Python is a versatile programming language

It can be used for a wide range of tasks, including search engine optimization (SEO). In this blog post, we'll explore how Python can be leveraged to boost your SEO efforts and drive more traffic to your website.

1. Web Scraping 🐸

One of the most useful applications of Python in SEO is web scraping. Web scraping involves extracting data from websites, and it can be a time-consuming process if done manually. Python makes it easy to automate this task by using libraries such as Beautiful Soup and Selenium.

Beautiful Soup is a library that allows you to parse HTML and XML documents and extract specific data. It's particularly useful for extracting data from websites that don't have APIs (Application Programming Interfaces) or for extracting data from websites that have APIs but limit the amount of data you can access.

Selenium, on the other hand, is a library that allows you to control a web browser through Python. This is useful for tasks that require interacting with web pages, such as filling out forms or clicking on buttons.

Together, Beautiful Soup and Selenium can be used to extract data from websites and perform tasks that would otherwise be tedious to do manually.

2. Analyzing Log Files 🪵

Another area where Python can be useful for SEO is analyzing log files. Log files contain information about the traffic to your website, such as the IP addresses of visitors, the pages they accessed, and the time they spent on your site.

Python makes it easy to process and analyze log files using libraries such as Pandas and Matplotlib. Pandas is a library that provides tools for data manipulation and analysis, while Matplotlib is a library for creating charts and graphs.

With these tools, you can create reports and visualizations that help you understand how users are interacting with your website. This can be useful for identifying trends and identifying areas for improvement.

3. Keyword Research 🔑

Keyword research is an essential part of SEO, as it helps you understand what users are searching for and how to optimize your website for those keywords. Python can be used to automate the keyword research process using libraries such as Scrapy and Pytrends.

Scrapy is a web scraping library that can be used to extract data from websites such as Google and Bing. This can be useful for identifying the most popular keywords for a given topic.

Pytrends is a library that allows you to access Google Trends data through Python. Google Trends is a tool that shows you how often a particular search term is entered into Google over a given period of time. With Pytrends, you can access this data and use it to identify trends and optimize your website for high-volume keywords.

4. Link Building 🔗

Link building is the process of acquiring links from other websites to your own. These links are important because they help improve the authority and credibility of your website in the eyes of search engines. Python can be used to automate the link building process using libraries such as Requests and Urllib.

Requests is a library that allows you to send HTTP requests through Python. This can be useful for tasks such as checking the status of a website or submitting a form.

Urllib is a library that provides functions for working with URLs. With Urllib, you can parse and manipulate URLs, which can be useful for tasks such as extracting data from a website or submitting a form.

5. SEO Auditing 💻

An SEO audit is a process of analyzing a website to identify areas for improvement in terms of search engine optimization. Python can be used to automate the SEO auditing process using libraries such as Beautiful Soup and Selenium.

Beautiful Soup can be used to extract data from the HTML of a website, such as the titles and meta descriptions of pages. This can be useful for identifying pages that are missing titles or have duplicate titles, as well as for identifying pages with long or short meta descriptions.

Selenium can be used to simulate a user interacting with a website, such as clicking on links and filling out forms. This can be useful for tasks such as checking the redirects on a website or identifying broken links.

In addition to Beautiful Soup and Selenium, Python also has libraries such as Requests and Urllib that can be useful for SEO auditing. Requests can be used to send HTTP requests to a website and check the status code of the response, while Urllib can be used to parse and manipulate URLs.

By using Python to automate the SEO auditing process, you can save time and effort and quickly identify issues that may be impacting the search engine visibility of your website.

6. Content Optimisation ✍️

Python can be used to help with content optimization in a number of ways. Here are a few examples:

• Extracting Data: Python can be used to extract data from the HTML of a website using a library such as Beautiful Soup. This can be useful for tasks such as identifying pages with duplicate or missing titles and meta descriptions.

• Analyzing Text: Python has libraries such as NLTK (Natural Language Toolkit) and Gensim that can be used to analyze text and identify patterns and trends. For example, you can use these libraries to identify the most common words or phrases on a website, or to identify synonyms for a given keyword.

• Generating Reports: Python has libraries such as Pandas and Matplotlib that can be used to create reports and visualizations of data. These reports can be useful for presenting findings to clients or stakeholders and identifying areas for improvement.

• Integration with Other Tools: Python has a wide range of libraries and frameworks that can be used to integrate with other tools and platforms, such as Google Analytics and Ahrefs. This can be useful for extracting data from these tools and using it to optimize the content on your website.

• By using Python to automate the content optimization process, you can save time and effort and quickly identify opportunities to improve the search engine visibility of your website.

