8 Useful Python Libraries for SEO & How To Use Them

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If you are looking to learn more about how to use website marketing practices in Python, this post is for you. It will cover eight useful libraries and their uses as well as a brief tutorial on using those tools.

The “seo ranking python” is a guide that will teach you how to use 8 useful Python libraries for SEO. The libraries are: BeautifulSoup, PyQuery, Scrapy, Selenium, Requests, lxml, scrapy-web and W3C Markup Validation Service.

8 Useful Python Libraries for SEO & How To Use Them

Editor’s note: As the year draws to a close, we’re counting down the top 12 most popular and useful expert posts on SagaReach Marketing this year.

Our editorial staff selected this collection based on the performance, usefulness, quality, and value provided for you, our readers.

We’ll reprint one of the finest articles of the year every day until December 24th, beginning with No. 12 and going down to No. 1. Today, we begin our countdown with our No. 3 column, which was first published on March 18, 2021.

The post by Ruth Everett on using Python modules to automate and complete SEO duties makes a marketer’s job considerably simpler. It’s simple to read and understand, making it ideal for novices as well as more seasoned SEO experts who want to learn more about Python.  

Ruth, you did an excellent job on this, and we value your efforts to SagaReach Marketing.


Python libraries are an easy and enjoyable approach to begin learning and utilizing Python for SEO.


Continue reading below for more information.

A Python library is a collection of helpful functions and code that allows you to execute a variety of tasks without having to develop your own code.

In Python, there are over 100,000 libraries that may be used for anything from data analysis to video game development.

You’ll discover numerous different libraries in this post that I’ve utilized to complete SEO assignments and chores. They’re all user-friendly, and there’s plenty of documentation and resources to get you started.

What Are the Benefits of Python Libraries for SEO?

Each Python library provides a variety of functions and variables (arrays, dictionaries, objects, and so on) that may be used to execute various tasks.

They may be used to automate particular tasks, forecast results, and give intelligent insights in SEO, for example.

While working with plain Python is doable, libraries may make jobs much simpler and faster to create and finish.

Python Libraries for Search Engine Optimization

Data analysis, site scraping, and displaying findings are just a few of the SEO activities that Python packages can help with.


Continue reading below for more information.

This is by no means a complete list, but these are the libraries I use the most for SEO.


Pandas is a Python library for manipulating data tables. It enables for high-level data manipulation using a DataFrame as the primary data structure.

DataFrames are comparable to Excel spreadsheets, but they are significantly quicker and more efficient since they are not restricted by row and byte constraints.

The simplest approach to get started with Pandas is to take a basic CSV of data (for example, a crawl of your website) and save it as a DataFrame in Python.

After you’ve saved this in Python, you may use it to execute a variety of analytical activities, such as aggregating, pivoting, and cleaning data.

For example, if I have a full crawl of my website and just want to extract indexable pages, I may use a Pandas method to include only those URLs in my DataFrame.

pandas as a pd import df = pd.read csv(‘/Users/rutheverett/Documents/Folder/file name.csv’, ‘/Users/rutheverett/Documents/Folder/file name.csv’, ‘/Users/rutheverett/Documents/Fold indexable = df[(df.indexable == True)] df.head indexable = df[(df.indexable == True)] indexable


Requests is the next library, and it’s used to create HTTP requests in Python.

Requests makes requests using various request methods such as GET and POST, with the responses being saved in Python.

A basic GET request to a URL, for example, will print out the page’s status code:

response = requests.get(‘https://www.deepcrawl.com’) import requests print(response)

You may then utilize this information to build a decision-making mechanism, with a 200 status code indicating that the page is available and a 404 indicating that the page cannot be found.

print(‘Success!’) if response.status code == 200 response.status code == 404: print(‘Not Found.’) elif response.status code == 404: print(‘Not Found.’)

Different requests, such as headers, may be used to show relevant information about the page, such as the content type or the time it took to cache the answer.

response = headers response headers print(headers) headers[‘Content-Type’]

There’s also the option of simulating a certain user agent, such as Googlebot, to extract the answer this bot would receive when crawling the website.

‘User-Agent’: ‘SagaReach Marketingilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)’ headers = ‘User-Agent’: ‘SagaReach Marketingilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)’ print(ua response) = requests.get(‘https://www.deepcrawl.com/’, headers=headers) ua response = requests.get(‘https://www.deepcrawl.com/’, headers=headers) ua response = requests.get(‘https://www.deepcrawl.com/


Soup is lovely.

