Exporting data from GitHub pull requests to a CSV file is a common requirement for businesses and data professionals. Whether you're looking to analyze your data in spreadsheet software, feed it into another system, or simply create a backup, CSV files offer a versatile and widely-compatible solution.

This guide explores two methods to accomplish this task: a manual approach and an automated solution using Airbyte. We'll compare these methods to help you choose the one that best fits your needs and workflow.

By the end of this article, you'll understand:

• The basics of exporting data from GitHub pull requests to CSV
• Step-by-step instructions for manual export
• How to set up automated, scheduled exports using Airbyte
• The benefits and use cases of GitHub pull requests to CSV integration

Let's dive into the details.

About GitHub

GitHub is a web-based platform for version control and collaboration using Git. It allows developers to store, manage, track and control changes to their code repositories.

About CSV File

CSV (Comma-Separated Values) files are a simple, universal format for storing tabular data. Their simplicity and widespread support make them an excellent choice for data exchange between different systems and applications. CSV files can be easily opened and manipulated in various tools, including spreadsheet software like Microsoft Excel and Google Sheets, as well as programming languages and data analysis tools.

How to export GitHub pull requests data to CSV?

Let's explore two methods to export your GitHub pull requests data to CSV: a manual approach and an automated solution using Airbyte.

Method 1: Automate or Schedule the export of GitHub pull requests data to CSV using Airbyte

Airbyte provides a robust, scalable solution for exporting GitHub pull requests data to CSV format. This method not only automates the process but also allows for scheduled, consistent updates. Here's how to set it up:

1. Configure GitHub as an Airbyte source

• Log in to your Airbyte account.
• Go to the 'Sources' tab and click 'New Source'.
• Select 'GitHub' from the list of available integrations.
• Enter your GitHub credentials to configure the connection.
• Test the connection to ensure proper setup.

2. Set up CSV as your destination

• Go to the 'Destinations' section in Airbyte.
• Choose 'Local CSV' as your destination.
• For local CSV, specify the directory path where files will be saved.

3. Create a connection

• In the 'Connections' tab, click 'New Connection'.
• Link your GitHub source to your CSV destination.
• In the 'Streams' section, choose which data you want to export from GitHub.
• Configure your sync settings:some text
  • Choose between full refresh or incremental sync modes.
  • Set your desired sync frequency (e.g., hourly, daily, weekly).
• Configure transformations or mappings if necessary.
• Save and run your connection to start the initial sync.

Once complete, verify the exported CSV files in your specified location.

By employing Airbyte for your GitHub pull requests to CSV exports, you're not just automating a task – you're implementing a scalable, maintainable data pipeline. With this setup, your GitHub pull requests data will be regularly exported to CSV format without manual intervention, allowing you to focus on data analysis and decision-making rather than repetitive export tasks.

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Method 2: Manually exporting GitHub pull requests data to CSV

Here's a step-by-step process to export GitHub pull requests to a CSV file without using any third-party data integration tools. This process will involve using the GitHub API and a script to fetch the data and create the CSV file.

Step 1: Set up GitHub API access

1. Go to your GitHub account settings

2. Navigate to "Developer settings" > "Personal access tokens"

3. Generate a new token with the "repo" scope

4. Copy and save the token securely

Step 2: Choose a programming language

For this example, we'll use Python, but you can use any language that can make HTTP requests and handle JSON data.

Step 3: Install required libraries

Install the requests library for making API calls:

```

pip install requests

```

Step 4: Write the script

Create a new Python file (e.g., `github_pr_export.py`) and add the following code:

```python

import requests

import csv

from datetime import datetime

# GitHub API configuration

github_api_url = "https://api.github.com"

owner = "your_github_username"

repo = "your_repository_name"

access_token = "your_personal_access_token"

# Set up headers for API requests

headers = {

    "Authorization": f"token {access_token}",

    "Accept": "application/vnd.github.v3+json"

}

# Function to fetch pull requests

def fetch_pull_requests(state="all"):

    url = f"{github_api_url}/repos/{owner}/{repo}/pulls"

    params = {"state": state, "per_page": 100}

    all_prs = []

    while url:

        response = requests.get(url, headers=headers, params=params)

        response.raise_for_status()

        prs = response.json()

        all_prs.extend(prs)

        url = response.links.get("next", {}).get("url")

    return all_prs

# Function to write pull requests to CSV

def write_to_csv(pull_requests, filename):

    with open(filename, mode='w', newline='', encoding='utf-8') as file:

        writer = csv.writer(file)

        writer.writerow(["Number", "Title", "State", "Created At", "Updated At", "Closed At", "Author", "URL"])

‍

        for pr in pull_requests:

            writer.writerow([

                pr['number'],

                pr['title'],

                pr['state'],

                pr['created_at'],

                pr['updated_at'],

                pr['closed_at'],

                pr['user']['login'],

                pr['html_url']

            ])

# Main execution

if __name__ == "__main__":

    pull_requests = fetch_pull_requests()

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

    filename = f"github_prs_{timestamp}.csv"

    write_to_csv(pull_requests, filename)

    print(f"Exported {len(pull_requests)} pull requests to {filename}")

