{"id":4062,"date":"2026-02-13T13:40:35","date_gmt":"2026-02-13T13:40:35","guid":{"rendered":"https:\/\/fooddy.in\/blog\/?p=4062"},"modified":"2026-02-17T13:47:55","modified_gmt":"2026-02-17T13:47:55","slug":"swiggy-bangalore-restaurants-dataset","status":"publish","type":"post","link":"https:\/\/fooddy.in\/blog\/swiggy-bangalore-restaurants-dataset\/","title":{"rendered":"Swiggy Bangalore Restaurants Dataset | Structure, Stats &#038; Insights for 2026"},"content":{"rendered":"\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h4 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h4>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>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.<\/li>\r\n\r\n\r\n\r\n<li>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.<\/li>\r\n\r\n\r\n\r\n<li>A stable workflow standardizes ratings, converts prices to numeric values, de-duplicates outlets, and normalizes cuisine strings before analysis.<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<p><style>\r\n  .stat-grid {\r\n    display: grid;\r\n    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));\r\n    gap: 22px;\r\n    margin: 32px 0;\r\n  }\r\n  .stat-box {\r\n    border: 1px solid #eee;\r\n    border-radius: 16px;\r\n    padding: 22px 24px;\r\n    background: #ffffff;\r\n  }\r\n  .stat-heading {\r\n    font-size: 15px;\r\n    font-weight: 700;\r\n    color: #111;\r\n    margin-bottom: 14px;\r\n  }\r\n  .stat-main {\r\n    display: flex;\r\n    align-items: baseline;\r\n    gap: 10px;\r\n    margin-bottom: 12px;\r\n  }\r\n  .stat-value {\r\n    font-size: 44px;\r\n    font-weight: 800;\r\n    color: #ff711e;\r\n    animation: blinkMove 1.6s infinite ease-in-out;\r\n    line-height: 1;\r\n    white-space: nowrap;\r\n  }\r\n  .stat-unit {\r\n    font-size: 15px;\r\n    font-weight: 700;\r\n    color: #111;\r\n  }\r\n  .stat-text {\r\n    font-size: 14px;\r\n    color: #444;\r\n    font-weight: 600;\r\n    line-height: 1.55;\r\n    margin-bottom: 10px;\r\n  }\r\n  .stat-source {\r\n    font-size: 13px;\r\n    font-weight: 600;\r\n    color: #666;\r\n  }\r\n  .stat-source a {\r\n    color: #ff711e;\r\n    font-weight: 700;\r\n    text-decoration: none;\r\n    word-break: break-word;\r\n  }\r\n  .stat-source a:hover {\r\n    text-decoration: underline;\r\n  }\r\n  @keyframes blinkMove {\r\n    0%   { opacity: 0; transform: translateX(12px) scale(0.95); }\r\n    25%  { opacity: 1; transform: translateX(0) scale(1); }\r\n    75%  { opacity: 1; transform: translateX(0) scale(1); }\r\n    100% { opacity: 0; transform: translateX(-12px) scale(0.95); }\r\n  }\r\n  @media (prefers-reduced-motion: reduce) {\r\n    .stat-value { animation: none; }\r\n  }\r\n<\/style><\/p>\r\n<div class=\"stat-grid\"><!-- CARD 1 -->\r\n<div class=\"stat-box\">\r\n<div class=\"stat-heading\">Swiggy Restaurant Dataset (Restaurant-Level)<\/div>\r\n<div class=\"stat-main\">\r\n<div class=\"stat-value\">8,680<\/div>\r\n<div class=\"stat-unit\">Rows<\/div>\r\n<\/div>\r\n<div class=\"stat-text\">A structured Swiggy restaurant dataset sample contains 8,680 restaurant listings with attributes such as rating, cost-for-two, cuisine, and delivery time.<\/div>\r\n<div class=\"stat-source\">Source: <a href=\"https:\/\/www.gigasheet.com\/sample-data\/swiggy-restuarants-dataset\" target=\"_blank\" rel=\"nofollow noopener\">Gigasheet Dataset<\/a><\/div>\r\n<\/div>\r\n<!-- CARD 2 -->\r\n<div class=\"stat-box\">\r\n<div class=\"stat-heading\">India-Wide Swiggy Menu Dataset<\/div>\r\n<div class=\"stat-main\">\r\n<div class=\"stat-value\">197,430<\/div>\r\n<div class=\"stat-unit\">Menu Items<\/div>\r\n<\/div>\r\n<div class=\"stat-text\">The Swiggy Restaurant Data (India) dataset includes 197,430 rows representing unique menu items across restaurants.<\/div>\r\n<div class=\"stat-source\">Source: <a href=\"https:\/\/www.kaggle.com\/datasets\/nikhilmaurya1324\/swiggy-restaurant-data-india\" target=\"_blank\" rel=\"nofollow noopener\">Kaggle<\/a><\/div>\r\n<\/div>\r\n<!-- CARD 3 -->\r\n<div class=\"stat-box\">\r\n<div class=\"stat-heading\">Bangalore Swiggy Sample Dataset<\/div>\r\n<div class=\"stat-main\">\r\n<div class=\"stat-value\">118<\/div>\r\n<div class=\"stat-unit\">Outlets<\/div>\r\n<\/div>\r\n<div class=\"stat-text\">An interactive Swiggy Bangalore dataset analysis included 118 restaurant outlets for locality-based visualization and insights.<\/div>\r\n<div class=\"stat-source\">Source: <a href=\"https:\/\/medium.com\/@triveni.mishra\/an-interactive-analysis-of-swiggy-bangalore-data-d9c292947a4c\" target=\"_blank\" rel=\"nofollow noopener\">Medium Analysis<\/a><\/div>\r\n<\/div>\r\n<!