Python is becoming popular with the SEO community due to its ability to automate repetitive tasks that can be time restrictive. This ensures that technical SEO campaigns are improved, time is saved, and there is no sacrifice with results. But, what exactly is python SEO?  

Python is an open-source programming language that was created in 1991. Your business can use it to uncover incredible SEO insights. Often compared to R, it’s even easier to use due to its simple and common sense syntax, and is the perfect option for those who are new to programming. 

Imagine this: You’re an SEO powerhouse, commanding the search engine landscape with unparalleled efficiency. Tasks like data analysis, keyword research, and content optimization no longer drain your time; they’re seamlessly automated by your secret weapon – Python.

Yes, Python, the versatile programming language, is not just for data scientists and coders anymore. It’s rapidly becoming the go-to tool for SEO professionals seeking to automate tedious tasks, extract valuable insights, and optimize their strategies for maximum impact.

Why Python for SEO?

image for what is python seo blog
  • Automation powerhouse: Python’s robust libraries and frameworks like Scrapy and BeautifulSoup enable you to automate repetitive tasks like data scraping, content analysis, and link checking. Imagine analyzing thousands of competitor backlinks in minutes, freeing up your time for strategic planning and creative thinking.
  • Data analysis wizard: Python excels in data analysis, allowing you to crunch vast amounts of SEO data – website traffic, keyword rankings, social media engagement – to uncover hidden trends and actionable insights. Identify your most profitable keywords, optimize content for specific user intents, and track your progress with laser precision.
  • Content optimization maestro: Python’s capabilities extend to content creation and optimization. Generate unique, SEO-friendly meta descriptions, automate image optimization, and personalize content based on user data – all with the power of Python scripts.
  • Customization king: Unlike rigid SEO tools, Python offers unparalleled flexibility. Craft custom scripts tailored to your specific needs and website structure, ensuring your SEO strategy is truly unique and effective.

Defining Python

As previously mentioned, Python is an open-source, object-oriented interactive programming language that is usually interpreted line by line. Python’s syntax is easy to learn and has advanced readability which means that it can be supported for a number of modules and libraries. 

Python provides opportunities for increased productivity as it can power platforms, perform data analysis, and run machine learning models. Due to its speed and its simplicity, Google, YouTube, Spotify, and even NASA have credited Python for being an important part of their growth as it allows for scalability with ease. 

Google still has Python as one of their official server-side languages and their very first web crawler was written in Python.

How to Use Python

Python scripts are easy to access and use. Most systems now already have Python installed making it much easier to run these scripts. Python 2 is what most systems currently host but it will be updated to Python 3 now that it has demonstrated to be stable. It can be run from either your command line Integrated Development Environment (IDE) or your terminal. 

However, cloud-based alternatives are available in the form of Google Colab and Juptyer Notebooks. These are perfect for beginners as they allow you to learn and test elements of code line by line. This makes it much easier to use and understand.

What is Python SEO? Essential Libraries, Tools, and Tips for Automating SEO Processes 1
Python scripts are easy to access and use. Most systems now already have Python installed making it much easier to run these scripts.
(Image Credit: Arupsentity)

Python Libraries

Python has a myriad of libraries that can enable a number of additional features. It is here that is Python’s biggest attraction. These libraries can do things like machine learning, natural language processing, analysis and preparation, data extraction, and even scientific computing. 

There are a range of useful libraries to aid with automation and data analysis. Pandas can be used for data manipulation and analysis, TensorFlow is an open source machine learning library, NymPy can be used for scientific computing which ScifPy can as well with the additional skill of technical computing. 

Python Libraries: Your SEO Toolbox

Now that you’re pumped about the possibilities of Python for SEO, let’s dive into the treasure chest of tools – Python libraries. These libraries are like pre-built modules packed with powerful functions, ready to be deployed to solve specific SEO challenges. Buckle up, as we explore some of the most valuable libraries for your SEO arsenal:

1. Data Acquisition and Manipulation:

  • Scrapy: This web scraping champion helps you extract valuable data from websites, like competitor backlinks, keyword rankings, and social media mentions. Imagine automatically gathering thousands of competitor backlinks for analysis – Scrapy makes it a breeze!
  • BeautifulSoup: This versatile library parses and extracts data from HTML structures, allowing you to analyze website content, identify on-page SEO elements, and even automate content audits. Think of it as your SEO scalpel, dissecting websites with precision.
  • Pandas: This data analysis powerhouse wrangles massive datasets, cleaning, manipulating, and summarizing them for easy interpretation. Analyze website traffic patterns, track keyword performance, and uncover hidden trends – Pandas is your data analysis maestro.

