Using Python To Automate Content Optimisation

Using Python for Auditing Large Datasets

As an SEO expert, I’ve always been fascinated by the potential of programming languages to streamline and enhance my SEO process. Among these languages, one stands out for its simplicity, versatility, and power: Python.

In this article, I’ll delve into the benefits of using Python for on-page SEO optimisation, providing you with practical examples and insights to help you harness this powerful tool.

Understanding Python and SEO

Before we dive into the benefits, let’s first understand what Python is and how it relates to SEO. Python is a high-level, interpreted programming language known for its readability and simplicity. It’s widely used in various fields, from web development to data analysis, and yes, even SEO.

On-page SEO, on the other hand, refers to the practice of optimising individual web pages to rank higher and earn more relevant traffic in search engines. It involves aspects like content quality, keyword placement, HTML tags, and more.

Python SEO Video Tutorial

For a more visual guide on using Python for SEO, check out this YouTube video.

Python for SEO: How to Automate Keyword Research

Why Use Python for SEO?

Python’s simplicity and power make it an excellent tool for SEO. Here’s why:

  • Automation: Python can automate repetitive tasks, saving you time and effort. For instance, it can automatically extract metadata, check broken links, or analyse keyword density.
  • Data Analysis: Python’s powerful libraries, like Pandas and NumPy, can handle large datasets, making it perfect for analysing SEO data.
  • Web Scraping: With Python, you can easily scrape web pages to extract useful information, such as competitor data or keyword ideas.
  • Machine Learning: Python is a popular language for machine learning, which can be used to predict SEO trends or understand user behaviour.

Using Python for On-Page SEO Optimisation

Now that we understand why Python is a good fit for SEO, let’s explore its specific benefits for on-page SEO optimisation.

Efficient Keyword Analysis

Python can automate the process of keyword analysis, helping you identify the best keywords for your content. For example, you can use Python to scrape Google’s Keyword Planner for keyword ideas, analyse keyword density on your pages, or even predict keyword performance using machine learning.

Improved Content Optimisation

Python can also help you optimise your content for SEO. For instance, you can use Python to analyse your content’s readability level, review grammar and spelling, check for duplicate content and optimising your SEO meta tags are some examples.

What Technical SEO Tasks Can Be Done Using Python?

Python can also assist with technical SEO tasks, such as checking for broken links, generating XML sitemaps, or analysing page load times using Google Lighthouse or GTMetrix.

Website Audit and Reporting

Python can be used to automate the process of site audits, which are crucial for identifying issues that might affect a website’s performance in search engine results.

A website audit typically involves checking for broken links, assessing page loading speeds, reviewing meta tags, and ensuring that the site is mobile-friendly. Using Python, you can script these checks to run automatically, saving time and ensuring consistency in your audits.

Python’s libraries like BeautifulSoup for parsing HTML and requests for handling HTTP requests make it easy to automate these tasks. This automation helps in regularly monitoring the health of a website and quickly identifying and rectifying any issues that could harm its search engine rankings.

Python Data Analysis and Data Blending

Python excels in handling and analysing large datasets, or combining data to make it easier to perform SEO data analysis. This task involves collecting and examining data from various sources like search engine results pages (SERPs), keyword rankings, and website traffic statistics.

This gives you insights into relevant SEO performance. Python’s powerful data analysis libraries, such as Pandas, Matplotlib and NumPy, allow for efficient manipulation and analysis of large datasets.

It is also possible to use the Google Search Console API and the Google Search API in python to download the relevant data as CSV files. You can then load the data into Panda data frames and combine the data to uncover hidden insights.

By using Python, SEO professionals can automate the collection and analysis of data, identify trends, track the performance of keywords, and make data-driven decisions to optimise your SEO strategies.

Keyword Research and Analysis

This is one of the most important parts you need to get right. If you target the wrong keywords you’re setting yourself up to fail before you’ve even started writing the content. Keyword research and analysis are fundamental SEO tasks where Python can provide significant advantages.

This process involves identifying the keywords and phrases that potential customers use to search for products or services. Python can automate the extraction of keyword data from various sources, analyse keyword trends, and even help in understanding the competitive landscape.

Libraries like Google’s Keyword Planner API can be integrated with Python to fetch extensive keyword data. Python’s ability to handle large volumes of data and perform complex computations allows for deeper insights into keyword effectiveness, helping to refine SEO strategies and content creation.

Backlinks are a vital component of SEO, and Python can be used to automate the process of backlink analysis. This task involves evaluating the quality and quantity of backlinks to a website, which is essential for understanding a site’s authority and ranking potential.

Python scripts can be written to crawl the web, extract backlink data, and analyse the relevance and quality of these links. Tools like Scrapy can crawl websites to gather backlink data, and it’s one of my favourite tools. You can easily add logging, error reporting, data extraction callback functions and more.

