What Exactly Can You Get From Web/Amazon Scraping?

Imagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster.

Why Web Scraping?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping:

1. Price Comparison:

Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products.

2. Email address gathering:

Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails.

3. Social Media Scraping:

Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending.

4. Research and Development:

Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc.) from websites, which are analyzed and used to carry out Surveys or for R&D.

5. Job listings:

Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the user.

Why Python for Web Scraping?

Why Python for Web Scraping?

Product List

You’ve probably heard of how awesome Python is. But, so are other languages too. Then why should we choose Python over other languages for web scraping?

Here is the list of features of Python which makes it more suitable for web scraping.

  1. Ease of Use: Python is simple to code. You do not have to add semicolons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use.
  1. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data.
  1. Dynamically typed: In Python, you don’t have to define data types for variables, you can directly use the variables wherever required. This saves time and makes your job faster.
  1. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. 
  1. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code.
  1. Community: What if you get stuck while writing the code? You don’t have to worry. The Python community has one of the biggest and most active communities, where you can seek help from.

How does Web Scraping work?

List of all the products available
Individual Products (As per the Categorize)
Select the listing field.

When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. 

To extract data using web scraping with python, you need to follow these basic steps:

  1. Find the URL that you want to scrape
  2. Inspecting the Page
  3. Find the data you want to extract
  4. Write the code
  5. Run the code and extract the data
  6. Store the data in the required format 
Now let us see how to extract data from the Amazon website using Python.

Libraries used for Web Scraping

As we know, Python is used for various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries:

  • Selenium:  Selenium is a web testing library. It is used to automate browser activities.
  • BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that are helpful to extract the data easily.
  • Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. 

The main issues of crawling Amazon yourself:

  • Captcha and IP blocks;
  • You need to upgrade the scraper;
  • Different layouts, attributes and features of pages;
  • You should have many VPNs or proxies;
  • You need a database;
  • Legal issues (sometimes your persistent crawling may upset website owners);
  • Structuring data is difficult;
  • Other issues.

Solutions to scraping Amazon Data

If you’re building your own scraper, the solution is to have a lot of money and undying desire to solve any upcoming challenges to complete the process successfully and luckily automate things in the future. However, not everybody has development skills. And definitely not everybody wants to build their own scraping tools, simply because it’s out of their targeted niche and it also requires a lot of manual work first.

Some may only need to do competitors analysis, prices comparison, product sale forecastings, product URLs, reviews and ratings, etc.

Before creating anything yourself, I would recommend searching for already-existing solutions on the web which are APIs.

While there are a lot of comprehensive API guidelines explaining what APIs are and how they work, we will not dig into it today. However, what’s worth mentioning is that Amazon has its own official API which deals with all the above-mentioned issues but does it more effectively and smoothly.

However, it’s proven that some people are not always satisfied with official APIs and have an interest in looking for third-party APIs either because they want a simpler interface or they want to build their own software.

Conclusions

While some developers like scraping for sport or profit, business owners, especially retail and e-commerce businesses need to crawl Amazon to make price comparisons, forecasting product sales, estimating competition rate, etc. Creating your own scraper is a time-consuming, challenging process and is only for the most enthusiastic enthusiasts out there.

On the other hand, if you’re a decision-maker, you may be interested in APIs to hit your goals directly. Usually, APIs are doing pretty much the same work and solve the same issues, however, more effectively. While APIs may or may not always be cheaper than building your own scraper, they can definitely save time and nerves.

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