2. Keyword Research and Analysis:

  • NLTK (Natural Language Toolkit): This library unlocks the power of Natural Language Processing (NLP) for SEO. Analyze keyword sentiment, identify related keywords, and even generate unique keyword variations – NLTK helps you understand the language of your audience and target them effectively.
  • TextBlob: Another NLP gem, TextBlob performs sentiment analysis, topic extraction, and other linguistic tasks, allowing you to optimize your content for user intent and engagement. Imagine tailoring your content to user emotions and preferences – TextBlob makes it possible.
  • MozRank and Ahrefs APIs: These APIs provide access to valuable SEO data like keyword difficulty, competitor analysis, and backlink profiles. Integrate these APIs into your Python scripts to empower your research and decision-making.

3. Content Optimization and Automation:

  • Gensim: This library specializes in topic modeling, identifying hidden themes and patterns within large text collections. Use Gensim to automatically categorize your content, personalize content recommendations, and optimize your website structure for topical relevance.
  • Selenium: This powerful automation framework allows you to interact with web browsers programmatically. Imagine automating tasks like social media engagement, link building outreach, and even on-page SEO adjustments – Selenium is your robotic assistant for repetitive tasks.
  • PyAutoGUI: Another automation gem, PyAutoGUI lets you automate tasks on your desktop, like generating reports, scheduling content publishing, and even managing your SEO workflow. Think of it as your personal digital assistant, handling the mundane while you focus on strategy.

Other libraries of note include Requests which is a great option for those who want to make HTTP requests. SciKit Lear is great for machine learning if you want to do data analysis and mining. SpaCy can be used as a language processing library. These libraries make Python an incredible coding language to enhance automation and processes but how does it help with your technical SEO? 

Using Python for Technical SEO

When people think of the code that needs to be written to have a functional website, most people are drawn to languages like HTML, CSS, and even JavaScript. What they don’t realise is the incredible benefits that Python can bring to monotonous tasks. With a myriad of opportunities, Python offers you more time by automating simple tasks.

One of the major ways that Python aids SEO experts is through its ability to analyse and extract large data sets. Marketers are constantly working with large amounts of data and finding economical and time-saving ways to deal with it can solve problems later. It allows marketers to channel their energies into other important areas of SEO. This means website audits become a breeze. 

Due to these factors, Python has continued to grow in popularity with SEO experts. Python allows the data to be presented in a functional and understandable way making it easier for marketing professionals to make data-driven decisions that provide insights for clients and better justification for implementing new strategies.

Learning How to Automate with Python

Python scripts can be used to automate a lot of time-consuming tasks for marketers. While it can’t fully imitate a human strategy, it is incredibly useful when trying to speed up processes that are sometimes labour intensive. 

Python is developing at a rapid rate but there are plenty of things that can aid your marketing efforts. Python can help you find user intent and allows for internal link analysis as well as optimising images and scraping websites. It can also map URLS ahead of a migration and perform keyword research. There are plenty of useful scripts that you can use to help with your SEO efforts.

a screen capture of python data gathering information on the brexit campaign
Python scripts can be used to automate a lot of time-consuming tasks for marketers. (Image Credit: Conductor)

Python SEO Scripts 

Python can help marketers and SEO professionals in a number of ways and it is done through creating scripts. Here are some of our favourite scripts to try: 

Image Captioning with Pythia

Using Pythia, which is a modular deep learning framework created by Facebook, allows you to generate a caption for an image URL. This caption can then be used for images currently missing alt tags, which are important for accessibility and image search.

The script is based upon the bottom-up and top-down mechanism, which calculates results by focusing attention on different elements within an image.

Pythia Mechanism

For each word generated, attention is weighted to individual pixels within the image, outlining the region with the maximum attention. The ease of this script is due to the fact that it can be run straight from Google Colab and requires no direct coding. Once a copy of the necessary code is saved to your personal Google Colab drive, all cells can be run, performing each step for you. 

This will download the data sources needed to run the process, as well as automatically complete all of the steps that would typically need to be undertaken manually. For example, all libraries will be installed, classes will be created and functions assigned.

Pythia Captioning Cells

This will generate an area to add in your image URL and a button to caption the image. A caption will then be provided for each image, which can be directly used as an alt tag or to inspire the creation of one.

