Introduction
Before starting any business either product selling or services provider the one thing that everyone must do is the analysis past and current trends of that product or services like location-wise demand, highest selling month, is demand increasing on certain events, etc.
To do this analysis we need a huge number of related data and we know that the internet is a massive source of data. So, our next step is to collect the data from various websites as per our analysis requirement and where web scraping comes into the picture.
Web scraping is a method used to extract large amounts of data or information from different websites with or without the consent of the website owner. As websites having unstructured data and Web scraping helps collect these unstructured data and store it in a structured form. It can be done manually, but in most cases, it’s done automatically because of its efficiency.
Use cases – Web Scraping
As per the business requirements data are getting collected from the websites but some common examples are listed below for that web scraping are used.
- Price Comparison
- Email address gathering
- Research and Development
- Social Media Scraping
- Job listings
Challenges during Web Scraping
Here one should remember that some websites allow web scraping, and some don’t. To know whether a website allows web scraping or not, we need to check the website’s “robots.txt” file and this can be done by appending “/robots.txt” to the URL that you want to scrape. Just for example to know about amazon.com, the URL is www.amazon.com/robots.txt.
The second challenge is the variety. Every website is different and needs its own personal treatment if you want to extract the information that is relevant to you, means cannot create a general extraction rule for all website.
The third challenge is durability. As many websites are in active development so website structure constantly changes due to this the scraper script executed without any error will not execute properly after website structure change in that case scraper script also need to be modified accordingly.
Another challenge, especially to those new to scraping, would be proxy management. A good practice is a proxy rotation, but still, constant management and upkeep of the infrastructure will be needed, as all issues do not disappear with just rotation. It can be hard to tell which proxy service providers offers the best service, so here are the most popular rotating proxies for web scraping.
Languages for Web scraping
There is a number of languages that can be used for writing web scraping scripts. Just, for example, we have python, Node.js, Ruby, PHP, C/C++ but how well you can do web scarping will depend on the language and the framework that is used.
For a language to be a first choice for writing web scraping scripts must having some pre-defined features listed below.
- Flexibility
- Operational ability to feed database
- Crawling effectiveness
- Ease of coding
- Scalability
- Maintainability
Python is the most popular language for writing web scraping scripts. It is a complete product because it can handle almost all processes related to data extraction smoothly.
In the case of python, small code is required for a large task even syntax is easily understandable mainly because reading a python code is very similar to reading a statement in English. No need to define data types for variables, you can directly use the variables wherever required. The major advantage is python libraries or frameworks that put this language on top of the list.
Python libraries for web scraping
Beautiful Soup and Scrapy are the two most famous and widely used frameworks. Beautiful Soup is designed for effective and high-speed scraping tasks and Scrapy has features like support for XPath, enhanced performance owing to the twisted library, and a variety of debugging tools. Beautiful Soup libraries can convert incoming documents to Unicode and outgoing documents to UTF-8. It works on popular Python parsers like lxml and html5lib, which allow programmers to try different parsing methodologies.
Apart from their two libraries few more which are listed below are required during scraping activity.
Selenium: It is a web testing library and used to automate browser activities.
Pandas: It is used for data manipulation and analysis during data extraction and also to store it in the desired format.
Web scraping steps
Firstly, a request will be sent to the URL from where data need to be scrap. As a response to the request, the server sends the data and allows you to read the related HTML/XML pages. The code then, parses the HTML/XML page, finds the data, and extracts it.
To extract data using web scraping with python, you need to follow these basic steps:
- Identify the data scraping URL.
- Search the correct Page
- Find the data you want to extract
- Write the code
- Run the code and extract the data
- Store the data in the required format
Python script for web scripting
For clear understanding will write the python code to extract the data from the webpage – https://www . roomandboard . com/catalog/bath/vanities
From this website we will try to extract the item name and its listed price in an excel file.
As data is usually nested in different tags in the webpage. So, you need to identify the actual tag under which required data lies. To inspect the page, just right click on the element and click on Inspect as shown below in the image.
Now our aim is to identify the tag where item name and price are defined and as shown in the below image it’s under <div class = “product-group small-6 medium-4 large-4 columns”
Python code written on Jupyter notebook is mention below and the same can be download from the GitHub path.
https://github.com/prakash507979/Web_scraping
After execution will check our output by opening the file Scraping_details.csv
The data stored in this file will show as below.
Conclusion
Hope this article helped you to understand what web scraping is and through an example, we learned how to do web scraping using python which is one of the best languages for web scraping.
The next article will learn how to do web scraping through some other languages.
Cheers !!
