'Beautiful is Better than Ugly': Using Python to Automate Your SEO Strategy by@ruchirkakkad

'Beautiful is Better than Ugly': Using Python to Automate Your SEO Strategy

Python is a programming language created by mathematician Guido Van Rossum and officially unveiled in 1991. It's an open-source technology with a massive community of developers. Python's versatility is the consequence of two main aspects: that it is multiplatform and that it works well in several ecosystems. The learning curve is incredibly lower than in other cases, precisely because of this philosophy that aims to cooperate with programmers to improve their experience. Python is also used effectively to solve several problems for businesses in online systems development or SEO strategies.
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Ruchir Kakkad

Ruchir Kakakd is a Co-founder of WebOccult Technologies.

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Python is already one of the essential languages ​​in the programming world. However, lately, we have seen that technology is invading other areas as well.

One of the main applications is artificial intelligence which means that companies are already learning to adopt the language to make better decisions and achieve results aligned with business goals.

This language is also used effectively to solve several problems for businesses in the web scenario, whether in online systems development or SEO strategies.

It allows us to improve marketing results and strengthen the strategy of attracting and converting customers.

In this article, we will cover:

  1. What is Python?
  2. What is the potential of Python today?
  3. What are the possibilities of Python and SEO?
  4. How does Python work for SEO in practice?

Stay with us and learn more!

What is Python?

Python is a programming language created by mathematician Guido Van Rossum and officially unveiled in 1991.

It's an open-source technology with a massive community of developers. Therefore, it can be multi-paradigmatic, that is to say, functional, structured, or object-oriented. That is why it is considered a universal language.

Its versatility is the consequence of two main aspects: the first is that it is multiplatform and that it works well in several ecosystems.

The second is that this programming model involves a vast library with different functions, classifications, and standards that could be used for various purposes. We will see, later, what exactly the linguistic applications are.

However, it is not only a code writing technology but also a development philosophywhich explains its success.

This idea focuses on certain principles, such as the preference for simplicity, the emphasis on the code's readability, clarity, and practicality.

These concepts were compiled in the Zen of Python, a set of ideas and best practices used in programming.

This list was created by Tim Peters, a significant contributor to the language, and has become a standard for language documentation, available even on the official website.


In keeping with these concepts, the technology emphasizes indexing rejects semicolons (a mandatory rule in most other languages) and uses patterns similar to other well-known languages, such as Java and C.

The learning curve is incredibly lower than in other cases, precisely because of this philosophy that aims to cooperate with programmers to improve their experience.

You can use an IDE, a terminal, or even cloud-based software options like Jupyter Notebook to get started in Python.

What is the potential of Python today?

Considering the systems used for a specific purpose, Python stands out as one of the more exciting options.

If a professional from another field enters the world of programming using this language, they will find it less complicated than other standards.

Machine learning and deep learning

In this sense, one of the great opportunities is the use of Python for artificial intelligence solutions and its subdomains: Machine Learning and Deep Learning.

In this topic, the use of technology for big data processing and other purposes related to main data-driven decision-making is also worth mentioning.

It is made possible by its user-friendly nature and support for processing large databases. In addition, there are countless libraries and standards in the language dedicated to numerical computation, data structure, and statistical analysis.

So you have a wide range of possibilities with technology: regression, machine learning algorithms, decision trees, among other techniques.

All of them allow you to navigate big data and extract useful information by crossing that data and identifying patterns.

In practice, these strategies can become natural language processing, for example. The systems analyze the texts and break down the information to try to get the meaning.

Thus, the system can identify the exact sentiment exhibited in a text, just as a human being would locate it.


The main goal here is automation. The power of Python allows us to delegate some fundamental functions for machines to solve and learn.

With the evolution of these algorithms, it is possible to obtain better results with these functions.

This way, humans can focus on other tasks, like more strategic matters, which require a little more creativity.

Libraries and data visualization

The language can also be applied to data mining, scientific computation, information analysis, and preparation.

For machine learning issues, some helpful tools are TensorFlow, the Scikit-Learn library, SpaCy, and Panda. For scientific computing, we can cite SciPy and NumPy.

Moreover, it is an excellent tool to transform your data, making it easier to visualize.

It is common to use this technology to create reports, charts, and other graphs to understand information and extract value. This approach can be used to optimize campaigns and sales presentations, for example.

Web programming

However, we also have the Python app for web programming. So frameworks, like Django, have become an exciting way to apply language principles to this kind of development, both on the front-end and the back-end.

With simplicity and versatility, it is possible to improve results, facilitate testing, manage interactivity and manage more data.

On the web, Django helps format and organize information, authenticate data, and validate the information in forms, for example.

Integration with other languages

Another advantage is the integration with other more common languages, such as Java. It makes Python powerful and valuable in different contexts, which further contributes to its versatility.

While we talk about using Python for task automation, we get to the basics of using this language for SEO activities.