SEO Analyser

Created by Seth Black, on GitHub, this SEO Anaylser Script  is used to analyze the structure of a site by crawling it and providing an analysis of the basic SEO issues. It requires Python 3.4 or above, as well as the BeautifulSoup and urllib packages. Once installed you can then crawl a website from the homepage or XML sitemap.

Once it has finished crawling the site it will display data including word count, page titles, and meta descriptions as well as warnings, where applicable, for missing titles, meta descriptions and alt text.

Image Optimizer

Another GitHub find is this script, created by Victor Domingos, which is written in pure Python and is used to reduce the file size of images. It requires Python 3.6 or above, as well as the Pillow library, in order to run.

Once installed you will be able to optimize either a single image or a folder with multiple images, using the appropriate string detailed in the GitHub repository. It is worth noting that this script does optimize images destructively, so it’s recommended that you save a copy before running the operation.

an example of an n-gram analysis of url slugs being ran by python scripts
Python is also a popular language used to power machine learning applications due to its simple, intuitive and accessible syntax. (Image Credit: Pemavor)

Python Script Capabilities 

These are just some examples of the many automation and optimization possibilities using Python scripts, including:

  • 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.

a python script showing image captioning
Python can be used to power scripts for training a dataset, before it summarizes and visualizes the data. (Image Credit: SEJ)

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.

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 analyses 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.

Choosing the Right Tool for Your SEO Arsenal: Python vs. R, SQL, and Excel

Use CasesVersatile SEO tasks, automation, content optimization, data analysisAdvanced statistical analysis, data visualizationStructured data management, database integrationBasic data analysis, reporting
ProsVast library ecosystem, automation power, community and resources, learning curve manageableStatistical powerhouse, strong visualization, free and open-sourceEfficient data management, seamless database integration, powerful query languageUser-friendly interface, familiar for most, convenient for reporting
ConsLearning curve steeper than Excel or SQL, slower for large-scale data analysisSyntax challenging for beginners, limited automationNot ideal for complex data visualization, requires database experienceLimited scalability, prone to errors with manual data entry, basic automation
Best forData-driven SEO professionals, those seeking automation and content optimizationResearch-focused SEO, advanced data analysisSEO professionals with database experience, data management needsBasic data analysis, quick insights and reporting

Additional Notes:

  • Consider your skillset and comfort level with different tools.
  • Often, a combination of tools can be the most effective approach.
  • Don’t be afraid to experiment and find the right fit for your specific needs.
  • Remember, ongoing learning and adaptation are key to success in the evolving SEO landscape.

FAQ: Python SEO Demystified

Q: I’m new to SEO, is Python too advanced for me?

A: You don’t need to be an expert coder to leverage Python for SEO! Start with basic libraries like Pandas and TextBlob, and gradually expand your skills as you progress.

Q: Are there any free Python SEO resources available?

A: Absolutely! Check out online tutorials, open-source projects like Scrapy, and communities like r/PythonSEO for valuable learning resources.

Q: Python vs. Excel for SEO? Which one should I choose?

A: Excel is great for basic tasks and reporting, but Python offers more power and automation for complex SEO needs. If you’re handling large datasets or want to automate tasks, Python is the better choice.

Q: Can I use Python for local SEO?

A: Definitely! Libraries like Geocoder and Google Places API let you analyze local search data, optimize your listings, and target specific regions.

Q: What’s the future of Python in SEO?

A: Python’s role in SEO is only growing. With its constant evolution and vast library ecosystem, it’s poised to become an essential tool for any SEO professional seeking success in the ever-changing digital landscape.

Conclusion: Unleashing the Python SEO Powerhouse

Python isn’t just a programming language; it’s a secret weapon for SEO warriors. By leveraging its versatility, automation capabilities, and powerful libraries, you can unlock data-driven insights, optimize your workflows, and conquer the search engine landscape.

Remember, the journey with Python SEO is a continuous one. Keep learning, experimenting, and adapting your approach. With dedication and the right tools, you can unlock the full potential of Python and transform your SEO strategy into a powerful growth engine.

Learn How to Use Python Today 

Python can transform how marketers approach SEO, taking mundane tasks and performing them sufficiently and successfully, allowing more time to be invested in other areas or aspects of marketing or a business. 

Want to learn more about Python and how you can utilise it to help with your marketing efforts? Get in touch with ProfileTree today and speak to one of our SEO experts who will help you understand the importance of SEO for your business online.

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