Various types of web page loading efficiency measurements are provided by PageSpeed Insights. Though the browser-based version of this tool is excellent for comparatively small in-depth analyses, it isn’t designed for mass checks. However, the Google PageSpeed Insights API is useful in this situation.
We will provide you with a brief introduction to the PageSpeed Insights API in this article, as well as instructions on how to use Python to automate the Bulk Performance tests.
What Is PageSpeed Insights API?
The PageSpeed Insights API provides lab data from Lighthouse and authentic data from the Chrome User Experience Report. It analyses the provided URL using Lighthouse to create a performance score that quantifies the page’s effectiveness across many criteria.
Additionally, PSI offers data on “DOM Size,” “Deepest DOM Child Element,” “Total Task Count,” and “DOM Content Loaded” time. These are all indicators of how quickly a website loads. It shows how Google developers interpret the idea of page loading performance.
See Also: Google PageSpeed Scores | New Lighthouse 8.0 Update
Automation of PageSpeed Tests Ssing PythonÂ
Using Python for bulk page speed tests allows you to get an overview of the entire website and examine potential changes that could enhance the user experience, crawl effectiveness, conversion rate, and ranking. So let’s look at how you can use Python to execute PageSpeed Insights API performance tests in bulk.
To begin the automation, you will need the PageSpeed Insights API key. If this feature is unknown to you, you can learn more about the PageSpeed Insights API at https://developers.google.com.
After that, there is a requirement for various libraries that can retrieve the URL and create the data frame, such as Pandas and Advertools. Use the Pandas library, which makes it simple to extract the data we need in table style and export it to CSV, to do this.
In addition, you must list all of the URLs before parsing the metrics. You must simultaneously establish a “For Loop” to capture the API responses at each URL.
Next, in order to get the metrics, you must submit a request to the Page Speed Insights API. To start with, you can check the metrics that are available on the Internet, while a significant portion of them will be first for you.
Once you have all the metrics, creating a data frame and obtaining the results in XLSX format is simple.
Conclusion
The Google PageSpeed Insights API is a fantastic, user-friendly, and scalable way to automate the site speed testing procedure for any website. It allows you to simply include PageSpeed Insights information into your development and delivers customized ideas for how to optimize your site.
Additionally, testing a webpage with Lighthouse within a browser is very simple and convenient. But scaling the program is still manual and challenging. However, Python is able to help in this situation.
Therefore, using Python and the PageSpeed Insights API, you can quickly do the bulk performance tests. This will assist you in running several pages each day, which would otherwise be a very laborious operation.