This is what we will explore in the next topic.

What are the possibilities of Python and SEO?

First, we can summarize the relationship between Python and SEO by citing two goals: analyze large amounts of data without risk of errors and automate tasks that leave professionals free to work on other topics like us—already mentioned.

We will see below the main applications of this language for the SEO world.

URL mapping

When a site migrates to a different address, it is common for the links from the old address to break and lead the user to an error page.

However, famous technology can map URLs in these situations, correlating some old links with new addresses.

Even if removing these references from Google search pages is possible, it is still essential to perform this mapping to avoid any inconvenience that could generate business losses.

With Python, site administrators can do this conveniently and efficiently, managing large pages without significant issues.

Internal link analysis

If the goal is to gather information about internal links, technology can also help.

It is possible to analyze the number of links, check if there are any broken links, what are the links to images without alt tags, etc. It is also possible to analyze whether they are internal, external, or point to certain subdomains.

In other words, it is possible to obtain a general map of the site's link-building strategy, which makes it possible to trace improvement actions.

On a large website, this is almost impossible to do manually. Therefore, the tool helps a lot and offers fast and dynamic support.

Keyword search

Digital marketing is very much about keywords. So why not use programming in this function as well?

You can research keywords relevant to your buyer's personality and the most valuable ones in the market to create specific content to rank on Google.

In addition, the language allows you to categorize the thousands of existing keywords by intention, improve the strategy and provide a complete experience.

This categorization, carried out with Deep Learning, allows us to understand the purpose of each keyword and how the site can better match it.

As we know, there are four types of research: informational, transactional, navigational, and commercial.

Understanding how the keywords line up within these groups help companies better understand customer preferences.

It's also interesting to analyze the current SERPs and which companies are already achieving higher rankings for each group.

Image optimization

In digital strategies, performance is an important indicator.

When working with websites and digital marketing, stipulating metrics is essential to track your website's load speed and understand how that affects user experience.

To do this, administrators can use Python to optimize images and reduce their size without compromising on quality.

Keyword tracking

If the goal is to track keyword performance, technology can also help.

It is possible to monitor their behavior and obtain data to improve the content and improve positioning on search pages.

Performance improvement

Generally speaking, the responsible team can also optimize the overall performance of the site using Python tools.

It is possible to analyze each page and identify the factors that affect the loading speed and interfere with the experience with them.

It is possible to monitor each page and get practical information for improvements.

Data extraction

Another factor that can be automated is the practice of web scraping. This is the mining and extraction of data from sites for various purposes, such as user understanding, gaining competitive advantage, etc.

Libraries and frameworks promote working with a large amount of data, following the principles already mentioned.

How does Python work for SEO in practice?

In this topic, we will introduce some tools and algorithms used to solve SEO problems in Python.

Add subtitles to images.

For that, we have to use Pythia, a Deep Learning framework. The main goal is to generate captions and tags for images that lack this information to optimize SEO for visual marketing and improve website accessibility.

You can access this feature directly from Google Colab, a tool that facilitates development.

With a copy of the code needed for the Google app profile, you can run the system and get an accessible output to insert an image link with a button to click. When you use the button, the algorithm generates the legend.


So how do you get all the images in a specific URL to be captioned? Well, we need to use the following function to parse the URL and generate output as subtitles:


This code will extract all images from a URL, one step before:


While the following code completes adding subtitles to each of them:


Image optimization

This script for optimizing images was created by Victor Domingos and is available on GitHub.

The goal is to decrease file size to increase performance. The name of the application is "optimize-images." To install it, add the following line:


To optimize a single file, the user must add the following command:


To optimize all images in a single folder, the user should add the following command:


SEO analysis

There is another script, which can be found on Seth Black's GitHub page, with the primary purpose of performing a complete SEO analysis of a website.

It counts the words of the pages and identifies errors, such as lack of meta descriptions, titles, and alt tags.

Here's an image that illustrates the main class logic of the project a bit:


Here too, the main class imports:


In turn, we have here the methods and loops of the class responsible for analyzing the site:


To install, use the command:


To use it, you must type:


The video below shows the code to analyze a URL for the keyword, a good practice for on-page SEO. It checks if the headlines and H1's contain the keyword and parses the size of the URL itself.

Check for 404 errors.

This script searches for pages containing a 404 error, that is, broken links.

It is beneficial for optimizing the user experience and preventing it from running into dead ends. The algorithm uses the Beautifulsoup library.

This part of the code consists of collecting the links in the HTML of the site:


This part consists of sending a request to the links and checking the response code. That is, it will identify when there is a 404 error.


As we have seen, there is great potential in using Python for SEO. Language is one of the most important technologies available precisely because of its speed, versatility, clarity, and ease of learning.

In addition, it can be used for machine learning and deep learning applications and to automate SEO analysis and find broken links, among others.

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