7. Competitor Analysis 🥇

Competitor analysis is the process of examining the strategies, tactics, and performance of your competitors in order to gain insights and identify areas for improvement. Python can be a useful tool for competitor analysis in a number of ways:

• Web Scraping: Python has libraries such as Beautiful Soup and Selenium that can be used to scrape websites and extract data about your competitors. For example, you can use these libraries to extract data about your competitors' traffic, keyword rankings, and backlinks.

• API Access: Many SEO tools and platforms offer APIs that allow you to access data about your competitors. Python has libraries such as Requests and Urllib that can be used to access these APIs and extract data about your competitors.

• Data Analysis: Python has libraries such as Pandas and Numpy that can be used to analyze data and identify trends and patterns. You can use these libraries to analyze data about your competitors and identify areas for improvement in your own strategies.

• Data Visualization: Python has libraries such as Matplotlib and Seaborn that can be used to create charts and graphs to visualize data. You can use these libraries to create visualizations of data about your competitors, which can be useful for presenting findings to clients or stakeholders.

• By using Python to automate the competitor analysis process, you can save time and effort and quickly identify opportunities to improve your own SEO efforts.

Here are some interesting Python Scripts that I built to whelp me with my daily SEO tasks.

These scripts can be further improved and are simple tools to help me with my daily SEO tasks. Some of these if not all can be achieved using tools such as Screaming Frog, however, there is joy and simplicity on building your own tools and doing it all from the command line without any effort. It fits my workflow perfectly and I plan on creating other scripts to aid me on these tasks.

Keyword count

The below script will scrape a given webpage via user input and it will and return the number of times a given keyword is present on a specific page.

import requests
from bs4 import BeautifulSoup

# Make a request to the URL
url = input('Website to check: ')
response = requests.get(url)

# Parse the HTML of the page
soup = BeautifulSoup(response.text, 'html.parser')

# Find the text of the page
text = soup.get_text()

# Count the number of occurrences of the word "the"
keyword = input('Which keyword: ')
count = text.count(keyword)

print(f'keyword:', keyword )
print(count)

Get the text from anchor tags

This script allows you to quickly and easily grab the anchor text from any page. It excludes elements such as nav bars and footers. There is room for improvement here but it is a good starting point.

import requests
from bs4 import BeautifulSoup

# Make a request to the URL
url = input('url: ')
response = requests.get(url)

# Parse the HTML of the page
soup = BeautifulSoup(response.text, 'html.parser')

# Find the navigation and footer elements
navs = soup.findAll('nav' or 'header')
footers = soup.findAll('footer')

# Remove the navigation and footer from the HTML'
for match in navs:
    match.decompose()

for match in footers:
    match.decompose()

# Find all the anchor tags
tags = soup.find_all('a')

# Print the text of each anchor tag
for tag in tags:
    print(f'👉', tag.text)

Discover the most used keywords

This script fetches the most used keywords and outputs the frequency of times in which they appear on the page that you provide.

from typing import Counter
import requests
from bs4 import BeautifulSoup

# list of stop words (words not to include)
stop_list = [ "about", "blog", "contact", "find",
"full", "have", "list", "need", "news",
"their", "with", "your" ]

# prepare a word counter
word_count = Counter()

# lets get our web page (adjust to the url you want to review)
base_url = input('URL to check: ')
r = requests.get(base_url)

# parse the webpage into an element hierarchy and store in soup
soup = BeautifulSoup(r.text, 'html.parser')

# Get only the main text of the page as list of words
all_words = soup.get_text(" ", strip=True).lower().split()

#count words
for word in all_words:
    cln_word = word.strip('.,?')
    # ignore words less 4 char long
    if len(cln_word) > 3:
        # ignore words in our custom stop list
        if cln_word in stop_list:
            continue
        word_count[cln_word] += 1

# print 50 most common words
print(word_count.most_common(50))

Total keywords

This script outputs the total amount of words on a given page. It also neglects the navigational elements and the footer.

import requests
from bs4 import BeautifulSoup

# Make a request to the URL
url = input('URL to count: ')
response = requests.get(url)

# Parse the HTML of the page
soup = BeautifulSoup(response.text, 'html.parser')

# Find the elements for the nav and footer
navs = soup.findAll('nav' or 'header' )
footers = soup.findAll('footer')


#remove the nav and footer from the HTML
for match in navs:
    match.decompose()

for match in footers:
    match.decompose()

# Get the text of the page
text = soup.get_text()

# Split the text by spaces
words = text.split()

# Get the number of words
num_words = len(words)

print(num_words)

Future development

Combining all the above script might be a good option to reduce the number of python files and to make the operations faster when cycling through files. Some sort of item selection could be used to allow the user to select based on indexes and preferences.