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.
As we have already 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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.