How to Use Python for SEO – 8 use cases for SEO automation

Python has gained significant popularity in the field of marketing due to its versatility and ease of use. With its extensive libraries and frameworks, Python offers marketers a wide range of tools to automate tasks, analyze data, and enhance marketing strategies. In this blog, we will explore various practical use cases where Python can be applied to automate SEO. Each use case will be accompanied by complete Python code to help you get started. Let’s dive in!

Essential Python Libraries required

Before diving into the actual code, let’s understand the common libraries that are essential for creating SEO automation scripts. These libraries help with tasks such as web crawling, parsing, reading/writing Excel files, data analysis, and more.

By leveraging these libraries, you can create robust and efficient SEO automation scripts that can handle tasks such as web crawling, data extraction, data processing, and quality assurance. However, keep in mind that some of these libraries may require additional dependencies or configurations based on your specific use case.

1. Web site Crawler

With website crawler you can find out critical errors like missing pages, broken links etc.
Python allows marketers to inspect their own website to find out web errors and overall health of website. Python’s libraries like BeautifulSoup and Scrapy make web scraping a breeze. 

Full code to website crawler can be found here – crawl an entire website using python

How does it work?

• Import necessary libraries/modules including os, signal, BeautifulSoup from bs4, requests, openpyxl, datetime, urlparse from urllib.parse, urllib3, and sys. A higher recursion limit is set using sys.setrecursionlimit().
• Define the URL to crawl (url), skip patterns (skip_patterns), and file path for the Excel workbook (file_path).
• Set up the Excel workbook (wb) using openpyxl and an internal worksheet (ws_internal) with headers defined by headers_internal.
• Define the function crawl_website to crawl the website recursively, extract data, and write it to the Excel workbook. Handle the interrupt signal (signal.SIGINT) using interrupt_handler.
• Handle exceptions during crawling using try and except. Call the crawl_website function for the main URL (url).
• Finally, save the Excel file using wb.save(file_path).

2. Index your website faster using google indexing API

Indexing your website faster is crucial for improving its visibility on search engines and driving organic traffic. The Google Indexing API allows webmasters to notify Google about new or updated content on their site, expediting the indexing process. By using Python to interact with the API, you can automatically submit URLs for indexing as soon as they are published or modified. This not only helps search engines discover your content faster but also ensures that the latest version of your web pages appears in search results promptly. 

Here is full code to implement the same – Google Indexing API with Python

How does it work?

• Imports necessary libraries/modules including ServiceAccountCredentials from oauth2client.service_account, build from googleapiclient.discovery, httplib2, and openpyxl.
• Defines constants such as EXCEL_FILE, JSON_KEY_FILE, SCOPES, and ENDPOINT for the Excel file path, JSON key file path, OAuth scopes, and Google Indexing API endpoint respectively.
• Authorizes credentials using ServiceAccountCredentials.from_json_keyfile_name() and builds a service object with build().
• Defines a callback function insert_event() to handle batch HTTP request responses.
• Initializes a batch HTTP request object using service.new_batch_http_request(callback=insert_event).
• Reads URLs from the Excel file specified in EXCEL_FILE using openpyxl, and appends them to a list urls.
• Iterates over each URL in the list urls and adds a URL notification request to the batch using batch.add().

3. Semantic Keyword Clustering

Semantic Keyword Clustering is a powerful marketing use case that Python can efficiently handle. With the vast amount of data available on the internet, marketers often struggle to organize keywords effectively for their SEO and content strategies. By utilizing Python’s Natural Language Processing (NLP) libraries, such as spaCy or others, marketers can group keywords based on their semantic meaning rather than exact match phrases. This approach enables the identification of related keywords, allowing marketers to create more comprehensive and relevant content, target a broader range of search queries, and improve their website’s overall search engine ranking. 

Here is full code to Semantic Keyword Clustering with Python

How does it work?

• Imports necessary libraries/modules including pandas as pd , spacy(for spacy model) and sentence_transformers import SentenceTransformer(for Bert and Roberts model)
• Loads the appropriate model using load() function.
• Reads keywords from an Excel file located at ‘C:\keywordcluster.xlsx’ using pd.read_excel().
• Processes keywords using model, obtaining document vectors for each keyword.
• Groups keywords by similarity, forming clusters.
• Identifies the pillar page keyword for each cluster based on maximum similarity within the cluster.
• Prints clusters along with their pillar page keywords.
• Creates a new DataFrame containing clusters and pillar page keywords.
• Writes the DataFrame to a new sheet in the same Excel file using pd.ExcelWriter().

4. Check active backlinks

The Backlinks Checker is another valuable use case for Python in marketing. Backlinks are essential for improving a website’s authority and search engine ranking. With Python, marketers can automate the process of checking and monitoring backlinks to their website. By leveraging Python libraries like Requests and BeautifulSoup, marketers can crawl through various web pages to identify and extract backlinks pointing to their site. Additionally, Python can analyze the quality and relevance of these backlinks, enabling marketers to assess the impact on their SEO efforts. With an automated Backlinks Checker implemented in Python, marketers can efficiently manage their link building strategies, identify potential opportunities for collaboration, and proactively address any negative backlinks that could harm their website’s reputation. 

