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.
We can do web scraping with Python since P
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.
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 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 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.
Here we can see all the restaurants fetched.
Here we can see the reviews with their restaurant references.
When you appoint data scraping experts from Mindbowser, we dedicatedly provide end-to-end support to accomplish your organizational objectives quickly.
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:
We can also extract reviews from other review sites.
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