Beautiful Soup is a data extraction package for HTML and XML files.


Continue reading below for more information.

Fun fact: The BeautifulSoup library was named after a poem from Lewis Carroll’s Alice’s Adventures in Wonderland.

BeautifulSoup is a library for making sense of online files, and it’s most often used for web scraping since it can convert an HTML page into various Python objects.

For example, you may extract the title of a page from a URL using Beautiful Soup and the Requests library.

BeautifulSoup import requests url=”https://www.deepcrawl.com” from bs4 requests.get = req (url) BeautifulSoup = soup (req.text, “html.parser”) print title = soup.title (title)


BeautifulSoup also allows you to extract certain items from a page using the find all function, such as all a href links on the page:


Continue reading below for more information.

url=”https://www.deepcrawl.com/knowledge/technical-seo-library/” soup = BeautifulSoup(req.text, “html.parser”) for link in soup.find all(‘a’): req = requests.get(url) soup = BeautifulSoup(req.text, “html.parser”) for link in soup.find all(‘a’): print(link.get(‘href’))


Connecting the Dots

These three libraries may also be used in tandem, with Requests being used to perform an HTTP request to the website from which BeautifulSoup will extract data.

The raw data may then be transformed into a Pandas DataFrame for further investigation.

‘https://www.deepcrawl.com/blog/’ is the URL. requests.get = req (url) BeautifulSoup = soup (req.text, “html.parser”) soup.find all(‘a’) = links df = pd.DataFrame(‘links’:links) df = pd.DataFrame(‘links’:links) df = pd.DataFram df

Seaborn and Matplotlib

Two Python libraries for making visuals are Matplotlib and Seaborn.

You may use Matplotlib to build a variety of data visualizations, including bar charts, line graphs, histograms, and even heatmaps.


Continue reading below for more information.

For example, if I wanted to use Google Trends data to illustrate the most common searches over a 30-day period, I could use Matplotlib to generate a bar chart to depict everything.


In addition to line and bar graphs, Seaborn, which is based on Matplotlib, offers other visualization patterns such as scatterplots, box plots, and violin plots.

It varies from Matplotlib in that it contains built-in preset themes and utilizes less syntax.


Continue reading below for more information.

Seaborn has let me visualize log file visits to certain areas of a website over time by allowing me to generate line graphs.


sns.lineplot(x = “month”, y = “log requests total”, hue=”category”, data=pivot status) plt.show(x = “month”, y = “log requests total”, hue=”category”, data=pivot status) ()

This example uses data from a pivot table that I created in Python using the Pandas library, and it’s another example of how these libraries work together to generate a clear image from the data.


Advertools is a library established by Elias Dabbas that may be used to assist SEO experts and digital marketers organize, analyse, and make choices based on the data they have.


Continue reading below for more information.

Analyze the Sitemap

This library enables you to do a variety of operations, including downloading, parsing, and analyzing XML Sitemaps to extract patterns and determine how often material is added or modified.

Analysis of the robots.txt file

Another cool thing you can do with this library is use a function to extract the robots.txt file from a website into a DataFrame so you can easily understand and evaluate the rules.

You can also perform a test inside the library to see whether a certain user-agent is capable of fetching specific URLs or folder directories.

Examining URLs

Advertools also allows you to parse and analyze URLs in order to extract data and have a better understanding of analytics, SERP, and crawl data for specific URL sets.

The library may also be used to divide URLs and identify things like the HTTP scheme, the primary route, extra arguments, and query strings.


Selenium is a Python library that is often used to automate tasks. Web application testing is the most popular use case.


Continue reading below for more information.

A script that opens a browser and executes a number of distinct actions in a specific sequence, such as filling in forms or clicking particular buttons, is a typical example of Selenium automating a flow.

Selenium follows the same logic as the Requests library, which we discussed before.

It will, however, not only submit the request and wait for a response, but it will actually display the requested website.

In order to get started with Selenium, you’ll need a WebDriver to interface with the browser.

Each browser has its own WebDriver; for example, Chrome uses ChromeDriver and Firefox uses GeckoDriver.

These are simple to download and install using Python code. Here’s a helpful article with an example project that walks you through the setup procedure.


Scrapy is the last library I wanted to talk about in this blog.

