8 Useful Python Libraries for SEO & How To Use Them

Python libraries are a enjoyable and accessible method to get began with studying and utilizing Python for SEO.

A Python library is a set of helpful features and code that assist you to full quite a few duties without having to put in writing the code from scratch.

There are over 100,000 libraries out there to make use of in Python, which can be utilized for features from information evaluation to creating video video games.

In this text, you’ll discover a number of completely different libraries I’ve used for finishing SEO initiatives and duties. All of them are beginner-friendly and also you’ll discover loads of documentation and sources that will help you get began.

Why Are Python Libraries Useful for SEO?

Each Python library comprises features and variables of every kind (arrays, dictionaries, objects, and so on.) which can be utilized to carry out completely different duties.

For SEO, for instance, they can be utilized to automate sure issues, predict outcomes, and supply clever insights.

It is feasible to work with simply vanilla Python, however libraries may be used to make tasks much easier and faster to put in writing and full.

Python Libraries for SEO Tasks

There are quite a few helpful Python libraries for SEO duties together with data analysis, internet scraping, and visualizing insights.

This is just not an exhaustive listing, however these are the libraries I discover myself utilizing probably the most for SEO functions.

Pandas

Pandas is a Python library used for working with desk information. It permits for high-level information manipulation the place the important thing information construction is a DataBody.

DataFrames are just like Excel spreadsheets, nonetheless, they don’t seem to be restricted to row and byte limits and are additionally a lot quicker and extra environment friendly.

The greatest method to get began with Pandas is to take a easy CSV of information (a crawl of your web site, for instance) and save this inside Python as a DataBody.

Once you have got this saved in Python, you possibly can carry out quite a few completely different evaluation duties together with aggregating, pivoting, and cleansing information.

For instance, if I’ve an entire crawl of my web site and need to extract solely these pages which are indexable, I’ll use a built-in Pandas operate to incorporate solely these URLs in my DataBody.

import pandas as pd 
df = pd.read_csv('/Users/rutheverett/Documents/Folder/file_name.csv')
df.head
indexable = df[(df.indexable == True)]
indexable

Requests

The subsequent library is named Requests and is used to make HTTP requests in Python.

Requests makes use of completely different request strategies akin to GET and POST to make a request, with the outcomes being saved in Python.

One instance of this in motion is an easy GET request of URL, this can print out the standing code of a web page:

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

You can then use this consequence to create a decision-making operate, the place a 200 standing code means the web page is obtainable however a 404 means the web page is just not discovered.

if response.status_code == 200:
    print('Success!')
elif response.status_code == 404:
    print('Not Found.')

You can even use completely different requests akin to headers, which show helpful details about the web page just like the content material kind or how lengthy it took to cache the response.

headers = response.headers
print(headers)

response.headers['Content-Type']

There can be the flexibility to simulate a particular person agent, akin to Googlebot, to be able to extract the response this particular bot will see when crawling the web page.

headers = {'User-Agent': 'Mozilla/5.0 (appropriate; Googlebot/2.1; +http://www.google.com/bot.html)'} ua_response = requests.get('https://www.deepcrawl.com/', headers=headers) print(ua_response)

Beautiful Soup

Beautiful Soup is a library used to extract information from HTML and XML information.

Fun reality: The BeautifulSoup library was really named after the poem from Alice’s Adventures in Wonderland by Lewis Carroll.

As a library, BeautifulSoup is used to make sense of internet information and is most frequently used for internet scraping, as it could actually remodel an HTML doc into completely different Python objects.

For instance, you possibly can take a URL and use Beautiful Soup along with the Requests library to extract the title of the web page.

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

Additionally, utilizing the find_all technique, BeautifulSoup lets you extract sure parts from a web page, akin to all a href hyperlinks on the web page:

url="https://www.deepcrawl.com/data/technical-seo-library/" 
req = requests.get(url) 
soup = BeautifulSoup(req.textual content, "html.parser")

for hyperlink in soup.find_all('a'): 
    print(hyperlink.get('href'))

Putting Them Together

These three libraries will also be used collectively, with Requests used to make the HTTP request to the web page we want to use BeautifulSoup to extract info from.

We can then remodel that uncooked information right into a Pandas DataBody to carry out additional evaluation.

URL = 'https://www.deepcrawl.com/weblog/'
req = requests.get(url)
soup = BeautifulSoup(req.textual content, "html.parser")

hyperlinks = soup.find_all('a')

df = pd.DataBody({'hyperlinks':hyperlinks})
df

Matplotlib and Seaborn

Matplotlib and Seaborn are two Python libraries used for creating visualizations.

Matplotlib permits you to create quite a few completely different information visualizations akin to bar charts, line graphs, histograms, and even heatmaps.

For instance, if I needed to take some Google Trends information to show the queries with probably the most recognition over a interval of 30 days, I might create a bar chart in Matplotlib to visualise all of those.

Seaborn, which is constructed upon Matplotlib, supplies much more visualization patterns akin to scatterplots, field plots, and violin plots along with line and bar graphs.

It differs barely from Matplotlib because it makes use of fewer syntax and has built-in default themes.

One means I’ve used Seaborn is to create line graphs to be able to visualize log file hits to sure segments of an internet site over time.

sns.lineplot(x = "month", y = "log_requests_total", hue="class", information=pivot_status)
plt.present()

This specific instance takes information from a pivot desk, which I used to be capable of create in Python utilizing the Pandas library, and is one other means these libraries work collectively to create an easy-to-understand image from the info.

Advertools

Advertools is a library created by Elias Dabbas that can be utilized to assist handle, perceive, and make selections primarily based on the info we have now as SEO professionals and digital entrepreneurs.

