Step-by-step Web Scraping Process
Web scraping is about extracting data from websites by parsing its HTML. On some sites, data is available easily to download in CSV or JSON format, but in some cases that’s not possible for that, we need web scraping.
How Does It Work?
How is web scraping done?
We can do web scraping with Python since Python has a bigger developer community it has many libraries that can help us parsing a web page.
Python libraries most commonly used for scraping:
Scrapy is a fast high-level web crawling and web scraping framework used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. It is developed & maintained by Scrapinghub and many other contributors.
Scrapy is best out of the two because in it we have to focus mostly on parsing the webpage HTML structure and not on sending requests and getting HTML content from the response, in Scrapy that part is done by Scrapy we have to only mention the website URL.
A Scrapy project can also be hosted on Scrapinghub, we can set a schedule for when to run a scraper.
Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favourite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.
To scrape a website with Beautiful Soup we also need to use requests library to send requests to the website and get the response and then get HTML content from that response and pass it to Beautiful Soup object for parsing.
Selenium Python bindings provide a simple API to write functional/acceptance tests using Selenium WebDriver. Through Selenium Python API you can access all functionalities of Selenium WebDriver in an intuitive way.
Selenium is used to scrape websites which load content dynamically like Facebook, Twitter, etc. or if we have to perform a click or scroll page action to login or signup to get to the page that has to be scrapped.
Selenium can be used with Scrapy and Beautiful Soup after the site has loaded the dynamically generated content we can get access to the HTML of that site through selenium and pass it to Scrapy or beautiful soup and perform the same operations.
Step-by-Step Data Scraping Example
For this example, we will be scraping Yelp for restaurant reviews in San Francisco, California with Scrapy.
Step 1 => Since we are only fetching restaurant reviews in San Francisco, scraping URL will redirect us to the page below.
Step 2 => We will now create a Scrapy project with the command below
Scrapy startproject restaurant_reviews Scrapy project structure
Step 3 => Now we will create 2 items(Restaurant and Review) in items.py to store and output the extracted data in a structured format.
Step 4 => Now we will create a custom pipeline, in Scrapy to output data in 2 separate CSV files(Restaurants.csv & Reviews.csv). After creating the custom pipeline we will add it in ITEM_PIPELINES of Scrapy settings.py file.
Step 5 => Now we will inspect the Yelp page we are going to scrape and find the and will find the URL for each restaurant’s review page from where we will fetch the reviews. In the below image, we can see that all the search results are in <li> tags with the same CSS classes. In the same manner, we will inspect the review pages of some restaurants to understand their structure.
Step 6 => Now we will create a scraper to fetch the information.
Here we can see all the restaurants fetched.
Here we can see the reviews with their restaurant references.
Why Mindbowser for Web Scraping?
When you appoint data scraping experts from Mindbowser, we dedicatedly provide end-to-end support to accomplish your organizational objectives quickly.
Mindbowser has been delivering high-quality web scraping services to all size businesses across the world for more than 10 years. At Mindbowser, you will receive comprehensive support from our web data scraping experts, who have immense knowledge in the latest website scraping tools, technologies, and methodologies.
The above example shows us how with the help of some tools, we can extract information from a website for a number of purposes. It only shows a basic use case of Scrapy, it can do a lot more.
We can do a lot of things with the output of the above example like:
- Topic Modelling: It can help us get in-depth information about the topics the review is about.
- Sentiment Analysis: It can help us get sentiments of each review for more in-depth analysis.
We can also extract reviews from other review sites.
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Adit is a full stack developer with around 3 years of experience. He is an expert in web scrapping and natural language processing. He loves to solve technical issues and learn new technologies by helping others.