The Swiggy Bangalore restaurants dataset is a structured snapshot of swiggy bangalore restaurants, capturing restaurant identity, locality, cuisine tags, pricing signals, ratings, and delivery-focused attributes in one table. It supports restaurant analytics, recommendation prototypes, and locality-level food trend studies for Bangalore.

In practice, the dataset is used to compare neighborhoods, estimate price bands, profile popular cuisines, and test models that predict rating, delivery time, or cost-for-two. It is also used to build dashboards that summarize restaurant density, delivery readiness, and listing quality across Bengaluru.

This article explains what the dataset contains, where it is sourced, how it is cleaned, and how it supports Bangalore-specific questions in 2026 without overclaiming live accuracy.

TL;DR

• A Bengaluru snapshot usually stores thousands of restaurant outlets with fields such as name, area, cuisine, rating, cost-for-two, and delivery time, making it suitable for EDA and clustering.
• It is reliable for aggregate patterns like locality density and price bands, and weak for live menus, current discounts, and day-to-day availability changes.
• A stable workflow standardizes ratings, converts prices to numeric values, de-duplicates outlets, and normalizes cuisine strings before analysis.

What the Swiggy Bangalore Restaurants Dataset Means

Each row typically represents a single restaurant outlet listing visible in Bangalore, not a brand-level entity. Chains therefore appear many times, which can inflate “top restaurants” counts unless outlets are grouped.

What The Dataset Is Not

The dataset is not an official, continuously updated Swiggy feed. It cannot guarantee that every restaurant in Bangalore is listed, and it cannot guarantee that any specific listing is still active or priced the same today.

Related – Zomato Bangalore Restaurants Dataset

Where The Data Comes From

Many public datasets are exported from listing pages into CSV form. Some versions are restaurant-level for locality analysis, while other versions are dish or menu-item level for pricing studies.

Why Row Counts Vary So Much

A menu-item export can be far larger than a restaurant list because each restaurant can contribute dozens of rows. One India-wide Swiggy export is described as 197,430 rows and 9 columns where each row is a unique menu item, which makes it better for menu pricing than locality density.

Scale Context From Swiggy Disclosures

Official corporate materials describe Swiggy Food as collaborating with over 2.5 lakh restaurant partners across 720+ cities, which shows why “complete coverage” is unrealistic for any single city scrape. A Bangalore dataset should be treated as a slice captured under specific filters and time windows.

Top 20 Swiggy Restaurants in Bangalore 

Restaurant Name	Area	Cuisine	Rating	Cost for two	Delivery time	Veg and Non-Veg	Popular Dishes	Established
Meghana Foods	Koramangala 5th Block	Biryani, Andhra, South Indian, Chinese, Seafood	4.6	₹650	55-65 mins	Non-Veg	Hyderabadi Biryani, Fish Biryani, Chicken Boneless Biryani	2006
Empire Restaurant	Brigade Road	North Indian, Chinese, Mughlai, Seafood	4.6	₹1000	45-55 mins	Non-Veg	Butter Chicken, Pepper Chicken, Ghee Rice, Coin Parotta	1981
Truffles	Koramangala 5th Block	American, Italian, Continental, Desserts, Burgers	4.5	₹900	40-50 mins	Both	All American Burger, Chicken Lasagna, Pink Pasta, Cold Coffee	1995
MTR	Lalbagh Road	South Indian, Breakfast	4.3	₹350	30-40 mins	Veg Only	Masala Dosa, Rava Idli, Kesari Bath, Filter Coffee	1924
Vidyarthi Bhavan	Basavanagudi	South Indian, Breakfast	4.4	₹300	35-45 mins	Veg Only	Masala Dosa, Idli Vada, Coffee	1943
Chinita Real Mexican Food	Indiranagar	Mexican, Fast Food	4.3	₹700	40-50 mins	Both	Burritos, Tacos, Nachos, Quesadillas	2011
Toit Brewpub	Indiranagar	Continental, Italian, Pizza, Craft Beer	4.5	₹1800	Not Available	Both	Wood Fired Pizza, Craft Beers, Pasta	2010
The Hole in the Wall Cafe	Koramangala	Cafe, Continental, Italian	4.4	₹800	45-55 mins	Both	Breakfast Platters, Burgers, Pasta, Coffee	2013
Nagarjuna	Residency Road	Andhra, South Indian, Biryani	4.2	₹600	50-60 mins	Non-Veg	Andhra Meals, Gongura Chicken, Natukodi Biryani	1989
Airlines Hotel	Lavelle Road	South Indian, North Indian, Chinese	4.1	₹500	40-50 mins	Non-Veg	Chicken Ghee Roast, Rava Fry, Mutton Chops	1960s
Shivaji Military Hotel	Jayanagar	South Indian, Non-Veg	4.3	₹400	45-55 mins	Non-Veg	Mutton Curry, Ragi Mudde, Koli Saaru	1950s
Koshy’s	St Marks Road	Multi-cuisine, Continental, Indian	4	₹700	50-60 mins	Both	English Breakfast, Mutton Chops, Pancakes	1940
CTR (Central Tiffin Room)	Malleshwaram	South Indian, Breakfast	4.5	₹300	35-45 mins	Veg Only	Benne Masala Dosa, Khara Bath, Filter Coffee	1920s
Veena Stores	Malleshwaram	South Indian, Breakfast, Snacks	4.3	₹250	30-40 mins	Veg Only	Masala Dosa, Khara Bath, Kesari Bath	1950s
Glen’s Bakehouse	Indiranagar	Bakery, Desserts, Continental	4.6	₹900	40-50 mins	Both	Croissants, Cheesecake, Quiche, Sandwiches	2010
Ebony Restaurant	MG Road	North Indian, Continental, Multi-cuisine	4.2	₹1500	55-65 mins	Both	Tandoori Platter, Dal Makhani, Grilled Chicken	1980s
Brahmins Coffee Bar	Basavanagudi	South Indian, Breakfast	4.4	₹200	25-35 mins	Veg Only	Idli Vada, Ghee Podi Dosa, Filter Coffee	1965
Corner House Ice Cream	Koramangala	Ice Cream, Desserts	4.5	₹400	30-40 mins	Veg Only	Death by Chocolate, Sundaes, Hot Chocolate Fudge	1993
Arbor Brewing Company	JP Nagar	American, Continental, Pizza, Craft Beer	4.4	₹2000	Not Available	Both	Craft Beers, Wood-Fired Pizza, BBQ Wings	2012
Biere Club	Koramangala	Continental, North Indian, Craft Beer	4.3	₹1600	Not Available	Both	Craft Beers, Kebabs, Burgers, Pizza	2012

