SEO Python as a tool for SEO

27 March 2024

Reading time: 5 minutes

Senior SEO-specialist

In the rapidly changing and highly competitive world of SEO, data analysis is of great importance. Python programming language can help automate SEO processes and analyze complex data sets in order to get higher in Google with a website.

In this article, I explain the basics about SEO Python, including looking at tools, scripts and libraries that make SEO strategy easier and more efficient.

Python SEO collects data and conducts technical audits, among other things. It is therefore a valuable tool for improving SEO position and search engine results.

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When to use Python SEO

Python SEO is mostly used by technical SEO specialists working on large websites with lots of data. Read more about automating SEO here.

Pyhton offers a wide range of libraries and tools that help with SEO strategy. For parsing HTML and XML, Beautiful Soup is useful. Parsing takes care of extracting data and web pages. For HTTP requests, Request library is appropriate. HTTP requests are the basis of any scraping activity on the Web.

Use Pandas for data manipulation and analysis. This tool can help filter data. For making numerical calculations, you can use NumPy, while Scikit-learn offers numerous possibilities in terms of machine learning. SEO specialists can use this information to discover patterns and gain insight into user behavior and website performance. Based on this, they can make predictions and the SEO is optimized.

SEO specialists use the libraries/tools listed below:

Beautiful Soup: to parse HTLM and XML documents;
Request: to perform HTTP requests to web pages;
Pandas: for the manipulation of analysis and data;
NumPy: to make scientific calculations;
Scikit-learn: for data mining and machine learning;
Scrapy: to extract data from websites. This is an open-source and collaborative framework;
LXML: for easy and fast processing of HTML and XML;
Matplotlib and Seaborn: for data visualization
Selenium: to automate Web browsers and perform browser-based tasks

Web scraping and data extraction

Python helps with web scraping and data extraction. These are important components for SEO optimization. An example of a suitable tool for this is Scrapy. This open-source framework allows users to quickly and efficiently extract data from Web sites.

This allows you to monitor SERP positions, collect keyword data and analyze competitors. Python also allows you to merge extracted data with other data sources, giving you a better overview.

Data analysis of SEO

The next step in the SEO roadmap is to analyze the collected data with Python. Cleaning, transforming and analyzing datasets is done using the Pandas library. These actions provide great insight into the SEO process.

Libraries such as Matplotlib and Seaborn visualize data in clear and understandable graphs and charts. SEO specialists use this data to measure performance and devise new strategies. Positive results are also shared with stakeholders. In addition, NumPy provides the numerical analyses.

What to use Python for?

I use Python to speed up certain parts in the SEO package, including the following:

Task	Explanation
Data extraction	Collecting data such as keywords, backlinks and metadata from competitors or from one’s own website for analysis.
Automation of reporting	Creating scripts that automatically collect data and produce reports on SEO performance.
Technical SEO audits	Using Python scripts crawl large websites and check for technical problems including non-working links, redirect loops and page speed.
Content analysis	Analyzing website content to identify duplication, identify content gaps and check readability.
Keyword research	Processing and analyzing large amounts of keyword data to gain insights into search volume, competition and content relevance.
Link Building	Identifying and classifying link prospects by scrapping the web and analyzing backlink profiles.
SERP monitoring	Daily tracking of keyword positions in the SERP to identify changes and trends.
On-page optimization	Creating an automated analysis of on-page SEO elements such as titles, descriptions and keyword usage to optimize the page.

What do I use Python for within SEO?

How do you get started with Python?

To apply Python efficiently, it is a good idea to become familiar with Python as soon as possible. You do this by taking a beginner’s course in Python to get familiar with the syntax and basic concepts the tool offers – think variables, functions and loops.
Then study specific libraries in Python that help optimize SEO. Some examples include BeautifulSoup, Request, Pandas and Scrapy. Then put all the knowledge gained into practice by already performing small exercises. Simple projects might include scrapping data from a small, simple website or analyzing a small data set.
Next, develop an SEO-related project. For example, create a keyword tracking tool or conduct a technical SEO audit. Previous knowledge of Python may come in handy.
There are several online SEO communities where professionals impart their knowledge about Python and provide novice Python users with feedback. Take advantage of this community and learn even more about using Python.
Once the basics are mastered, the Python script can be integrated into the overall SEO plan. Automate SEO processes to increase SEO strategy efficiency. In addition, always keep an eye on all new trends and updates, both of Python and SEO in general. These worlds are changing at lightning speed, and standing still actually means going backwards.

Summary

Python is a useful tool in improving SEO. Despite the many alternatives, I find the tool very nice to use. However, this tool is complex and to get results from it, sufficient knowledge and experience is important. A good technical foundation is especially important.