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How-to-Extract-Flipkart-Products-Data-Using-BeautifulSoup-and-Python

In this blog, we will see how to extract Flipkart product data using BeautifulSoup and Python in an easy and sophisticated manner.

This blog aims to do real-world problem-solving while keeping that very simple so that you become familiar with and have practical results quickly.

After that, install BeautifulSoup using:

List-of-Data-Fields

Also, we will need lxml, library requests, and soupsieve to get data, split it down into XML, and apply CSS selectors. Then, install those.

List-of-Data-Fields

When get installed, open the editor and type:

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Let's go through Flipkart listing page to inspect the data we get.

That’s how it will look:

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Coming back to code, let's get data by imagining that we have a browser like this:

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Save that as scrapeFlipkart.py.

In case you run that:

Example-Result-of-Web-Scraped-Flipkart-Products-Data

You would see the entire HTML page.

Let's utilize CSS selectors to get the desired data. To do it, let's come back to Chrome and open it inspect tool.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

We observe that all individual product data are controlled with an attribute data-id. You also follow that the attribute's value is nonsense and keeps changing. So, we can't use that. However, the evidence is the occurrence of the data-id attribute. So let's scrape it.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

It prints all content in all containers which hold product data.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Let’s get back to work in all the desired fields. It is challenging as Flipkart HTML doesn’t have any meaningful CSS classes to use. Therefore, we would resort that to a few tricks, which might be dependable.

For title, we have noticed that the initial anchor tag comes with an image within it that always has a title in the alt attribute. Therefore, let's get it.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

The subsequent line above provides us a URL to listing.

The product ratings have a meaningful id productRating trailed by some nonsense. However, we can utilize the *= operator for selecting anything that has a word called productRating:

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Extracting the price data is more challenging as this has no visible class ID or name like a clue of getting to it. However, it always provides a currency denominator having ₹ in that. Therefore, we utilize regex to discover it.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

Here, we do same to have a discount percentage. This always has a word off in that.

Example-Result-of-Web-Scraped-linkedIn-profile-data

Putting that together.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

In case you run that, it would print all the information.

Example-Result-of-Web-Scraped-Flipkart-Products-Data

And Kudos!! We have them all. This was challenging yet satisfying.

If you need to use that in production or wish to measure thousands of links, you will get that you will have your IP blocked effortlessly by Flipkart. With this condition, using rotating proxy services to rotate different IPs is essential. You can utilize services like Proxies APIs to send calls through the pool of millions of proxies.

In case you need to scale up crawling speed and you don’t want to have the infrastructure; you can utilize our data crawler to easily extract thousands of URLs with higher speed from network of crawlers.

For more information about Flipkart product data scraping, contact us now! We also provide mobile app scraping and web scraping services at a reasonable price!

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