Sitemap Analysis

This library permits you to carry out quite a few completely different duties akin to downloading, parsing, and analyzing XML Sitemaps to extract patterns or analyze how typically content material is added or modified.

Robots.txt Analysis

Another attention-grabbing factor you are able to do with this library is to make use of a operate to extract a website’s robots.txt right into a DataBody, to be able to simply perceive and analyze the foundations set.

You can even run a check throughout the library to be able to test whether or not a selected user-agent is ready to fetch sure URLs or folder paths.

URL Analysis

Advertools additionally lets you parse and analyze URLs to be able to extract info and higher perceive analytics, SERP, and crawl information for sure units of URLs.

You can even break up URLs utilizing the library to find out issues such because the HTTP scheme getting used, the principle path, further parameters, and question strings.

Selenium

Selenium is a Python library that’s usually used for automation functions. The most typical use case is testing internet purposes.

One in style instance of Selenium automating a movement is a script that opens a browser and performs quite a few completely different steps in an outlined sequence akin to filling in types or clicking sure buttons.

Selenium employs the identical precept as is used within the Requests library that we lined earlier.

However, it won’t solely ship the request and wait for the response but in addition render the webpage that’s being requested.

To get began with Selenium, you will have a WebDriver to be able to make the interactions with the browser.

Each browser has its personal WebDriver; Chrome has ChromeDriver and Firefox has GeckoDriver, for instance.

These are straightforward to obtain and set-up along with your Python code. Here is a useful article explaining the setup course of, with an instance undertaking.

Scrapy

The remaining library I needed to cowl on this article is Scrapy.

While we will use the Requests module to crawl and extract inside information from a webpage, to be able to move that information and extract helpful insights we additionally want to mix it with BeautifulSoup.

Scrapy primarily permits you to do each of those in a single library.

Scrapy can be significantly quicker and extra highly effective, completes requests to crawl, extracts and parses information in a set sequence, and permits you to protect the info.

Within Scrapy, you possibly can outline quite a few directions such because the title of the area you want to crawl, the beginning URL, and sure web page folders the spider is allowed or not allowed to crawl.

Scrapy can be utilized to extract the entire hyperlinks on a sure web page and retailer them in an output file, for instance.

class SuperSpider(CrawlSpider):
   title="extractor"
   allowed_domains = ['www.deepcrawl.com']
   start_urls = ['https://www.deepcrawl.com/knowledge/technical-seo-library/']
   base_url="https://www.deepcrawl.com"
   def parse(self, response):
       for hyperlink in response.xpath('//div/p/a'):
           yield {
               "hyperlink": self.base_url + hyperlink.xpath('.//@href').get()
           }

You can take this one step additional and comply with the hyperlinks discovered on a webpage to extract info from all of the pages that are being linked to from the beginning URL, type of like a small-scale replication of Google discovering and following hyperlinks on a web page.

from scrapy.spiders import CrawlSpider, Rule
 
 
class SuperSpider(CrawlSpider):
    title="follower"
    allowed_domains = ['en.wikipedia.org']
    start_urls = ['https://en.wikipedia.org/wiki/Web_scraping']
    base_url="https://en.wikipedia.org"
 
    custom_settings = {
        'DEPTH_LIMIT': 1
    }
 
    def parse(self, response):
        for next_page in response.xpath('.//div/p/a'):
            yield response.comply with(next_page, self.parse)
 
        for quote in response.xpath('.//h1/textual content()'):
            yield {'quote': quote.extract() }

Learn extra about these initiatives, amongst different instance initiatives, here.

Final Thoughts

As Hamlet Batista at all times stated, “one of the best ways to study is by doing.”

I hope that discovering a number of the libraries out there has impressed you to get began with studying Python, or to deepen your data.

Python Contributions from the SEO Industry

Hamlet additionally beloved sharing sources and initiatives from these within the Python SEO group. To honor his ardour for encouraging others, I needed to share a number of the superb issues I’ve seen from the group.

As an exquisite tribute to Hamlet and the SEO Python group he helped to domesticate, Charly Wargnier has created SEO Pythonistas to gather contributions of the superb Python initiatives these within the SEO group have created.

Hamlet’s priceless contributions to the SEO Community are featured.

Moshe Ma-yafit created a brilliant cool script for log file analysis, and on this submit explains how the script works. The visualizations it is ready to show together with Google Bot Hits By Device, Daily Hits by Response Code, Response Code % Total, and extra.

Koray Tuğberk GÜBÜR is presently engaged on a Sitemap Health Checker. He additionally hosted a RankSense webinar with Elias Dabbas the place he shared a script that data SERPs and Analyses Algorithms.

It primarily data SERPs with common time variations, and you may crawl all of the touchdown pages, mix information and create some correlations.

John McAlpin wrote an article detailing how you should use Python and Data Studio to spy on your competitors.

JC Chouinard wrote a complete guide to using the Reddit API. With this, you possibly can carry out issues akin to extracting information from Reddit and posting to a Subreddit.

Rob May is engaged on a brand new GSC evaluation device and constructing a couple of new area/actual websites in Wix to measure towards its higher-end WordPress competitor whereas documenting it.

Masaki Okazawa additionally shared a script that analyzes Google Search Console Data with Python.

🎉 Happy #RSTwittorial Thursday with @saksters 🥳

Analyzing Google Search Console Data with #Python 🐍🔥

Here’s the output 👇 pic.twitter.com/9l5Xc6UsmT

— RankSense (@RankSense) February 25, 2021

More Resources:

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Image Credits

All screenshots taken by creator, March 2021