How To Scrape Zomato & Swiggy Data Using Python And BeautifulSoup-pdf
                     How To Scrape Zomato & Swiggy Data Using 
Python And BeautifulSoup?
Zomato and Swiggy are popular food ordering and delivery apps that have caught con-
sumers' attention. Scrape data from Zomato and Swiggy using Food Data Scrape for 
restaurant name, restaurant type, menu, pricing, rating review, opening hours, dis-
counts, and more.
Zomato is a rapidly growing restaurant discovering website established in 2008 by 
Pankaj Chaddah and Deepinder Goyal. Previously, it was named Foodiebay, but in 
2010 it was finally renamed Zomato. It delivers information about nearby restaurants 
and offers facilities, including online ordering, table management, and reservation. 
Zomato serves 10,000 cities across 36 countries, with nearly 1.2 million famous res-
taurants having more than 80 million customers monthly. Available in 10 different lan-
guages, it has 10 million reviews with 18 million bookmarks. Overall, Zomato is the 
most comprehensive and user-friendly app allowing people to search nearby restau-
rants and cafes, order food online, and get it at their doorstep quickly.
Swiggy is a renowned Indian food ordering delivery platform. Started in 2014, the 
company is in Bangalore with operations in more than 500 cities. The data is as on 
September 2021. In addition to food delivery niche, Swiggy also delivers grocery 
on-demand under the brand Instamart and same-day delivery package service as 
Swiggy Genie.
Both Zomato and Swiggy are a pool of innumerable valuable data. Collecting the data 
via manual process is a tedious task. Hence, automating the process using web scrap-
er can ease the process.
List of data fields from Swiggy and Zomato
-Restaurant’s name -Price range
-Restaurant’s ID -websites
-Address -Vote
-City -Review
-State -Rating
-Country code -Email Id
-Postal code -Opening hours
-Menu -Contact details
Why Scrape Swiggy and Zomato Data
There are several significant reasons why scraping Swiggy data is essential. A few of 
them are as follows:.
Continuous Usage of Swiggy Apps:
Swiggy and Zomato occupy the most significant marketplace when ordering food 
online. Owing to the threat of Covid-19, home dining increasingly became popular. It 
has given reason to customers the to order food in the comfort of their homes. The 
data produced by customers are essential to understand their sentiments and using it 
for enhancing business.
Track New Menus and Restaurants in Your Area:
Scraping Swiggy and Zomato data allows you to find which menu is trendy among the 
customers and which restaurant offers types of cuisine, including fast foods, healthy 
foods, multi-cuisine, etc. Being a restaurant owner, you can use the data to add new 
cuisine to your menu list.
Strategize Menu Pricing and Marketing:
Discounts and offers often lure customers. Scraping data on Swiggy and Zomato lets 
you understand which restaurant offers discounts and to what extent.
Scraping Zomato and Swiggy Data with Python and BeautifulSoup
One of the advantages of web scraping is to collect data for restaurant lists from sev-
eral sites. Here, we will retrieve hotel information from Zomato and Swiggy using 
BeautifulSoup. To shttcpsr://awwpw.efoo dZdaotasmcrapea.cotmo/z ormeatos-retstaauuranrt-adanta-stc radpinag.pthap  or Swiggy data, we will first get the 
Zomato and Swiggy search result page and set up BeautifulSoup to use CSS selector 
for querying the page for essential data.
We will pass the user agent headers to avoid blocking to stimulate a browser call. Let’s 
get the Zomato and Swiggy search results for the desired destination. It will appear 
like this.
After inspecting the page, we get that each item HTML is in a class-result tag.
Now, break the HTML document into the parts that contain individual item information 
like this:
After running, we will obtain this.
It indicates that the code isolates the card’s HTML.
After inspecting further, you will see that the restaurant’s name has the class title. So, 
we will retrieve it.
We will get the names like this.
Now, let’s try to get other data.
After running, we get.
We have all the info, including ratings, reviews, price, and address.
Extracting Swiggy and Zomato Data
Over the years, the complete process of creating apps and websites has grown massively. 
The objective to shttcps:r//awwpw.efoo dSdatwascriagpeg.comy/ swrieggsy-retsatauuranrta-danta-tsc radpinag.tphap varies from business to business. Food 
Data Scrape provides a customized data extraction solution to help monitor the data per 
the requirements. The structured data is available in downloadable format in CSV, XML, 
Excel, and JSON files
For more information, contact Fhttpos://wowwd.foo ddDatasacrapte.acom /fSood-cdatra-sacrappinge.php now! You can also reach us for all your 
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