```

Step 5: Configure the script

Replace the following variables in the script with your own values:

- `owner`: Your GitHub username

- `repo`: The name of the repository you want to export pull requests from

- `access_token`: Your personal access token generated in Step 1

Step 6: Run the script

Execute the script by running:

```

python github_pr_export.py

```

Step 7: Verify the output

The script will create a CSV file in the same directory with a name like `github_prs_20230515_120000.csv` (timestamp will vary). Open the file to verify that the pull request data has been exported correctly.

Additional considerations:

• This script fetches all pull requests (open, closed, and merged). You can modify the `fetch_pull_requests` function to filter by state if needed.
• The script uses pagination to fetch all pull requests, even if there are more than 100.
• You may want to add error handling and rate limit checking for more robust operation.
• You can customize the CSV output by adding or removing fields in the `write_to_csv` function.

By following these steps, you can export GitHub pull requests to a CSV file without using any third-party data integration tools. This method gives you full control over the data you're exporting

Use cases for exporting GitHub pull requests data to CSV

1. Performance Analysis and Reporting

• Managers or team leads can use the exported data to analyze team performance and generate reports.
• The CSV file can include metrics such as the number of PRs opened/closed, average time to merge, and contributors' activity.
• This data can be imported into spreadsheet software or data visualization tools to create charts and graphs for presentations or team meetings.
• It allows for tracking trends over time, identifying bottlenecks in the development process, and making data-driven decisions to improve workflow efficiency.

2. Code Review Process Improvement

• Exporting PR data can help in analyzing the code review process.
• The CSV file can contain information about reviewers, comments, review duration, and number of iterations per PR.
• This data can be used to identify patterns, such as which types of PRs take longer to review or which team members might need additional support.
• It can help in balancing workloads among reviewers and identifying areas where the review process can be streamlined or improved.
• The insights gained can be used to establish or refine code review guidelines and best practices.

3. Integration with External Tools and Systems

• Exporting PR data to CSV allows for easy integration with other tools and systems used by the organization.
• The data can be imported into project management tools to correlate PR activity with overall project progress and timelines.
• It can be used in continuous integration/continuous deployment (CI/CD) systems to track the relationship between PRs and build/deployment processes.
• The CSV format makes it easy to import the data into custom dashboards or reporting systems that aggregate information from multiple sources.
• This integration can provide a more comprehensive view of the development process, linking code changes to broader project goals and metrics.

Why choose Airbyte for connecting GitHub pull requests to CSV?

1. Unified data integration: Airbyte provides a single platform to manage all your data connections, eliminating the need for multiple tools or scripts.
2. Flexible scheduling: Set up exports to run at intervals that suit your business needs, from real-time syncs to daily or weekly updates.
3. Data integrity: Airbyte ensures consistent, reliable data transfers, reducing the risk of corruption or incomplete exports often associated with manual processes.
4. Scalability: As your data volume grows, Airbyte effortlessly scales to handle larger datasets without compromising performance.
5. Seamless integration with data tools: Airbyte's CSV outputs can be easily integrated with various data analysis tools and platforms, enhancing your overall data ecosystem.

Conclusion

Exporting data from GitHub pull requests to CSV is crucial for many businesses to leverage their data effectively. While manual export is possible, using a tool like Airbyte can significantly streamline this process, saving time and reducing errors. By automating your data exports with Airbyte, you can ensure that your CSV files from GitHub pull requests are always up-to-date, allowing you to focus on analyzing and deriving insights from your data rather than managing exports.

Ready to simplify your GitHub pull requests to CSV exports? Try Airbyte for free.