fundamentals of Git, an essential tool for data scientists looking to effectively manage and collaborate on projects. We kicked things off by guiding you through setting up a GitHub repository. This involved the creation of a new repository, both from scratch and by cloning an existing remote repository. We provided a step-by-step walk-through, offering a straightforward approach to establishing and preparing your local repository for development work.

Following this, we navigated through the common Git operations that form the backbone of interaction with this tool. We explored essential commands such as config, status, push, pull, ignore, commit, and diff, laying out their functions and demonstrating their usage with practical examples. Additionally, we delved into the concept of branches, a critical feature of Git that allows you to segregate your changes and efficiently manage different project versions, using tags to highlight specific...