 

Typical Columns In A Swiggy Bangalore Restaurants Dataset

A common Bengaluru schema includes restaurant name, area or neighborhood, cuisine type, rating, cost-for-two, and delivery time. Many datasets also include an identifier, address string, or listing URL for repeatability.

Swiggy Reviews Dataset Fields

When a dataset is framed as a Swiggy reviews dataset, it usually adds review-count style signals, rating buckets, or derived sentiment labels. Full review text is less common in simple exports because it increases scraping complexity and storage size.

Swiggy Reviews Dataset In Bangalore

A swiggy reviews dataset for Bangalore is usually built from listing-level signals, not from full review text. Common fields include average rating, rating bucket, and a review-count proxy that behaves like votes in ranking systems. These signals help separate high-confidence listings from small-sample outliers.

How To Avoid Review Bias

Review signals can over-represent popular cuisines and high-traffic neighborhoods because dense areas collect more ratings. A stable approach compares within the same locality and price band before comparing across the city. Reporting both average and median rating reduces skew.

Swiggy Bangalore Restaurants Dataset For Locality Pages

Locality pages work best when the dataset is grouped into Bangalore neighborhood clusters, then summarized by cuisine mix, price band share, and delivery-time distribution. This supports queries such as “swiggy bangalore restaurants” plus an area name while keeping the logic explainable.

Data Quality Issues In Real Projects

Ratings can be missing or recorded as placeholders, so they must be mapped to null before averages are computed. Review counts, when present, can also be missing for new listings, which changes reliability rules.

Price And Time Formatting

Cost-for-two can appear as text with currency symbols and commas. Delivery time can be stored as a range rather than a single value, so minutes must be standardized before comparisons.

Duplicate And Outlet Inflation

Duplicates appear when the same outlet is captured multiple times. Outlet inflation appears when chains reuse names across areas, so de-duplication should consider name plus locality or address instead of name alone.

Cleaning Workflow That Stays Stable

Cleaning is most reliable when it is repeatable on every run. A stable pipeline standardizes column names, removes exact duplicates, converts rating and cost to numeric types, normalizes delivery time into minutes, and turns cuisine strings into consistent lists.

Normalizing Multi-Value Cuisines

Cuisine fields should be split by commas into arrays for filtering. For frequency analysis, cuisines can be exploded into one-cuisine-per-row so “Biryani” and “North Indian” are counted consistently.

Swiggy Bangalore Restaurants: Questions The Dataset Answers Well

The dataset supports locality density questions such as which Bangalore areas have the most listed outlets and which zones show higher average cost-for-two. It supports cuisine questions such as which cuisine combinations are common and which neighborhoods skew toward specific categories.

Delivery-Time Segmentation

Delivery-time fields support segmentation by speed bands, which can reveal areas with consistently faster delivery and areas with long-tail delays. These patterns should be treated as distributional signals, not guarantees for any one order.

Number Of Restaurants In Bangalore: What To Say Safely

The keyword Number of restaurants in Bangalore is common, but a single dataset rarely proves the true citywide total. A safer statement is “restaurants in the dataset” and “unique outlets after de-duplication,” then comparing density across neighborhoods.

Why Counts Change Over Time

Counts change because platform availability, onboarding, and delivery radius filters change. A snapshot count is therefore a measurement of captured listings, not a permanent census.

Dataset Variant Table For Choosing The Right Source

Conclusion

The Swiggy Bangalore restaurants dataset is a practical foundation for Bangalore restaurant analytics, especially for locality comparisons, cuisine profiling, delivery-time segmentation, and reproducible recommendation baselines. It is strongest for aggregate pattern analysis and weakest as a current directory.

A clean, well-documented pipeline turns the dataset into a reusable asset for Bangalore-focused projects in 2026, including dashboards, clustering experiments, and ranking prototypes that remain explainable and consistent.