While the Requests module may crawl and retrieve internal data from a site, it must be combined with BeautifulSoup in order to transfer that data and derive relevant insights.


Continue reading below for more information.

Scrapy effectively combines both of these functions into a single library.

Scrapy is also more quicker and more powerful, completing crawl requests, extracting and parsing data in a predetermined order, and allowing you to protect data.

You may specify a variety of instructions in Scrapy, including the name of the site you want to crawl, the start URL, and which page folders the spider is authorized to explore.

Scrapy, for example, may be used to extract all of the links on a given page and save them in an output file.

allowed domains = [‘www.deepcrawl.com’] class SuperSpider(CrawlSpider): name=”extractor” allowed domains = [‘www.deepcrawl.com’] start urls = ‘https://www.deepcrawl.com/knowledge/technical-seo-library/’ start urls = ‘https://www.deepcrawl.com/knowledge/technical-seo-library/’ start urls = ‘https base url=”https://www.deepcrawl.com” for link in response, define parse(self, response) yield xpath(‘/div/p/a’) self.base url + link.xpath(‘./@href’) = “link” get() is a function that returns a value.

You may take this a step further by following the links discovered on a site to extract information from all the pages connected to from the initial URL, which is similar to Google discovering and following links on a page on a smaller scale.

scrapy.spiders from scrapy.spiders import CrawlSpider, SuperSpider(CrawlSpider) rule class: allowed domains = [‘en.wikipedia.org’] name=”follower” start urls = [‘https://en.wikipedia.org/wiki/Web scraping’] start urls = [‘https://en.wikipedia.org/wiki/Web scraping’] start urls = [‘ base url=”https://en.wikipedia.org” ‘DEPTH LIMIT’: 1 custom settings = ‘DEPTH LIMIT’: 1 for next page in response function parse(self, response) yield response with xpath(‘./div/p/a’). For a quotation in response, use follow(next page, self.parse). yield xpath(‘./h1/text()’) quote.extract() ‘quote’: quote.extract() ‘quote’: quote.extract() ‘quote’: quote

More information on these initiatives, as well as other examples, may be found here.

Last Thoughts

“The greatest way to learn is through doing,” Hamlet Batista always said.


Continue reading below for more information.

I hope that learning about some of the libraries available has piqued your interest in learning Python or expanding your expertise.

The SEO Industry’s Python Contributions

Hamlet also enjoyed collaborating with others in the Python SEO community to share information and initiatives. I wanted to share some of the fantastic things I’ve seen from the community to commemorate his enthusiasm for inspiring others.

Charly Wargnier has formed SEO Pythonistas to gather contributions of the fantastic Python projects that others in the SEO industry have made as a lovely monument to Hamlet and the SEO Python community he helped to build.

The importance of Hamlet’s contributions to the SEO community is highlighted.

Moshe Ma-yafit wrote a fantastic script for analyzing log files, and he describes how it works in this article. Google Bot Hits By Device, Daily Hits by Response Code, Response Code Percentage Total, and more visualizations are available.

A Sitemap Health Checker is presently being developed by Koray Tuberk GÜBÜR. He also offered a script that captures SERPs and analyzes algorithms at a RankSense webinar with Elias Dabbas.


Continue reading below for more information.

It simply captures SERPs with frequent time delays, allowing you to crawl all of the landing sites, mix data, and draw some conclusions.

John McAlpin posted an essay on how to spy on your rivals using Python and Data Studio.

A thorough tutorial to utilizing the Reddit API was written by JC Chouinard. You can use this to do things like pull data from Reddit and upload to a Subreddit.

Rob May is developing a new GSC analysis tool and creating a few new domains/real sites in Wix to compare it against its higher-end WordPress rival while recording the process.

Masaki Okazawa also provided a Python tool for analyzing Google Search Console data.

@saksters wishes you a happy #RSTwittorial Thursday.

Using #Python to Analyze Google Search Console Data

The result is as follows: pic.twitter.com/9l5Xc6UsmT

February 25, 2021 — RankSense (@RankSense)

Christmas Countdown 2021 SagaReach Marketing:


Continue reading below for more information.

jakkaje879/Shutterstock/Featured Image

Watch This Video-

The “python machine learning seo” is a library that allows developers to use Python to build complex machine learning models. This tool can be used for many different purposes, including search engine optimization (SEO).

Related Tags

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  • automate seo with python
  • python seo keyword research
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  • python seo scraper

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