How to Extract Data from Flipkart with Python?

Being amongst the biggest e-tailers, Flipkart has tons of data to scrape for web scrapers. Flipkart is the website where you require to run crawlers to get fine quality data. Let’s see how to scrape Flipkart data using Python.

Steps for Web Scraping Flipkart Python Code

Before starting coding, you need to have Python 3.7, BeautifulSoup library, as well as Atom, a code editor. Once you do the setup, just go ahead to run the codes given below.

It is very important to apprehend the codes so that you could optimize it, use that to iterate many webpages, or change codes to scrape flipkart data and other e-commerce websites. We begin by importing all the required internal as well as external libraries as well as ignoring the SSL certificate errors. Then, Awe accept the URLs from the users, this needs to be the product pages URLs from Flipkart. We have utilized the given URL in a script box given below:

After getting the URL as well as storing that in the variable, we drive an HTTP GET request as well as scrape the HTML content. Then, we read the webpages and convert them into a BeautifulSoup object to traverse webpage content with ease. We have also prettified the HTML content as well as save that to the variable.

Now as we have got the HTML data within the BeautifulSoup object, we would make a dictionary having the name “product_details” where we will save various data points, which we scrape from a webpage. We begin with the initial “script” tag having an attribute “id” getting value “jsonLD”. Within that, we get the JSON value through which we scrape ratings, total reviewers, product-name, brand-name, and images. After that, we choose all “li” tags having an attribute “class” set like “_2-riNZ”. All these tags have certain product highlights, which we scrape by one as well as append to “highlights” attributes in the “product_details”.

The Codes Used to Scrape Data from Flipkart

Pricing is amongst the most vital data-points as well as most e-commerce sites extract competitors’ data mostly for these data points. We make this easily from initial “div” tag within a webpage having an attribute “class” getting value sets as “_1vC4OE _3qQ9m1”. Finally, we capture various descriptions saved in different description headers. All these are scraped in key-value formats. The description-headers are available in the list of “div” tags having an attribute “class” set like “_2THx53”. Different descriptions are saved in the “div” tags having an attribute “class” set like “_1aK10F”.

When data has been scraped as well as saved in a dictionary, which we have made, we would need to save that to the JSON file having a name “product_details.json”. Also, we save the enhanced HTML in the file called “output_file.html”. That is needed so that an HTML could be manually analyzed as well as newer data-points could be found. Different data scraping points in the piece of code were possible using manual analysis of an HTML content previously.

Data We Scraped Using Web Scraping Flipkart

As our code is working fine, let us take a look at data we have scraped. With a product URL provided, this JSON file given below is the result we have got. Let’s analyze that deeply. Amongst the data points, which have solitary values include:

Amongst the other points, we have highlighted that have a listing of key product details. The product descriptions have the list of main value pairs. To get better understanding, just go through the JSON file given below.

Web Scraping Limitations

There are different constraints, which you might face when running the code. Initially, when comes to longsuffering the URLs, if the inputs are invalid URLs or not Flipkart URLs, exceptions are guaranteed to get thrown. All these require to get handled. In contrast, although a valid URL is provided, not all the products might have all data-points, which we have scraped in the code. All the scenarios require to get handled by exception handling.

Conclusion

At Web Screen Scraping, our makes web data scraping an easy solution as well as reduces a huge amount of work in the procedure to requirement submission as well as plugging in scraped data. We know that data scraping as well as injecting data in your business for making data-driven decisions must not be so hard. And this is the reason why our web scraping solutions help companies in taking the digital jump easily.

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