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There is a clear need for extracting data from the web to conduct various analysis in a timely manner. Most people involved in analytics will find themselves facing situations regularly where they have to extract information also known as data extraction, from the web, at times there a several metrics on which the data set is required from different sites on the web. Especially if you are working for an organisation dealing in a product or service, then web scrapping or data extraction becomes a constant exercise for you to understand the performance, and sentiments attached to your product or services, across several metrics.
Of course, there are many ways to extract information from the web, many organised websites offer API’s which will help you access their data in a more structured format. Hence at any given time if the information that you are seeking is available through API’s it becomes the most preferred approach over web scrapping, however, that is not always the case. As not all websites offer API’s mainly due to ignorance in the technical know-how.
In such a scenarios web scrapping for data extraction becomes the best alternative. Web scraping is a computer technique for extracting information from websites, while transforming the HTML unstructured format into a structured format, like a spreadsheet or database. Web scrapping can be performed in various ways however Python is the most preferred route in programming languages. Mainly because of the ease of use and the rich ecosystem it offers, because it is an open source programming language there are many libraries to perform one function.
One can make a need based decision on which library to use.
‘Requests’, ‘BeautifulSoup’, ‘Lxml’, ‘Selenium’, ‘Scrappy’,’Urllib2’, etc…, are a few option that can be combined based on the type of source and website from where the data needs to be scrapped.
There are some data extraction tips that would assist you in the process
To perform web scrapping one needs to deal with HTML tags, hence you need to have a functional understanding of them. It is always a good thought to check the terms and conditions of the website you wish to extract data from. Your request through your programming should be humane and not aggressive to the level of spamming.
Always get a hold of the unique location of the data, and only then start writing the code.
Why standardising the independent variable is important?
It is always advised to standardise the independent variable. Whenever conducting a comparative analysis of the data set, the distributed clustering usually depends on the type of normalisation procedure. Say if your input variables are combined linearly then it is not very necessary to standardise the inputs.
When standardising the input or the target variable it improves the numerical condition of the optimisation of the problem, and it also ensures that various default values which are involved in initialisation and the termination are appropriate. When standardisation is applied to an independent variable, an attempt is made to give all variables an equal weight in hope of reaching the right objectivity.
There are various web scrapping methods, and even in Python, here is a vast selection of libraries you can use. The fact is, if you need information from various touch points from the web, data scrapping through Python is a great approach for data extraction. There are several tips and procedures that will make your life simple in this process if you are updated.
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