Python libraries like NetworkX can help in analysing the link structure. Automating backlink analysis with Python saves time and provides more accurate insights into the backlink profile of a website, aiding in the development of effective link-building strategies.

Monitoring Search Engine Result Changes

Search Engine Results Page (SERP) monitoring is crucial for understanding the visibility and ranking of a website on search engines. Python can automate website rank tracking in the search engine results page for various keywords.

This involves scraping search engine results and analysing the position and changes over time. Libraries like Selenium or BeautifulSoup can be used for web scraping Google’s search engine results page, while Pandas can be used for storing and analysing this data.

Automating SERP monitoring with Python provides valuable insights into how different SEO strategies are impacting website rankings and visibility.

Freelancing vs. Agency Work in Web Development
This woman automates her SEO workflows and she feels great!

Case Study: Automating Keyword Analysis with Python

In one case study, a digital marketing agency used Python to automate their keyword analysis process. They developed a script that scraped Google’s Keyword Planner for keyword ideas, analysed keyword density on their clients’ pages, and predicted keyword performance using machine learning. This automation saved them hours of manual work and significantly improved their SEO performance.

Python Script Examples

Using Python to Optimise Meta Tags

Below is my process for writing a python script to analyse website meta tags for an array of URLs or crawling the website sitemap to make it more dynamic.

  • First, import the required Python libraries. You would need libraries like BeautifulSoup, Requests, and Pandas for this task. BeautifulSoup is for web scraping, Requests is for making HTTP requests, and Pandas is for data manipulation and analysis
  • Create a function to fetch the URL. Here, you will use the ‘Requests’ library to fetch the HTML content of the web page
  • Create a function to parse the HTML content. You will use the ‘BeautifulSoup’ library to parse the HTML content and extract the meta tags
  • Create a function to extract the meta tags. This function will extract the title, description, keywords, and other meta tags from the parsed HTML content
  • Create a function to validate the meta tags. This function will check if the extracted meta tags meet the SEO standards. For example, the title tag should be between 50-60 characters, the meta description should be between 150-160 characters, etc.
  • Create a function to optimise the meta tags. If the meta tags do not meet the SEO standards, this function will optimise them. For example, if the title tag is too long, the function will shorten it.
  • Use Open AI’s Natural Language Processing to generate related LSI keywords and use them in the title to improve click through rates
  • Create a main function to tie all the other functions together. This function will take a sitemap or an array of URLs as input, and it will use the other functions to fetch, parse, extract, validate, and optimise the meta tags for each URL.
  • Finally, use the ‘Pandas’ library to store and analyse the extracted and optimised meta tags. You can store the meta tags in a DataFrame and export them to a CSV file for further analysis
  • Implement a loop process to go through each URL from your sitemap or array of URLs
  • Run the program, and review the output CSV file to see the extracted and optimised meta tags for each URL

Python is a popular and powerful scripting language that can be used to perform various tasks. One of those tasks includes checking for broken links in a website.

Broken links can lead to a poor user experience, lower search engine ranking, and loss of potential customers. It is essential to keep track of and fix broken links as soon as possible. Checking for broken links manually can be a time-consuming task, especially for large websites.

Python, can automate this task, making it more efficient, reliable and faster. Here’s how I would go about writing a Python script for checking broken links:

  • Import necessary libraries: Python uses libraries like requests and BeautifulSoup to send HTTP requests and parse HTML respectively.
  • Create a proxies.txt file with one proxy IP Address, Protocol and Poxy per line
  • Write a function that reads the proxies file and picks one at random
  • Send a request to the website: This can be done using the `get()` method from the requests library. You can pass in an option for headers where you can specify the proxy
  • Parse the HTML: Use BeautifulSoup to parse the HTML content of the website
  • Find all links: Find all the ‘a’ tags because the ‘href’ attribute typically contains the URL
  • Check each link: Use a for loop to iterate over each link and send a request to the URL in the ‘href’ attribute. If the status code is 200, the link works. If it’s 404, the link is broken

Please Note: Excessive requests to a website in a short amount of time might be considered as a denial-of-service attack. So use this script responsibly.

You might want to review the use of proxy IP’s with a function to select a random proxy address that gets rotated.

I hope this article has given you a better understanding of Python’s benefits for on-page SEO optimisation. If you have any questions or thoughts, feel free to leave a comment below.

And if you found this article helpful, why not subscribe to my newsletter? You’ll get more insights and tips on SEO, Python, and more. Or if you need help with your SEO, don’t hesitate to hire me. I’d be more than happy to assist you.

Relevant Resources




Google’s Keyword Planner API





Can You Think Of Other Ways To Automate SEO Using Python?

Python’s power and versatility make it an invaluable tool for on-page SEO optimisation. Whether you’re analysing keywords, optimising content, or checking for broken links, Python can automate and streamline these tasks, saving you time and improving your SEO performance.

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