Here is full code to check existing backlinks and their quality – Dofollow or Nofollow

How does it work?

• Imports necessary libraries/modules including pandas as pd, date from datetime, requests, and BeautifulSoup from bs4.
• Defines a function get_backlink_type(url) to determine the type of backlink (dofollow or nofollow) for a given URL.
• Makes a GET request to the URL, parses the HTML content using BeautifulSoup, and finds all anchor tags (<a>).
• Filters anchor tags containing the backlink URL (backlink_url) and determines their rel attributes to identify if they are dofollow or nofollow.
• Returns the type of backlink or an error message if the request fails or no backlink is found.
• Handles different scenarios for backlink types and prints corresponding messages.
• Catches requests.exceptions.RequestException and returns appropriate error messages.

5. Build a Grammatical and Spelling Website Checker

Grammar and spell check is also a very crucial component of website SEO. Google doesn’t like sites with grammatical errors. You can use python code to crawl entire website, identify grammar and spelling errors, and store the results in an Excel file. This can be a valuable tool to improve the SEO of your website.

How does it work?

• Imports necessary libraries/modules including os, signal, BeautifulSoup from bs4, requests, openpyxl, datetime, urlparse from urllib.parse, urllib3, sys, and language_tool_python.
• Defines the website URL to crawl (url), skip patterns (skip_patterns), and file path for storing data (file_path).
• Removes the existing file if it exists and creates a new workbook with an internal worksheet.
• Defines a set to store visited URLs (visited_urls).
• Initializes LanguageTool for grammar and spelling error checking.
• Defines a function crawl_website to extract data from the website and write it to Excel.
• Calls the crawl_website function for the main URL
• Saves the Excel file and prints the file path.

6. Finding Existing Pillar Pages and Their Cluster Topics

Understanding the structure and interconnections of a website is crucial for effective SEO, user experience, and content strategy- specially, how pillar pages are interconnected with their clusters. With the help of Python and Gephi we can visualize the architecture of websites and gain valuable insights into the relationships between pages. In this use case, we will explore we can create visual representations of complete website, providing a clear and comprehensive overview of pillar and cluster pages and how they are interconnected.

Here are step by step instructions to find Pillar pages and their Cluster topics

7. Finding right Keywords and their Density 

Keywords are the building blocks of effective SEO and content marketing strategies. By performing TF-IDF (Term Frequency-Inverse Document Frequency) analysis using Python, marketers can identify the most important and relevant keywords in a collection of documents. TF-IDF assigns a weight to each term based on its frequency in a document relative to its occurrence in the entire corpus. In this use case, we will explore how Python can be employed to extract essential keywords through TF-IDF analysis, providing valuable insights for optimizing content and improving search engine rankings.

8. Find Contextual Internal Links on Website with Python

In today’s digital landscape, optimizing website content for both users and search engines is paramount. One essential aspect of this optimization is creating effective internal linking structures that enhance user experience and improve search engine rankings. Contextual internal linking plays a pivotal role in achieving these goals. Python, can be used to identify and harness the importance of contextual internal links on websites that can drive traffic, improve SEO, and ultimately enhance the overall performance of your website.

Here is full code to finding contextual Internal links on website with Python

How does it work?

• Imports necessary libraries/modules including os, signal, BeautifulSoup from bs4, requests, openpyxl, urlparse from urllib.parse, urllib3, sys, and re.
• Defines the website URL to crawl (url), skip patterns for crawling (skip_patterns_crawl), skip patterns for storing URLs in Excel (skip_patterns), and file path for storing data (file_path).
• Initializes headers for the Excel file and writes them to the internal worksheet.
• Defines a set to store visited URLs (visited_urls) and a counter for crawl number.
• Defines functions should_skip() and should_skip_crawl() to determine if a URL should be skipped based on skip patterns.
• Defines a function crawl_website() to extract data from the website and write it to Excel, filtering URLs based on skip patterns and HTML structure.
• Calls the crawl_website function for the main URL, handling exceptions during crawling.
• Saves the Excel file and prints the file path.

Other user cases of python for SEO

• Checking schema data
• Checking page speed
• Identifying SSL certificates
• SEO Analyzer script
• Link validator script
• Automating redirect maps
• Writing meta descriptions in bulk
• Analyzing keywords with N-grams
• Grouping keywords into topic clusters

Conclusion

Python has become an invaluable tool for marketers, offering a multitude of use cases to enhance marketing efforts

AIHelperHub

Managing Editor at AIHelperHub | Website

AIHelperHub is an expert in AI-focused SEO/Digital Marketing automation and Python-based SEO automation and enjoys teaching others how to harness it to futureproof their digital marketing. AIHelperHub provides comprehensive guides on using AI tools and python to automate your SEO efforts, create more personalized ad campaigns, automate content journey, and more.

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