An Introduction to Python for Technical SEO

What Is Python?

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

With simple and easy to learn syntax, as well as advanced readability and support for a number of modules and libraries, Python is well-loved due to the increased productivity it provides.

As a testament to this, Python is used by some of the biggest organizations in the world to power their platforms, perform data analysis, and run their machine learning models.

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

In fact, Google’s first web-crawler was actually written in Python and it remains one of their official server-side languages.

How to Run Python

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

Most systems come with Python already installed, although it’s worth noting that this will more than likely be Python 2, which will be officially deprecated in 2020 now that Python 3 is deemed stable.

You can run Python from your terminal or command line IDE (Integrated Development Environment) or use cloud-based alternatives including:

  • Jupyter Notebooks.
  • Google Colab.

These provide an easier experience for beginners to learn and test elements of code line by line.

Python Libraries

The main power of Python is in its libraries, which enable a number of add-ons including:

  • Data extraction.
  • Analysis and preparation.
  • Scientific computing.
  • Natural language processing.
  • Machine learning.

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

  • TensorFlow: An open-source machine learning library.
  • NumPy: Useful for scientific computing.
  • SciPy: Used for scientific and technical computing.
  • SciKit Learn: Machine learning for data mining and analysis.
  • Pandas: Used for data manipulation and analysis.
  • SpaCy: A great natural language processing library.
  • Requests: A library for making HTTP requests.

How Python Can Help with Technical SEO

While having an understanding of the languages which power the websites we work on (such as HTML, CSS, and JavaScript) is important, Python provides many automation opportunities for low-level tasks which we would usually spend several hours undertaking.

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

The amount of data marketers work with is only increasing, so being able to efficiently analyze this will help to solve many complex problems in a shorter amount of time.

This in turn saves valuable time and allows us to be more efficient in undertaking other important SEO tasks.

These factors combined have led to a growth in the popularity of Python amongst SEO professionals.

The ability to better understand data will not only help us do our jobs better, but will also allow us to make data-driven decisions.

These decisions will then enable us to provide concrete insights for our clients and stakeholders and have more confidence in the recommendations we implement.

Automating with Python

While Python will not be able to imitate human, emotion-led strategy, Python scripts can be used to automate a large number of time-consuming tasks.

This list of tasks you can automate with Python is growing continuously:

  • Identifying user intent.
  • Mapping URLs ahead of a migration.
  • Internal link analysis.
  • Performing keyword research.
  • Optimizing images.
  • Scraping websites.
  • Internal linking analysis.
  • Log file analysis.
  • Hreflang validation.
  • Keyword growth calculation.
  • Collecting GSC data.
  • Performing competitor analysis.

Powering Machine Learning

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

It is also open-source, with several developer advocates providing support for users.

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

What Is Machine Learning?

Machine learning is essentially “an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience, without the need to be explicitly programmed”.

Machine learning is typically used to identify patterns in data, upon which predictions can then be made.

Python & Machine Learning

Run in conjunction with machine learning, Python can be used to power scripts for training a dataset, before it summarizes and visualizes the data.

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

Real-World Machine Learning Examples

The use of machine learning on the web is increasing all the time, with new models being created and training data becoming more accessible daily.

Some real-world machine learning examples include:

  • Google’s RankBrain algorithm.
  • Baidu’s Deep Voice program.
  • Twitter’s curated timelines.
  • Netflix and Spotify recommendations.
  • Salesforce’s Einstein feature.

SEO Possibilities with Machine Learning

Due to their ability to solve complex problems, it is no surprise that machine learning models are being used to help make marketers’ lives easier.

As Britney Muller says:

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

This will enable you to spend more time finding solutions, rather than just identifying problems. Some examples of machine learning models used in SEO include:

  • Content quality evaluation.
  • Identifying keyword gaps and opportunities.
  • Gaining insights into user engagement.
  • Optimizing title tags.
  • Automating meta description creation.
  • Transcribing audio.
  • Google’s NLP Model

    One such model worth checking out is Google’s Natural Language Processing API, which uses machine learning to reveal the structure and meaning of text. It analyzes text to understand the sentiment, as well as extract key information.

    Not only does this API allow you to train a model personalized to your content, providing results that are relevant to your specific needs, you will also gain an insight into Google’s understanding of your content.

    To finish, I wanted to share my three biggest tips for getting started and continuing to learn:

    Tip 1: Talk to Your Developers

    There’s a high chance the developers you work with will have an understanding of Python.

    Have a conversation with them, let them know what you’re working on and spark their interest too – there may even be something you can collaborate on!

    Tip 2: Join Communities

    One of the best things about learning Python is the support available, there are so many online communities with hundreds of supportive individuals willing to provide non-judgmental advice.

    Tip 3: Keep Practicing & Have Fun

    This is the most important piece of advice a developer friend gave to me. There is no pressure to become a Python master in weeks. Take your time to learn the language and start fun side projects to put what you are learning into practice.