How To Use Python For Search Engine Optimization - Semalt Expert



Using Python for SEO can be a great way to give your website the features it needs while still optimizing it for search engines. Are you interested in exploring the possibilities of Python on your website? Here are some beginner-friendly ways to understand how Python functions and how it can be used for automation technical SEO and data analysis work. 

When we first started using Python, we found our experts using it more and more often, and with every new use came a new experience and a better understanding of the programming language. This has helped us level up our portfolio and we have become better as SEO professionals. 

Our ability to handle our client's Python needs ranges from fairly technical tasks such as evaluating how elements such as word counts and status codes have undergone changes over time. We can also take care of more advanced tasks, such as analyzing internal linking and log files. 

Also, we have been able to use Python for:
  • Working on very large bits of data sets. 
  • Working with files that would usually crash Excel or files requires complex analysis to extract any meaningful insights. 

How have we been able to use Python to improve our SEO performance?

When we use Python for SEO, we are empowered in several ways. That is thanks to its feature that allows users to automate repetitive, low-level functions that will normally take a long period to complete. 

By using this Python, we have more time and energy to spend on other important strategic work and optimize other efforts that are impossible to automate. 

It allows us to work better with large chunks of data, making it easier to arrive at better data-driven decisions, which provide valuable returns on our worlds and our clients go home pleased with our effort. 

To back up how effective Python can be, a study was carried out by McKinsey Global Institue, and it found that data driven organizations were 23 times more likely to acquire customers. They are likely to retain customers who click on their website six times more than regular sites. You get to benefit from all these by using Python. 

Using Python is also helpful for backing up any ideas or strategies we may have to improve your website. That is possible because we quantify it with the data we already have and use that to make the best decisions. We also maintain our power leverage when we try to implement these ideas. 

How do we add Python to our SEO workflow?

We use Python in our workflow by two primary methods: 
  1. We consider what can be automated and pay special attention to this factor when performing difficult tasks. 
  2. We identify any gaps in our analysis work as it's underway or on a completed analysis. 
We discovered that another user was to learn Python is to depend on the data you currently have to access or extract valuable insights. This method has helped several of our experts learn many things we will be discussing in this article. 

You should understand that we learned Python as an added advantage, not because it is necessary for becoming an SEO pro. 

How can I learn Python?

If you hope to get the best results from using this article as a guide to learning Python, here are some materials you should have at hand:
  • Some data from a website. 
  • An integrated Development Environment to run your code on. When we first started, we used Google Colab and Juster Notebook. 
  • An open mind. We believe our mindset helped a long way in making us this good with Python. We weren't afraid to make mistakes or write the wrong code. Every mistake is an opportunity to learn in a way that you can never forget. With a mistake, you get to work your way to the issue and figure out ways to fix it. This plays a big part in what we do as SEO professionals. 

Visit libraries

When we started learning Python, we were common visitors to libraries both online and locally. The library is a good starting point. There are several libraries you can check out, but three libraries stand out when it comes to teaching you the important stuff. They are: 

Pandas  

This is a Python library that is used for working on table data. This allows for high-level data manipulations where DataFrame is the key data structure. 

DataFrame is essentially a spreadsheet on Panda. However, its functions aren't limited to excels rows and byte limits. It is also much quicker and more efficient when compared to Microsoft Excel. 

Requests 

A request is used to make HTTP requests in Python. It makes use of different methods such as GET and POST when making a request, and eventually, the result gets stored in Python. Users can also use different requests like headers, which will display useful information concerning the content time and the time duration for its cache to respond. 

Beautiful soup

It is also a library used to extract data from HTML and XML files. We mostly use this for web scrapping because it can transform ordinary HTML documents into different Python objects. It has been used severally to extract the title of pages as an example. It can also be used to extract href links that are on the page. 

Segmenting pages 

Here, you will be grouping pages into categories based on their URL structure or the page title. You start by using a simple regex to break the site up and categorize it based on the URL of each page. Next, we add a function that loops through the URLs list, assigning a URL to a specific category before adding segments to a column in the DataFrame where you find the original URL list. 

There is also a way we can segment pages without manually creating the segments. By using the URL structure, we can grab the folder that is contained after the main document and use it to categorize each URL. This will still add a new column to our DataFrame with the engaged segment. 

Redirect relevancy 

If we didn't figure out that this was possible using Python, we might have never tried it. During migration, after adding redirects, we were looking to see if the redirect mapping was accurate. Our test depended on reviewing if the category and the depth of each page had changed or if it remained the same. 

As we did this, we had to take a pre and post-migration crawl of the site and segment each page using its URL structure, as we mentioned earlier. Following this, all that was left was to use some simple comparison operators who have been built into Python that help determine if the category of depth for each Python experience any changes. 

As an automated script, it ran through every URL to determine if the category or depth had any impact, and the output result as a new data frame. This new data frame will include additional columns that display true when they match or false if they fail to match. Just like excel, using the Panda library allows you to pivot data based on an index derived from the original DataFrame. 

Internal link analysis

It is important to run internal link analysis to identify which sections of a site have the most links as well as to discover new opportunities to develop more internal links across a site. To be able to perform this analysis, some of the columns of data from the web crawl will be needed. For example, you may require any metrics displaying link ins and link outs between pages on the site. 

Like before, we will need to segment this data so that we can determine the different categories of the website. It is also very important as it aided us when analyzing the links between these pages. 

Pivot tables are useful during this analysis because they allow us to pivot on the category in order to get the exact number of internal links on each page. 

With Python, we are also able to perform mathematical functions to derive sums and the meaning of any numerical data we have. 

Log file analysis

Another reason why Python is beneficial has to do with its log file analysis. Some of the insights we can extract include identifying areas of a site that get crawled the most by a Google search bot. It is also used to monitor any changes in the number of requests over time. 

Log file analysis can be used to see the number of pages that can't be indexed or broken pages that are still receiving bot attention in order to address crawl budget issues. 

The easiest way to perform a log file analysis is to segment the URLs of a site based on its umbrella category. We also use pivot tables to generate a figure of the total amount of URLs and the average amount for each segment. 

Conclusion 

Python has a lot to offer, and in the right hands, it is a powerful ally. Semalt and its team of experts have relied on Python for special needs for years. We know how to get the job done, and our clients have this as an advantage. You, too, can become a client today.  

  

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