Python is used to power platforms, perform data analysis, and run their machine learning models. Get started with Python for technical SEO.

Since I started talking about how Python is used in the SEO space two years ago, it has gained even more popularity and many people have started using and seeing the benefits of using it in their daily roles.

It is very interesting to see so many SEOs sharing their experiences, the interesting scriptures they have written and the impact they have had on their jobs.

It would not be fair to publish this without mentioning the impact that Hamlet Batista had on me and so many other people.

What is Python?

In short, Python is an object-oriented, open-source interactive programming language that is interpreted line by line.

With a simple and easy-to-learn syntax, as well as advanced readability and support for multiple modules and libraries, Python is well-loved for its increased productivity.

As proof of this, Python is used by some of the largest organizations in the world to power their platforms, perform data analytics, and run their machine learning models.

Companies such as Google, YouTube, Netflix, NASA, Spotify, and IBM have publicly stated that Python has been an important part of their growth due to its simplicity, speed, and scalability.

How to run Python?

You can run Python scripts in several ways, depending on what works best for you.

Most systems come with Python already installed, it will most likely be Python 3, but you can find out which version you have by typing python-version in your terminal.

If you have Python 2 installed, you can upgrade to Python version 3 by downloading Python 3 from the Python website, as Python 2 was officially deprecated in 2020 and there are some syntax differences between the two, so it’s best to make sure you’re using Python 3.

Python libraries

Python’s main power is in its libraries, which allow for several additional features, including:

• Data extraction.
• Analysis and training.
• Scientific calculation.
• Natural language processing

Some useful libraries for tasks involving data analysis and automation in SEO include:

• Pandas: Used for data manipulation and analysis.
• NumPy: useful for scientific calculation.
• SciPy: Used for scientific and technical calculation.
• SciKit Learn: Machine learning for data extraction and analysis.
• Pandas: Used for data manipulation and analysis.
• SpaCy: An excellent natural language processing library.
• Requests: A library for HTTP requests.

Why Python is Popular with technical seo?

While understanding the languages ​​that feed the websites we work on (such as HTML, CSS, and JavaScript) is important, Python offers many opportunities for low-level automation tasks that we typically spend a few hours on.

Python empowers SEO professionals in many ways, as it not only allows us to automate repetitive tasks, but also to extract and analyze large data sets.

The amount of data that marketers work with is growing, so the ability to analyze this effectively will help solve many complex problems in a shorter amount of time.

This, in turn, saves valuable time and allows us to be more efficient in performing other important SEO tasks. These combined factors have led to an increase in the popularity of Python among SEO professionals.

How to add Python to your SEO workflow

The best way to add Python to your workflow is to start thinking about what can be automated, especially the tedious, time-consuming tasks.

Alternatively, think of ways you can treat more effectively and draw conclusions from the data you have available.

A great way to get started is to play with the data on your website that you already have access to, such as crawling your site or your analytics tool.

How to add Python to your technical SEO workflow

The best way to add Python to your workflow is to start thinking about what can be automated, especially the tedious, time-consuming tasks.

Alternatively, think of ways you can treat more effectively and draw conclusions from the data you have available.

A great way to get started is to play with the data on your website that you already have access to, such as crawling your site or your analytics tool.

Powering machine learning

Python is also a popular language used to power machine learning applications because of its simple, intuitive, and accessible syntax.

In addition, there are a large number of useful libraries that are useful when working and training machine learning models.

What is Machine Learning?

Machine learning is essential “an artificial intelligence application that gives systems the ability to automatically learn and improve from experience, without the need to be explicitly programmed” (a full definition can be found here).

Machine learning is often used to identify patterns in data, after which predictions can be made.

There are two main types of machine learning, the first being supervised learning, which is trained on labeled data, in which a training set has input with the desired result.

Python and Machine Learning

Running with machine learning, Python can be used to feed scripts to drive a data set before summarizing and viewing the data.

From here, the model will evaluate algorithms to allow predictions to be made.

Examples of real-world machine learning

The use of machine learning on the web is growing all the time, new models are being created and training data is becoming more accessible every day. In some cases, we are also used to helping them train.

Some examples of real-world machine learning include:

• Google’s RankBrain algorithm.
• Baidu’s Deep Voice program.
• Twitter timeline.
• Netflix and Spotify recommendations.
• Salesforce’s Einstein function.

SEO opportunities with machine learning

 Due to their ability to solve complex problems, it is not surprising that machine learning models are used to make life easier for marketers.

As Britney Muller puts it:

“Machine learning is becoming more accessible and will free us to work on a higher-level strategy.”

This will allow you to spend more time finding solutions, rather than identifying issues.

Some examples of machine learning models used in technical SEO include:

• Content quality assessment.
• Identify keyword gaps and opportunities.
• Obtaining information about user involvement.
• Title tag optimization.
• Automate meta description creation.
• Audio transcription.

Internal link

There are two different ways in which machine learning can help you connect internally.

The first is to update broken links, this can be done by crawling to identify broken internal links, then using an algorithm to suggest the most accurate page replacement and replacement of broken internal links.

Content quality

The next example is improving the quality of content by predicting what users and search engines would prefer. You can do this by building a model that generates perspectives on the factors that are most important.

These factors may include things like search volume and traffic, conversion rate, internal links, bounce rate, page time, and word count.

User experience

Machine learning is also used to help improve the user experience, and there are many examples of how it is used, for example, Instagram uses sentiment analysis to identify and address the language of aggression.

Twitter also uses it to crop images to make sure it cuts out images to show the most important part, for example, to focus on the text.

Conclusion

Hope this has inspired you to start learning Python and explore how it can help you automate tasks and analyze complex data to increase your efficiency.

As a final note, please note that you don’t have to learn Python to be a good SEO, but if you’re intrigued or interested, then I hope you have fun learning and implementing a few Python scripts in your workflow.

Python contributions from the technical seo industry

To continue to honor Hamlet’s passion for encouraging and celebrating others, I wanted to share some of the amazing things the SEO community has shared this year.

Moshe Ma-yafit wrote a great script on how to detect competitors’ price changes with Python and send email alerts. You can find an article explaining this along with a Github repository.

Lazarina Stoy has a script for generating meta descriptions, as well as a guide to using Pytrends with Python.

Francis Angelo Reyes wrote a script for a simple Python redirection mapping tool. He goes through each URL and finds the match. The application is also in the article, so you can try it there!

Yaniss Illoul worked at Broken Links Finder in Python. As well as a tool to capture keyword rankings across multiple domains.

Danielle Rohe distributed a script to download all sitemaps from a sitemap index, as well as to browse each and extract all URLs in a CSV file.

Muhammad Hammad created a very cool script for NLP and SERP content analysis

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