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Using Python scripts to analyse SEO and broken links on your site

Python is all about automating repetitive tasks, leaving more time for your other Search Engine Optimization (SEO) efforts. Not many SEOs use Python for their problem-solving, even though it could save you a lot of time and effort. Python, for example, can be used for the following tasks:

  • Data extraction
  • Preparation
  • Analysis & visualization
  • Machine learning
  • Deep learning

We’ll be focussing mostly on data extraction and analysis in this article. The required modules will be indicated for each script.

Python SEO analyzer

A really useful script for analyzing your website is called ‘SEO analyzer’. It’s an all round website crawler that analyses the following information:

  • Word count
  • Page Title
  • Meta Description
  • Keywords on-page
  • Warnings
  • Missing title
  • Missing description
  • Missing image alt-text

This is great for a quick analysis of your basic SEO problems. As page title, meta descriptions and on-page keywords are important ranking factors, this script is perfect for gaining a clear picture of any problems that might be in play.

Using the SEO analyzer

After having installed the necessary modules (BeautifulSoup 4 + urllib2) for this script and having updated your Python to version 3.4+, you are technically ready to use this script. Json or working variants, however, can be useful for exporting the data you gain from the SEO analyser. After having installed the script, these are the commands you can use:


seoanalyze --sitemap

As seen in the examples above, for both internetvergelijk and telefoonvergelijk , it’s possible to either crawl the website, or the XML sitemap of a website in order to do an SEO analysis. Another option is to generate HTML output from the analysis instead of using json. This can be done through the following command:

seoanalyze --output-format-html

If you have installed json and want to export the data, use the following command:

from seoanalyzer import analyse output = analyse(site, sitemap) print(output)

You can also choose for an alternative path, running the analysis as a script, as seen in the example below:

This will export the file into a html after having run the –output-format html script. This seoanalyze script is great for optimizing your page titles, meta descriptions, images and on-page keywords. It’s also a lot faster than Screaming Frog, so if you’re only looking for this information, running the seoanalyze script is more efficient.

Link status analyser

Another way to use Python for Search Engine Optimization is by using a script that crawls your website and analyses your URL status codes. This script is called Pylinkvalidator and can be found here). All it requires is BeautifulSoup if you’re running it with Python 3.x. If you’re running a 2.x version like 2.6 or 2.7, you should not need BeautifulSoup.

In order to speed up the crawling, however, it might be useful to install the following libraries:

1) lxml – Speeds up the crawling of HTML pages (requires C libraries) 1) gevent – enables pylinkvalidator to use green threads 1) cchardet – Speeds up document encoding detection

Do keep this in mind, they could be very useful for crawling larger websites, and just to enhance the link status analyser.

What this script essentially does, is crawl the entire URL structure of a website in order to analyse the status codes of each and every URL. This makes it a very long process for bigger websites, hence the recommendation of using the optional libraries to speed this up.

Using the link status analyser

Pylinkvalidator has a ton of different usage options for usage. For example, you can:

  • Show progress
  • Crawl the website and pages belonging to another host
  • Only crawl a single page and the pages it links to
  • Only crawl links, ignore others (images, stylesheets, etc.)
  • Crawl a website with more threads or processes than default
  • Change your user agent
  • Crawl multiple websites
  • Check robots.txt
  • Crawling body tags and paragraph tags

Showing progress through -P or --progress is recommended, as without it, you will find yourself wondering when your crawl will be done without any visual signs. The command for crawling more threads (-- workers='number of workers') and processes (-- mode=process --workers='number of workers') can be very useful as well.

Of course, the script has many more options to explore. The examples below show some of the possible uses: -p

The function above crawls the website and shows progress. -p workers=4

This function crawls a website with multiple threads and shows progress. -p --parser=lxml

This function uses the lxml library in order to speed up the crawl while showing progress. -P --types=a

The function above only crawls links (<a href>) on your website, ignoring images, scripts, stylesheets and any other non-link attribute on your website. This is also a useful function when crawling the URLs of large websites. After the script has run its course, you’ll get a list of URLs with status codes 4xx and 5xx that have been found by crawling your website. Along with that, you’ll gain a list of URLs that link to that page, so you’ll have an easier time fixing the broken links. The regular crawl does not show any 3xx status codes. For more detailed information about what URLs your pages can be reached from, try the following function: --report-type=all

This give information about the status code of a page, and all the other pages that link to a page.

An incredibly useful SEO tool you can use to crawl your website for broken links (404) and server errors. Both of these errors can be bad for your SEO efforts, so be sure to regularly crawl your own website in order to fix these errors ASAP.


While these scripts are incredibly useful, there are a lot of various uses for Python in the world of SEO. Challenge yourself to create scripts that make your SEO efforts more efficient. There are plenty of Python scripts to make your life easier. There’s scripts for checking your hreflang tags, canonicals, robots.txt and much more. Because who, in today’s day and age, still does stuff manually when it can be automated?