Another point of difference is that PSI doesn\u2019t supply any information related to web accessibility, SEO, or progressive web apps (PWAs), while Lighthouse provides all of the above.<\/p>\n
Thus, when we use PageSpeed Insights API for the bulk URL loading performance test, we won\u2019t have any data for accessibility.<\/p>\n
However, PSI provides more information related to the page speed performance, such as \u201cDOM Size,\u201d \u201cDeepest DOM Child Element,\u201d \u201cTotal Task Count,\u201d and \u201cDOM Content Loaded\u201d timing.<\/p>\n
One more advantage of the PageSpeed Insights API is that it gives the \u201cobserved metrics\u201d and \u201cactual metrics\u201d different names.<\/p>\n
In this guide, you will learn:<\/p>\n
- \n
- How to create a production-level Python Script.<\/li>\n
- How to use APIs with Python.<\/li>\n
- How to construct data frames from API responses.<\/li>\n
- How to analyze the API responses.<\/li>\n
- How to parse URLs and process URL requests\u2019 responses.<\/li>\n
- How to store the API responses with proper structure.<\/li>\n<\/ul>\n
An example output of the Page Speed Insights API call with Python is below.<\/p>\n
![\"example](\"https:\/\/marketingnewsbox.com\/wp-content\/uploads\/2022\/06\/bulk-loading-performance-tests-with-pagespeed-insights-api-python-via-sejournal-koraygubur.png\")
Screenshot from author, June 2022<\/em><\/span><\/div>\nLibraries For Using PageSpeed Insights API With Python<\/h2>\n
The necessary libraries to use PSI API with Python are below.<\/p>\n
- \n
- Advertools<\/a> retrieves testing URLs from the sitemap of a website.<\/li>\n
- Pandas<\/a> is to construct the data frame and flatten the JSON output of the API.<\/li>\n
- Requests are to make a request to the specific API endpoint.<\/li>\n
- JSON is to take the API response and put it into the specifically related dictionary point.<\/li>\n
- Datetime is to modify the specific output file\u2019s name with the date of the moment.<\/li>\n
- URLlib<\/a> is to parse the test subject website URL.<\/li>\n<\/ul>\n
How To Use PSI API With Python?<\/h2>\n
To use the PSI API with Python, follow the steps below.<\/p>\n
- \n
- Get a PageSpeed Insights API key.<\/li>\n
- Import the necessary libraries.<\/li>\n
- Parse the URL for the test subject website.<\/li>\n
- Take the Date of Moment for file name.<\/li>\n
- Take URLs into a list from a sitemap.<\/li>\n
- Choose the metrics that you want from PSI API.<\/li>\n
- Create a For Loop for taking the API Response for all URLs.<\/li>\n
- Construct the data frame with chosen PSI API metrics.<\/li>\n
- Output the results in the form of XLSX.<\/li>\n<\/ul>\n
1. Get PageSpeed Insights API Key<\/h3>\n
Use the PageSpeed Insights API<\/a> Documentation to get the API Key.<\/p>\n
Click the \u201cGet a Key\u201d button below.<\/p>\n
![\"psi](\"https:\/\/marketingnewsbox.com\/wp-content\/uploads\/2022\/06\/bulk-loading-performance-tests-with-pagespeed-insights-api-python-via-sejournal-koraygubur-1.png\")
Image from developers.google.com, June 2022<\/em><\/span><\/div>\nChoose a project that you have created in Google Developer Console.<\/p>\n
![\"google](\"https:\/\/marketingnewsbox.com\/wp-content\/uploads\/2022\/06\/bulk-loading-performance-tests-with-pagespeed-insights-api-python-via-sejournal-koraygubur-2.png\")
Image from developers.google.com, June 2022<\/em><\/span><\/div>\nEnable the PageSpeed Insights API on that specific project.<\/p>\n
![\"page](\"https:\/\/marketingnewsbox.com\/wp-content\/uploads\/2022\/06\/bulk-loading-performance-tests-with-pagespeed-insights-api-python-via-sejournal-koraygubur-3.png\")
Image from developers.google.com, June 2022<\/em><\/span><\/div>\nYou will need to use the specific API Key in your API Requests.<\/p>\n
2. Import The Necessary Libraries<\/h3>\n
