How to Use the Python GitHub API: Step-by-Step Tutorial

Automating work with repositories, issues, and users has become much simpler thanks to the Python GitHub API. This tool lets you programmatically manage every aspect of your GitHub workflows, which is especially helpful for team collaboration, CI/CD setup, and project activity monitoring.

Benefits of Automation with the GitHub Python API

Using the Python GitHub API unlocks extensive opportunities to streamline repository operations. It helps save time, reduce repetitive tasks, and lower the risk of errors that often occur when performing the same actions manually. With automation, the team can focus more on writing code and solving complex technical problems.

Key advantages:

• Time savings through automated execution of routine tasks (creating issues, updating pull-request statuses, tracking changes).
• Higher team productivity and reduced administrative overhead.
• Fast reaction to repository events via automated email or messenger notifications.
• Centralized management of multiple GitHub repositories within a single organization.
• Consistent adherence to development and code-review standards.
• Easy integration with other services (CI/CD, Jira, Trello, Notion), enabling end-to-end automation pipelines.

Taken together, using the Git API with Python not only accelerates day-to-day workflows but also creates a foundation for scalable, more transparent, and efficient development across teams of any size.

How to Use GitHub API with Python

To get started with the API in Python, first create a personal access token (PAT) on GitHub. Open your account settings, scroll down, and go to Developer settings → Personal access tokens → fine-grained tokens.

You’ll need to fill out:

• Token name – the token’s name.
• Description – add a description if needed.
• Expiration – select the token’s lifetime.
• Repository access – choose which repositories the token can access. Tokens always include read-only access to all public repositories on GitHub.
• Permissions – choose the permissions to grant. Depending on the resource owner and the repository access you specify, there are permissions at the repository, organization, and account levels.
• Click Generate token.

Next, install a GitHub library for Python – PyGithub is a popular choice that significantly simplifies API work. Install it via pip:

pip install PyGithub

Below is a Python GitHub API example to access an account via PyGithub:

from github import Github

# Authenticate using a Personal Access Token (PAT)
g = Github("YOUR_PERSONAL_ACCESS_TOKEN")

# Get user information
user = g.get_user()
print(f"My login: {user.login}")
print(f"Public repos: {user.public_repos}")

# Get a repository
repo = g.get_repo("octocat/Hello-World")
print(f"Name: {repo.name}")
print(f"Stars: {repo.stargazers_count}")
print(f"Forks: {repo.forks_count}")

# Iterate through issues in the repository
for issue in repo.get_issues(state="open"):
    print(f"Issue: {issue.title}")

Common Pitfalls When Working with the GitHub API – and How to Fix Them

Even experienced developers run into issues integrating the GitHub API with Python. One of the most frequent is an authentication error — typically caused by an expired token or insufficient permissions. In that case, review access settings and generate a new token if needed.

Another common problem is exceeding rate limits, which can lead to request denials from GitHub. To scale effectively within platform limitations, it’s advisable to buy proxy servers — reliable providers help maintain stable operation when you’re sending high volumes of requests.

It’s also important to form request URLs correctly and handle server responses properly, especially when the API returns 404 or 403. Implementing logging and retries helps you detect and resolve problems quickly.

Best Practices on How to Use GitHub with Python

When working with the Python GitHub API, it’s important to follow several key practical recommendations. First and foremost – never store access tokens directly in your code. A safer approach is to use environment variables or separate configuration files, which are then excluded from the repository (for example, by adding them to .gitignore). If you’re committing your code to GitHub, make sure that files containing keys or other confidential information are hidden.

Below are examples.

Environment Variables

1. Install a library for working with .env files:

   pip install python-dotenv

2. Create a .env file where keys and tokens will be stored:

   GITHUB_TOKEN=your_personal_access_token

3. Use the token in your code:

   import os
   from dotenv import load_dotenv
   from github import Github
   
   # Load variables from .env
   load_dotenv()
   
   token = os.getenv("GITHUB_TOKEN")
   g = Github(token)
   
   user = g.get_user()
   print(user.login)

.gitignore

The .gitignore file tells Git which files or folders should not be tracked or uploaded to GitHub.

# Environment files
.env

# Caches and temporary files
__pycache__/
*.pyc

# IDE settings
.vscode/
.idea/

# Virtual environment
venv/

In this case:

• .env – a file containing sensitive data (for example, tokens or passwords).
• pycache/ – Python cache.
• venv/ – a folder containing the virtual environment.

CAPTCHA Handling

When automating actions in web interfaces, you may encounter additional protection systems such as ReCaptcha. To ensure uninterrupted script execution and prevent failures, it’s recommended to use methods for bypassing CAPTCHA that help overcome these challenges.

Request Rate Management

Another important consideration is minimizing the number of requests. The GitHub API imposes limits on how many calls you can make, so it’s best to cache frequently used data.

This approach is especially relevant when processing multiple repositories or analyzing user activity.

To use caching, install the following library:

pip install diskcache

Here’s an example:

import diskcache
from github import Github

cache = diskcache.Cache("./cache")  # folder for cache
g = Github("YOUR_ACCESS_TOKEN")

def get_user_repos(login):
    if login in cache:
        print("Fetched from cache")
        return cache[login]
    
    user = g.get_user(login)
    repos = [repo.name for repo in user.get_repos()]
    cache[login] = repos
    print("API request")
    return repos

print(get_user_repos("octocat"))

Conclusion

Integrating Python GitHub API is a powerful way to automate repository management, issue tracking, and other tasks. Understanding how to use it properly helps reduce manual work, streamline team processes, handle errors via status code checks, and make collaboration more flexible.

Following best practices, handling tokens carefully, and using the right libraries will help you avoid common pitfalls and get the most out of the GitHub API.