Pythonic SEO: The Ultimate Guide to Automating SEO Task in 2024 AI era

Are you an SEO? Looking for a way to speed up our SEO Manual Task? As I experienced 2 to 3 years in the SEO domain, Python SEO can solve your problems. Search Engine Optimization (SEO) takes a lot of time, concentration, patience, dedication, and some simple Tricks.

Though SEO is a long-term project and never a once-off thing, using some Python Code, you can automate some Manual Tasks, for example;

• Web Scraping for Collecting Data in Large Level
• Meta descriptions in bulk
• Keyword Clustering

Python is the most powerful programming language in the Automation AI and ML world. Of its, simple syntax and a huge level of the library, this language has revolutionized the SEO and Digital Marketing Field. And that’s why more and more SEO professionals are using automation to speed up boring and repetitive tasks with Python. SEO tasks that can be fully automated using Python code (as per your needs).

What is Python for SEO?

Python for SEO is a technique that utilizes the capabilities of the Python programming language to automate and enhance various aspects of search engine optimization (SEO) tasks. It involves writing scripts, programs, or frameworks that perform automated functions such as keyword research, content analysis, backlink analysis, competitor analysis, and website audits. These Python-based tools can help SEO professionals analyze large datasets, extract valuable insights, and streamline SEO processes, ultimately improving a website’s search engine ranking and visibility.

Why Learn Python for SEO?

Python is a high-level, general-purpose versatile programming language that has simple clean syntax. By using this programming language, the Automate SEO Process is very easy. Although this does not directly help in SEO, but using some script, you can speed up the overall SEO process such as Data Extraction using Requests and Scrapy, Data visualization using Matplotlib, and many more.

And here’s why Python is a smart choice for SEO professionals:

• Readability: Python code is known for being clear easy to understand, and human-friendly even for those without extensive programming experience.
• Versatility: Python’s libraries and frameworks cover a wide range of SEO tasks, from keyword research to technical SEO audits. Some are the most used for SEO are, request,bs4, Selenium,scrapy, and lxml. It’s like having a Swiss army knife of SEO tools at your disposal.

5 Python Code for automating SEO tasks

1. Web Scraping using Python

Web scraping using Python involves extracting structured data from websites using Python code. Web scraping, also known as web harvesting or web data extraction, refers to the process of automatically extracting data from websites. This is one of my favorite tactics to collect data from various targeted sites at a large level. In SEO, we all know the content and exact data are king, to get targeted data, web scraping is one of the most popular methods.

Suppose, you are doing programmatic SEO in the Car or Software niche site, using this data extraction method, you can get some facts type data(eg; Specifications, Model Variation, Price and image, and many more) from competitor sites and make your strategy.

Required libraries for web scraping use Python;

• pip install requests
• pip install bs4

Python

import requests
from bs4 import BeautifulSoup

def scrape_website_content(url):


    try:
        response = requests.get(URL)
        response.raise_for_status()

        soup = BeautifulSoup(response.content, "html.parser")

        title = soup.find("title").text
        headings = [h.text for h in soup.find_all("h2")]

        data = {
            "title": title,
            "headings": headings
        }

        return data

    except for requests.exceptions.RequestException as err:
        print(f"An error occurred: {err}")
        return None

if __name__ == "__main__":
    target_url = "https://www.geeksforgeeks.org/data-structures/"
    data = scrape_website_content(target_url)

    if data:
        print(data)
    else:
        print("Content could not be retrieved.")

What code will do;

• Parse HTML with BeautifulSoup: Inside the function, soup = BeautifulSoup(response. content, “html.parser”) parses the HTML content.
• Extract data with BeautifulSoup: The code now demonstrates extracting specific elements using BeautifulSoup’s methods (e.g., find(), find_all()).
• Structure extracted data: The extracted information is stored in a dictionary for better organization.

2. Meta Description in Bulk

Imagine you have a bunch of web pages on your website, and each page is like a virtual storefront. Now, think of meta descriptions as little snippets that give people a preview of what’s inside each storefront when they see it in search results. In SERP, Meta Description play a big role in whether people decide to click on your link or not. If you don’t bother writing them, Google might just create its snippet, and it may not be as appealing.

Now, let’s say you have a ton of pages without these snippets, especially if you run an online store. Writing Meta Descriptions for tons of web pages manually could be a real pain. That’s why this Python script comes to help you.

Python

from sumy.parsers.html import HtmlParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
from sumy.summarizers.lsa import LsaSummarizer
import csv


with open('urls.txt') as f:
    urls = [line.strip() for line in f]


results = []


for url in urls:
    parser = HtmlParser.from_url(url, Tokenizer("english"))
    summarizer = LsaSummarizer(Stemmer("english"))
    summarizer.stop_words = get_stop_words("english")
    description = " ".join(sentence._text for sentence in summarizer(parser.document, 3))
    description = description[:152] + '...' if len(description) > 155 else description
    results.append({'url': url, 'description': description})


with open('results.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['url', 'description'])
    writer.writeheader()
    writer.writerows(results)

NOTE: You need a text file of all URLs.

3. Keyword Clustering

Keyword clustering is a technique used in SEO to group similar keywords together for better organization and analysis.

Keyword clustering is the process of grouping similar keywords based on their semantic similarity and user intent . It’s a valuable technique used in various fields, particularly in SEO (Search Engine Optimization). After getting a new SEO project, first, we all do Keyword Research, which is necessary in the early stages. But, when it comes to thousands of keywords, it becomes very difficult to analyze the keyword making grouping challenging.

That’s where Python comes in. With Python, we can automatically arrange terms into related clusters to find trends and finish our keyword mapping.

4. Generating XML Sitemap Strategically

An XML Sitemap is a structured document in XML (Xtensible Markup Language) format, containing website links, that serves as a roadmap for search engines to navigate and index the content of a website. To put it simply, XML sitemaps inform Google about the most essential pages on your website and which ones to crawl. They function much like physical maps of your website.

Managing a dynamic website with a vast number of pages poses challenges in monitoring indexed content, particularly when all URLs are consolidated in a massive XML file. In such cases, identifying the status of individual pages becomes a formidable task.

For example, the best seller on an e-commerce site or the most popular destination on a travel site. Now, by mistake, If you mix your most important travel pages with other less important pages in your XML sitemap, you won’t be able to tell when your best pages are having crawling or indexing problems.

On the other hand, you may quickly and simply make customised XML sitemaps with just the sites you want to send to Google Search Console and deploy to your server by using Python scripts.

5. Comprehensive On-Page SEO Analysis

Unlock the power of our Python-based SEO analyzer. Python SEO analyzers offer a powerful way to conduct thorough page-level examinations, pinpointing areas for improvement and guiding strategic enhancements. By writing some basic Python code, you can analyze some on-page SEO parameters at a large level(thousands of web pages) such as:

• Does the page have a title tag at all, or does it have a decent one?
• How many words are on this page?
• What is the Keyword Density?
• How many Internal and External Dofollow and Nofollow links does the page have?
• Does the page have any Image with a proper ALT tag?
• Does the page have the right structured data?
• Is the meta description absent or does it include enough hooks to have someone click?
• What is the most common phrase used on this page?

Conclusion

Python is a potent and versatile tool for SEO professionals. It is a game changer for SEOs. Using Python Automation is helping SEO professionals save time and become more efficient so that professionals can focus more on organic strategies.