Web Scraping Ecommerce Websites Using Python

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  2. May 25, 2020 Among all these languages, Python is considered as one of the best for Web Scraping because of features like – a rich library, easy to use, dynamically typed, etc. Here are some most commonly used python3 web Scraping libraries.

Price Scraping involves gathering price information of a product from an eCommerce website using web scraping. A price scraper can help you easily scrape prices from website for price monitoring purposes of your competitor and your products.

#selenium #python #automateboring stuffIn this video we will cover below-mentioned topics: The video is a working example of how to create a web scraper for. In this post we are going to scrape an ecommerce website. We'll get into each individual product page and retrieve our information from there. This is the website we are going to scrape – it's an online shop that sells whisky. Do remember to check out the robots.txt file before scraping any website. You have to keep in mind that you can.

How to Scrape Prices

1. Create your own Price Monitoring Tool to Scrape Prices

There are plenty of web scraping tutorials on the internet where you can learn how to create your own price scraper to gather pricing from eCommerce websites. However, writing a new scraper for every different eCommerce site could get very expensive and tedious. Below we demonstrate some advanced techniques to build a basic web scraper that could scrape prices from any eCommerce page.

2. Web Scraping using Price Scraping Tools

Web scraping tools such as ScrapeHero Cloud can help you scrape prices without coding, downloading and learning how to use a tool. ScrapeHero Cloud has pre-built crawlers that can help you scrape popular eCommerce websites such as Amazon, Walmart, Target easily. ScrapeHero Cloud also has scraping APIs to help you scrape prices from Amazon and Walmart in real-time, web scraping APIs can help you get pricing details within seconds.

3. Custom Price Monitoring Solution

ScrapeHero Price Monitoring Solutions are cost-effective and can be built within weeks and in some cases days. Our price monitoring solution can easily be scaled to include multiple websites and/or products within a short span of time. We have considerable experience in handling all the challenges involved in price monitoring and have the sufficient know-how about the essentials of product monitoring.

Scraping

Learn how to scrape prices for FREE –

How to Build a Price Scraper

In this tutorial, we will show you how to build a basic web scraper which will help you in scraping prices from eCommerce websites by taking a few common websites as an example.

Let’s start by taking a look at a few product pages, and identify certain design patterns on how product prices are displayed on the websites.

Amazon.com

Ecommerce

Sephora.com

Observations and Patterns

Some patterns that we identified by looking at these product pages are:

Web Scraping Ecommerce Websites Using Python
  • Price appears as currency figures (never as words)
  • The price is the currency figure with the largest font size
  • Price comes inside first 600 pixels height
  • Usually the price comes above other currency figures

Of course, there could be exceptions to these observations, we’ll discuss how to deal with exceptions later in this article. We can combine these observations to create a fairly effective and generic crawler for scraping prices from eCommerce websites.

Implementation of a generic eCommerce scraper to scrape prices

Step 1: Installation

This tutorial uses the Google Chrome web browser. If you don’t have Google Chrome installed, you can follow the installation instructions.

Instead of Google Chrome, advanced developers can use a programmable version of Google Chrome called Puppeteer. This will remove the necessity of a running GUI application to run the scraper. However, that is beyond the scope of this tutorial.

Step 2: Chrome Developer Tools

Web scraping using python

The code presented in this tutorial is designed for scraping prices as simple as possible. Therefore, it will not be capable of fetching the price from every product page out there.

For now, we’ll visit an Amazon product page or a Sephora product page in Google Chrome.

  • Visit the product page in Google Chrome
  • Right-click anywhere on the page and select ‘Inspect Element’ to open up Chrome DevTools
  • Click on the Console tab of DevTools

Inside the Console tab, you can enter any JavaScript code. The browser will execute the code in the context of the web page that has been loaded. You can learn more about DevTools using their official documentation.

Step 3: Run the JavaScript snippet

Copy the following JavaScript snippet and paste it into the console.

Press ‘Enter’ and you should now be seeing the price of the product displayed on the console.

If you don’t, then you have probably visited a product page which is an exception to our observations. This is completely normal, we’ll discuss how we can expand our script to cover more product pages of these kinds. You could try one of the sample pages provided in step 2.

The animated GIF below shows how we get the price from Amazon.com

Web Scraping Ecommerce Websites Using Python

How it works

First, we have to fetch all the HTML DOM elements in the page.

We need to convert each of these elements to simple JavaScript objects which stores their XY position values, text content and font size, which looks something like {'text':'Tennis Ball', 'fontSize':'14px', 'x':100,'y':200}. So we have to write a function for that, as follows.

Now, convert all the elements collected to JavaScript objects by applying our function on all elements using the JavaScript map function.

Remember the observations we made regarding how a price is displayed. We can now filter just those records which match our design observations. So we need a function that says whether a given record matches with our design observations.

We have used a Regular Expressionto check if a given text is a currency figure or not. You can modify this regular expression in case it doesn’t cover any web pages that you’re experimenting with.

Now we can filter just the records that are possibly price records

Finally, as we’ve observed, the Price comes as the currency figure having the highest font size. If there are multiple currency figures with equally high font size, then Price probably corresponds to the one residing at a higher position. We are going to sort out our records based on these conditions, using the JavaScript sort<em><strong>function.

Now we just need to display it on the console

Taking it further

Moving to a GUI-less based scalable program

You can replace Google Chrome with a headless version of it called Puppeteer. Puppeteer is arguably the fastest option for headless web rendering. It works entirely based on the same ecosystem provided in Google Chrome. Once Puppeteer is set up, you can inject our script programmatically to the headless browser, and have the price returned to a function in your program. To learn more, visit our tutorial on Puppeteer.

Learn more: Web Scraping with Puppeteer and Node.Js

Improving and enhancing this script

You will quickly notice that some product pages will not work with such a script because they don’t follow the assumptions we have made about how the product price is displayed and the patterns we identified.

Unfortunately, there is no “holy grail” or a perfect solution to this problem. It is possible to generalize more web pages and identify more patterns and enhance this scraper.

A few suggestions for enhancements are:

  • Figuring out more features, such as font-weight, font color, etc.
  • Class names or IDs of the elements containing price would probably have the word price. You could figure out such other commonly occurring words.
  • Currency figures with strike-through are probably regular prices, those could be ignored.

There could be pages that follow some of our design observations but violates some others. The snippet provided above strictly filters out elements that violate even one of the observations. In order to deal with this, you can try creating a score basedsystem. This would award points for following certain observations and penalize for violating certain observations. Those elements scoring above a particular threshold could be considered as price.

The next significant step that you would use to handle other pages is to employ Artificial Intelligence/Machine Learning based techniques. You can identify and classify patterns and automate the process to a larger degree this way. However, this field is an evolving field of study and we at ScrapeHero are using such techniques already with varying degrees of success.

If you need help to scrape prices from Amazon.com you can check out our tutorial specifically designed for Amazon.com:

Learn More:How to Scrape Prices from Amazon using Python

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Disclaimer:Any code provided in our tutorials is for illustration and learning purposes only. We are not responsible for how it is used and assume no liability for any detrimental usage of the source code. The mere presence of this code on our site does not imply that we encourage scraping or scrape the websites referenced in the code and accompanying tutorial. The tutorials only help illustrate the technique of programming web scrapers for popular internet websites. We are not obligated to provide any support for the code, however, if you add your questions in the comments section, we may periodically address them.
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Web Scraping From Twitter Using Python

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