Use the lines below to import the fundamental libraries.<\/p>\n
import advertools as adv import pandas as pd import requests import json from datetime import datetime from urllib.parse import urlparse<\/pre>\n
3. Parse The URL For The Test Subject Website<\/h3>\n
To parse the URL of the subject website, use the code structure below.<\/p>\n
domain = urlparse(sitemap_url) domain = domain.netloc.split(\".\")[1]<\/pre>\n
The \u201cdomain\u201d variable is the parsed version of the sitemap URL.<\/p>\n
The \u201cnetloc\u201d represents the specific URL\u2019s domain section. When we split it with the \u201c.\u201d it takes the \u201cmiddle section\u201d which represents the domain name.<\/p>\n
Here, \u201c0\u201d is for \u201cwww,\u201d \u201c1\u201d for \u201cdomain name,\u201d and \u201c2\u201d is for \u201cdomain extension,\u201d if we split it with \u201c.\u201d<\/p>\n
4. Take The Date Of Moment For File Name<\/h3>\n
To take the date of the specific function call moment, use the \u201cdatetime.now\u201d method.<\/p>\n
Datetime.now provides the specific time of the specific moment. Use the \u201cstrftime\u201d with the \u201c%Y\u201d, \u201c\u201d%m\u201d, and \u201c%d\u201d values. \u201c%Y\u201d is for the year. The \u201c%m\u201d and \u201c%d\u201d are numeric values for the specific month and the day.<\/p>\n
date = datetime.now().strftime(\"%Y_%m_%d\")<\/pre>\n
5. Take URLs Into A List From A Sitemap<\/h3>\n
To take the URLs into a list form from a sitemap file, use the code block below.<\/p>\n
sitemap = adv.sitemap_to_df(sitemap_url) sitemap_urls = sitemap[\"loc\"].to_list()<\/pre>\n
If you read the Python Sitemap Health Audit<\/a>, you can learn further information about the sitemaps.<\/p>\n
6. Choose The Metrics That You Want From PSI API<\/h3>\n
To choose the PSI API response JSON properties, you should see the JSON file itself.<\/p>\n
It is highly relevant to the reading, parsing, and flattening of JSON objects.<\/p>\n
It is even related to Semantic SEO<\/a>, thanks to the concept of \u201cdirected graph,\u201d and \u201cJSON-LD\u201d structured data.<\/p>\n
In this article, we won\u2019t focus on examining the specific PSI API Response\u2019s JSON hierarchies.<\/p>\n
You can see the metrics that I have chosen to gather from PSI API. It is richer than the basic default output of PSI API, which only gives the Core Web Vitals Metrics, or Speed Index-Interaction to Next Paint, Time to First Byte, and First Contentful Paint.<\/p>\n
Of course, it also gives \u201csuggestions\u201d by saying \u201cAvoid Chaining Critical Requests,\u201d but there is no need to put a sentence into a data frame.<\/p>\n
In the future, these suggestions, or even every individual chain event, their KB and MS values can be taken into a single column with the name \u201cpsi_suggestions.\u201d<\/p>\n
For a start, you can check the metrics that I have chosen, and an important amount of them will be first for you.<\/p>\n
PSI API Metrics, the first section is below.<\/p>\n
fid = [] lcp = [] cls_ = [] url = [] fcp = [] performance_score = [] total_tasks = [] total_tasks_time = [] long_tasks = [] dom_size = [] maximum_dom_depth = [] maximum_child_element = [] observed_fcp = [] observed_fid = [] observed_lcp = [] observed_cls = [] observed_fp = [] observed_fmp = [] observed_dom_content_loaded = [] observed_speed_index = [] observed_total_blocking_time = [] observed_first_visual_change = [] observed_last_visual_change = [] observed_tti = [] observed_max_potential_fid = []<\/pre>\n
- Pandas<\/a> is to construct the data frame and flatten the JSON output of the API.<\/li>\n
- Advertools<\/a> retrieves testing URLs from the sitemap of a website.<\/li>\n
![\"pagespeed](\"https:\/\/marketingnewsbox.com\/wp-content\/uploads\/2022\/06\/bulk-loading-performance-tests-with-pagespeed-insights-api-python-via-sejournal-koraygubur-4.png\")