Web Scraping With Python and Requests-HTML

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This post is part of the complete Guide on Python for SEO

In this Python for SEO tutorial, we will learn how to scrape a website with Python using the Requests-HTML library.

(Code example included)

We will extract basic information from a website.

Title
H1
Links
Author
Canonical
Hreflang
Meta Robots Tag
Broken Links

You can find the full code in my Python Notebook.

What is Web Scraping?

Web scraping means the action of parsing the content of a webpage to extract specific information.

Parsing means that you analyze a document to describe the syntax (i.e. the HTML structure). Without a parser, your HTML document will look like a single block of text.

When you are scraping a website, you are asking the server to send you an HTML document that you parse to understand the building blocks (<head>,<body>,<title>,<h1>, etc.). Once the structure is understood, you can pull out any information that you want.

What is Requests-HTML Library?

The requests-HTML library is an HTML parser that lets you use CSS Selectors and XPath Selectors to extract the information that you want from a web page.

Install and load Libraries

In this tutorial, we will use the requests library to “call” the URL by making HTTP requests to servers, the requests-HTML library to parse the data, and the pandas library to work with the scraped information.

pip install requests
pip install requests-HTML
pip install pandas
pip install regex
pip install urlparse4

Call the URL With requests.get()

Use HTMLSession() to initialize the GET requests and the .get() function from requests to call the URL to scrape.

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Here, I will make an example with Hamlet Batista’s amazing intro to Python post.

Just to make sure that there is no error, I will add a try and except statement to return an error in any case the code doesn’t work.

We will store the response in a variable called response.

import requests
from requests_html import HTMLSession

url = "https://www.searchenginejournal.com/introduction-to-python-seo-spreadsheets/342779/"

try:
    session = HTMLSession()
    response = session.get(url)
    
except requests.exceptions.RequestException as e:
    print(e)

Structure of Scraping Functions

The structure of the requests-HTML parsing call goes like this:

variable.attribute.function(*selector*, parameters)

The variable is the instance that you created using the .get(url) function.

The attribute is the type of content that you want to extract (html / lxml).

The requests-HTML parser also has many useful built-in methods for SEOs.

links: Get all links found on a page (anchors included);
absolute_links: Get all links found on a page (anchors excluded);
find(): Find a specific element on a page with a CSS Selector;
xpath(): Get elements using Xpath function;

Extract the Title From the Page

Here, we are going to use find() with the html attribute to “find” the <title> tag using the 'title' CSS Selector and return a list of elements ([<Element 'title' >]).

title =  response.html.find('title')
print(title)
# [<Element 'title' >]

To print to the actual title, we need to use the index with the text attribute.

print(title[0].text)
# An Introduction to Python for SEO Pros Using Spreadsheets

This is the same as using the first parameter in the function in a one-liner.

title =  response.html.find('title', first=True).text
print(title)
# An Introduction to Python for SEO Pros Using Spreadsheets

Extract Meta Description

To extract the meta description from a page, we will use the xpath() function with the //meta[@name="description"]/@content Xpath.

meta_desc =  response.html.xpath('//meta[@name="description"]/@content')
print(meta_desc)
# ["Learn Python basics while studying code John Mueller recently shared that populates Google Sheets. We'll also modify his code to add a simple visualization."]

Extract All Links From a Webpage

The absolute_links function lets us extract all links, excluding anchors on a website.

links = response.html.absolute_links
print(links)

Extract Information Using Class or ID

You can extract any specific information from a page using the dot (.) notation to select a class, or the pound (#) notation to select the ID.

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Here we are going to extract the author’s name using the class.

author = response.html.find('.post-author', first=True).text
print(author)
# Hamlet Batista

Extract Canonical Link

canonical = response.html.xpath("//link[@rel='canonical']/@href")
print(canonical)
# ['https://www.searchenginejournal.com/introduction-to-python-seo-spreadsheets/342779/']

Extract Hreflang

hreflang = response.html.xpath("//link[@rel='alternate']/@hreflang")
print(hreflang)

Extract Meta Robots

meta_robots = response.html.xpath("//meta[@name='ROBOTS']/@content")
print(meta_robots)
# ['NOODP']

Extract Nested Information

To extract information within a specific location you can dig down the DOM using CSS Selectors.

get_nav_links = response.html.find('a.sub-m-cat span')

We will build a for loop to loop through all the indices in the nav_links list and add the text to another list called nav_links.

nav_links = []

for i in range(len(get_nav_links)):
    x = get_nav_links[i].text
    nav_links.append(x)
    
nav_links

# ['SEO', 'PPC', 'CONTENT', 'SOCIAL', 'NEWS', 'ADVERTISE', 'MORE']

Save a Subsection of a Page in a Variable

If the content that you want to extract is always in a specific <div>, you can save the path in a variable to call it.

Here, I will extract links that are in the actual content of a post by “saving” the post-342779 article in a variable called article.

article = response.html.find('article.cis_post_item_initial.post-342779', first=True)
article_links = article.xpath('//a/@href')

Case Study: Extract Broken Links

import re
import requests
from requests_html import HTMLSession
from urllib.parse import urlparse

# Get Domain Name With urlparse
url = "https://www.jobillico.com/fr/partenaires-corporatifs"
parsed_url = urlparse(url)
domain = parsed_url.scheme + "://" + parsed_url.netloc

# Get URL 
session = HTMLSession()
r = session.get(url)

# Extract Links
jlinks = r.html.xpath('//a/@href')

# Remove bad links and replace relative path for absolute path
updated_links = []

for link in jlinks:
    if re.search(".*@.*|.*javascript:.*|.*tel:.*",link):
        link = ""
    elif re.search("^(?!http).*",link):
        link = domain + link
        updated_links.append(link)
    else:
        updated_links.append(link)

Now, it is time to extract broken links and add them to a list.

broken_links = []

for link in updated_links:
    print(link)
    try: 
        requests.get(link, timeout=10).status_code
        if requests.get(link, timeout=10).status_code != 200:
            broken_links.append(link)
    except requests.exceptions.RequestException as e:
        print(e)

broken_links

Automate Your Web Scraping Script

To automate the web scraping, schedule your python script on Windows task scheduler, or automate python script using CRON on Mac.

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Other Technical SEO Guides With Python

This is the end of this Python tutorial on web scraping with the requests-HTML library.

Jean-Christophe Chouinard

Sr SEO Specialist at Seek (Melbourne, Australia). Specialized in technical SEO. In a quest to programmatic SEO for large organizations through the use of Python, R and machine learning.