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Wednesday, 21 September 2016

Things to take care while doing Web Scraping!!!

Things to take care while doing Web Scraping!!!

In the present day and age, web scraping word becomes most popular in data science. Basically web scraping is extracting the information from the websites using pre-written programs and web scraping scripts. Many organizations have successfully used web site scraping to build relevant and useful database that they use on a daily basis to enhance their business interests. This is the age of the Big Data and web scraping is one of the trending techniques in the data science.

Throughout my journey of learning web scraping and implementing many successful scraping projects, I have come across some great experiences we can learn from.  In this post, I’m going to discuss some of the approaches to take and approaches to avoid while executing web scraping.

User Proxies: Anonymously scraping data from websites

One should not scrape website with a single IP Address. Because when you repeatedly request the web page for web scraping, there is a chance that the remote web server might block your IP address preventing further request to the web page. To overcome this situation, one should scrape websites with the help of proxy servers (anonymous scraping). This will minimize the risk of getting trapped and blacklisted by a website. Use of Proxies to hide your identity (network details) to remote web servers while scraping data. You may also use a VPN instead of proxies to anonymously scrape websites.

Take maximum data and store it.

Do not follow “process the web page as it comes from the remote server”. Instead take all the information and store it to disk. This approach will be useful when your scraping algorithm breaks in the middle. In this case you don’t have to start scraping again. Never download the same content more than once as you are just wasting bandwidth. Try and download all content to disk in one go and then do the processing.

Follow strict rules in parsing:

Check various rules while parsing the information from the web site. For example if you expect a value to be a date then check that it’s really a date. This may greatly improve the quality of information. When you get unexpected data, then the algorithm need to be changed accordingly.

Respect Robots.txt

Robots.txt specifies the set of rules that should be followed by web crawlers and robots. I strongly advise you to consider and adjust your crawler to fully respect robots.txt. Robots.txt contains instructions on the exact pages that you are allowed to crawl, user-agent, and the requisite intervals between page requests. Following to these instructions minimizes the chance of getting blacklisted and banned from website owner.

Use XPath Smartly

XPath is a nice option to select elements of the HTML document more flexibly than CSS Selectors.  Be careful about HTML structure change through page to page so one xpath you made may be failed to extract data on another page due to changes in HTML structure.

Obey Website TOC:

Some websites make it absolutely apparent in their terms and conditions that they are particularly against to web scraping activities on their content. This can make you vulnerable against possible ethical and legal implications.

Test sample scrape and verify the data with actual scrape

Once you are done with web scraping project set up, you need to test it for sometimes. Check the extracted data. If something is not good, find out the cause and make changes accordingly and finally come to a perfect web scraping project.

Source: http://webdata-scraping.com/things-take-care-web-scraping/

Sunday, 11 September 2016

Benefits of Ruby over Python & R for Web Scraping

Benefits of Ruby over Python & R for Web Scraping

In this data driven world, you need to be constantly vigilant, as information and key data for an organization keeps changing all the while. If you get the right data at the right time in an efficient manner, you can stay ahead of competition. Hence, web scraping is an essential way of getting the right data. This data is crucial for many organizations, and scraping technique will help them keep an eye on the data and get the information that will benefit them further.

Web scraping involves both crawling the web for data and extracting the data from the page. There are several languages which programmers prefer for web scraping, the top ones are Ruby, Python & R. Each language has its own pros and cons over the other, but if you want the best results and a smooth flow, Ruby is what you should be looking for.

Ruby is very good at production deployments and using Ruby, Redis & Chef have proven to be a great combination. String manipulation in Ruby is very easy because it is based on Perl syntax. Also, Ruby is great for analyzing web pages using  one of the very powerful gems called Nokogiri. Nokogiri is much easier to use as compared to other packages and libraries used by R and Python respectively. Nokogiri can deal with broken HTML / HTML fragments easily. Ruby also has many extensions, such as Sanitize and Loofah, that can help clean up broken HTML.

Python programmers widely use a library called Beautiful Soup for pulling data out of HTML & XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. R programmers have a new package called rvest that makes it easy to scrape data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces.

To help you understand it more effectively, below is a comprehensive infographic for the same.

Ruby is far ahead of Python & R for cloud development and deployments.  The Ruby Bundler system is just great for managing and deploying packages from Github. Using Chef, you can start up and tear down nodes on EC2, at will, and monitor for failures,  scale up or down, reset your IP addresses, etc. Ruby also has great testing frameworks like Fakeweb and Capybara, making it almost trivial to build a great suite of unit tests and to include advanced features, like crawling  and scraping using webkit / selenium. 

The only disadvantage to Ruby is lack of machine learning and NLP toolkits, making it much harder to emulate the capacity of a tool like Pattern.  It can still be done, however, since most of the heavy lifting can be done asynchronously using Unix tools like liblinear or vowpal wabbit.


Each language has its plus point and you can pick the one which you are most comfortable with. But if you are looking for smooth web scraping experience, then Ruby is the best option. That has been our choice too for years at PromptCloud for the best web scraping results. If you have any further questions about this, then feel free to get in touch with us.

Source: https://www.promptcloud.com/blog/benefits-of-ruby-for-web-scraping