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May 20, 2022

Ways Python Can Boost Your Technical SEO

Python is a powerful multi-purpose language that programmers use for everything from web scraping to data analysis. It's not only great for back-end development, but also has technical SEO applications as well.

As a general-purpose programming language, Python is used for web development, software development, mathematics, system scripting, and more. Although Google doesn't use Python for its search engine, several teams within Google are using Python. Programs written in Python, such as Gmail and YouTube, are frequently accessed by millions of people.

Other examples of how Google has integrated Python include the following products:

  • App Engine (Python runtime environment)
  • Udacity (online courses)
  • Google APIs Client Library for Python
  • dfp-api-python-client (Google DoubleClick for Publishers API)

The Python community is both large and active and open source projects in Python exist for nearly every task under the sun.

Python's popularity has given rise to an incredibly supportive community of developers and a wealth of resources for beginners and experts alike. Python libraries, or groups of modules containing functions written in the Python language, exist for nearly every task under the sun. You'll find libraries for data analysis, web scraping, machine learning, natural language processing, computer vision automation, web development—even scientific computing.

The Python Package Index (PyPI) allows you to search through almost 230K packages (and counting). The most popular packages are grouped under PyPI's "Top Packages" section so that you can easily find whichever library is best suited to your needs.

If Python isn't already part of your technical SEO toolbox then you're missing out on a lot of useful functionality and potential cost savings. If you're not using it yet and want some help integrating it into your programmatic workflow then get in touch!

A number of commonly used packages are designed for data analysis and scientific computing.

There are a number of commonly used packages that are designed for data analysis and scientific computing. While these are not SEO-specific, they can be very useful tools to have in your toolbelt. Some examples include:

  • NumPy and SciPy — Mathematical libraries for complex numerical computations
  • Pandas — A package that provides structures and functions to manipulate tabular data (e.g., dataframes)
  • Matplotlib, Seaborn, Plotly, ggplot2, Pygal — Packages for creating beautiful visualizations of your datasets

Alongside these basic packages there are also more specialized packages that you may want to look into when you want to get started on some machine learning or computer vision projects (read: image processing). These include:

  • Tensorflow, Keras and PyTorch — Deep learning frameworks for building neural networks with lots of documentation and community support (check out this great guide on machine learning with Python from
  • OpenCV and scikit-image — Libraries for computer vision

You can create powerful scripts that parse your log files to glean information about your website's traffic, metrics, and crawl behavior.

One of the most powerful aspects of Python is how it can take messy data and output it into a friendly format that you can then do more with. For example, you could export data in CSV format which Excel can read to make graphs with. You could also use JSON, XML or HTML formats.

You can leverage the power of the Python community by using a library like Scrapy which will do most of the work for you. Scrapy can be used to scrape websites and put them in databases or JSON files for further analysis.

Web Scraping

  • Python could be used for web scraping and for link analyze to gather important information about your competitors.
  • There is a python module called Scrapy that can go through websites and put them in databases or JSON files for further analysis.
  • Using web scraping you could analyze how often competitors links are posted on other websites (or vice versa) which could give you ideas on how to build relationships with those who have linked to them before or provide insights into what types of content gets the most links or where links are coming from geographically speaking.

Why python is better for SEO?

  • It’s the most popular language
  • You can do anything with it
  • It doesn’t take long to learn.
  • It is easy to read and understand.
  • It is free, it is open source and it has a lot of applications

Use Python to scrape data from the web.

Scraping data from websites using Python is not the easiest thing to do, but it’s not impossible. In this post, I’ll show you how you can scrape a website using Python and BeautifulSoup.

The first section of the code requires you to import the necessary libraries:

import requests

from bs4 import BeautifulSoup

import pandas as pd

Next, open a URL of your choice (I used

page = requests.get('') soup = BeautifulSoup(page.content, 'html.parser')

Use python to create a piece of content.

  • Using Python for Content Marketing

Perhaps the most obvious way to use Python is for content marketing. The language has one of the largest communities in the world, and with it comes tons of libraries and tools that can help you generate written content and it is able to easily understand the Googlebot. You can use python to scrape websites, sites like Quora or Reddit for questions that your website could answer, and so much more. Alternatively, if you have thousands of blog posts on your website, you can scrape them yourself and create new content by combining existing posts that are similar topics.

  • Python for Content Analysis

This is another great way to use Python as a technical SEO tool. Every site has a vast amount of data that they could be using to improve their performance online.

Use case 1: Use Python to determine search intent.

You can use Python to determine search intent when performing keyword research. Think of this as the "why" behind a user query, or the motivation for why someone entered a particular phrase into Google. Knowing the search intent for your targeted long-tail keywords can help you produce more relevant and helpful content to capture organic traffic from those terms (and make people happy!).

For example, if you're trying to rank for "shoes", it might seem like a good idea to write about shoes and their history, colors, styles—but that's not what people usually mean when they enter that term into Google. Instead, users are most likely looking for places to buy shoes. Therefore, the search intent is transactional: it relates to making a purchase or finding information before making a purchase. So while an article on shoe colors may be interesting, it wouldn't do well in terms of SEO because it doesn't match up with what users often want when searching with that phrase.

How to use python for keyword research

Before we begin, let’s go over the prerequisites for this tutorial. You should know:

  • how to create a project in your code editor
  • how to use terminal
  • some basic understanding of Python programming language concepts such as data structures and functions
  • some knowledge of APIs and Google search results (this will come later)

If you don’t have any prior coding experience, I recommend taking a course like Codecademy’s Intro to Python first if you are interested in learning about web scraping with Python. It is also suggested that you have Anaconda installed on your computer so that you can easily install all the necessary libraries.

Python for scraping a page's contents and comparing that with their schema markup.

  • You can use Python to scrape and parse all kinds of data from the web, including metadata
  • You can use Python to check that a page's metadata matches its content

So for example you could create your own Python script which crawled through a website's pages.

At each page it crawled:

  • It would then scrape the contents of the page (to get the actual copy in there) and
  • It would then extract all of the meta data from that page.
  • Then it would compare both pieces of information and report back on any differences.

Python for SEO audits.

In this section, we'll look at how to use Python in technical SEO audits.

A good SEO audit is a deep and thorough look at what is and isn't working when it comes to optimizing a site for search engines. It should include an analysis of your website's architecture and performance, as well as an extensive audit of your current on-page and off-page SEO strategies.

Your audit should provide you with actionable recommendations that you can implement right away, as well as highlight opportunities for long-term growth.

Among the most important factors included in a good technical SEO audit are page speed, security vulnerabilities, crawlability, title tag optimization, canonical URLs, image optimization, and XML sitemap creation.

How does Python work with SEO? What features make Python such a fantastic option for SEO? Why should you learn Python for SEO?

Python is a programming language that can be used to create software applications and automate tasks. SEOs can use Python to create scripts that can scrape data from websites and crawl through the Internet, identify links, analyze competitors' websites and much more.

Just a few of the features that make Python such a fantastic option for SEO include:

  • it's free!
  • it's easy to learn (there’s an active community with helpful resources!)
  • there are libraries available, which allow you to do pretty much anything you want!

Author bio:-

Kosha Shah is a digital strategist at Technostacks Infotech, a top web, mobile, and python development company in India, USA, and UK. She writes engaging blog topics for trends, mobile, and industry software news.


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