-- CARD 4 -->\r\n<div class=\"stat-box\">\r\n<div class=\"stat-heading\">Swiggy Restaurant Network Scale<\/div>\r\n<div class=\"stat-main\">\r\n<div class=\"stat-value\">2.7<\/div>\r\n<div class=\"stat-unit\">Lakh+ Restaurants<\/div>\r\n<\/div>\r\n<div class=\"stat-text\">Swiggy Food collaborates with over 2.7 lakh restaurant partners across 720+ cities in India, reflecting its large platform scale.<\/div>\r\n<div class=\"stat-source\">Source: <a href=\"https:\/\/www.swiggy.com\/corporate\/wp-content\/uploads\/2026\/01\/Press-release_Q3FY26.pdf\" target=\"_blank\" rel=\"nofollow noopener\">Swiggy Corporate Press Release<\/a><br \/><br \/><\/div>\r\n<\/div>\r\n<\/div>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>What the Swiggy Bangalore Restaurants Dataset Means<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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 \u201ctop restaurants\u201d counts unless outlets are grouped.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>What The Dataset Is Not<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<p><strong>Related &#8211; <a href=\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/\">Zomato Bangalore Restaurants Dataset<\/a><\/strong><\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Where The Data Comes From<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Why Row Counts Vary So Much<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Scale Context From Swiggy Disclosures<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Official corporate materials describe Swiggy Food as collaborating with over <strong>2.5 lakh restaurant partners across 720+ cities,<\/strong> which shows why \u201ccomplete coverage\u201d is unrealistic for any single city scrape. A Bangalore dataset should be treated as a slice captured under specific filters and time windows.<\/p>\r\n<h2><strong>Top 20 Swiggy Restaurants in Bangalore\u00a0<br \/><br \/><\/strong><\/h2>\r\n<table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\" data-sheets-baot=\"1\"><colgroup><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><col width=\"100\" \/><\/colgroup>\r\n<tbody>\r\n<tr>\r\n<td><strong>Restaurant Name<\/strong><\/td>\r\n<td><strong>Area<\/strong><\/td>\r\n<td><strong>Cuisine<\/strong><\/td>\r\n<td><strong>Rating<\/strong><\/td>\r\n<td><strong>Cost for two<\/strong><\/td>\r\n<td><strong>Delivery time<\/strong><\/td>\r\n<td><strong>Veg and Non-Veg<\/strong><\/td>\r\n<td><strong>Popular Dishes<\/strong><\/td>\r\n<td><strong>Established<\/strong><\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Meghana Foods<\/strong><\/td>\r\n<td>Koramangala 5th Block<\/td>\r\n<td>Biryani, Andhra, South Indian, Chinese, Seafood<\/td>\r\n<td>4.6<\/td>\r\n<td>\u20b9650<\/td>\r\n<td>55-65 mins<\/td>\r\n<td>Non-Veg<\/td>\r\n<td>Hyderabadi Biryani, Fish Biryani, Chicken Boneless Biryani<\/td>\r\n<td>2006<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Empire Restaurant<\/strong><\/td>\r\n<td>Brigade Road<\/td>\r\n<td>North Indian, Chinese, Mughlai, Seafood<\/td>\r\n<td>4.6<\/td>\r\n<td>\u20b91000<\/td>\r\n<td>45-55 mins<\/td>\r\n<td>Non-Veg<\/td>\r\n<td>Butter Chicken, Pepper Chicken, Ghee Rice, Coin Parotta<\/td>\r\n<td>1981<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Truffles<\/strong><\/td>\r\n<td>Koramangala 5th Block<\/td>\r\n<td>American, Italian, Continental, Desserts, Burgers<\/td>\r\n<td>4.5<\/td>\r\n<td>\u20b9900<\/td>\r\n<td>40-50 mins<\/td>\r\n<td>Both<\/td>\r\n<td>All American Burger, Chicken Lasagna, Pink Pasta, Cold Coffee<\/td>\r\n<td>1995<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>MTR<\/strong><\/td>\r\n<td>Lalbagh Road<\/td>\r\n<td>South Indian, Breakfast<\/td>\r\n<td>4.3<\/td>\r\n<td>\u20b9350<\/td>\r\n<td>30-40 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Masala Dosa, Rava Idli, Kesari Bath, Filter Coffee<\/td>\r\n<td>1924<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Vidyarthi Bhavan<\/strong><\/td>\r\n<td>Basavanagudi<\/td>\r\n<td>South Indian, Breakfast<\/td>\r\n<td>4.4<\/td>\r\n<td>\u20b9300<\/td>\r\n<td>35-45 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Masala Dosa, Idli Vada, Coffee<\/td>\r\n<td>1943<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Chinita Real Mexican Food<\/strong><\/td>\r\n<td>Indiranagar<\/td>\r\n<td>Mexican, Fast Food<\/td>\r\n<td>4.3<\/td>\r\n<td>\u20b9700<\/td>\r\n<td>40-50 mins<\/td>\r\n<td>Both<\/td>\r\n<td>Burritos, Tacos, Nachos, Quesadillas<\/td>\r\n<td>2011<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Toit Brewpub<\/strong><\/td>\r\n<td>Indiranagar<\/td>\r\n<td>Continental, Italian, Pizza, Craft Beer<\/td>\r\n<td>4.5<\/td>\r\n<td>\u20b91800<\/td>\r\n<td>Not Available<\/td>\r\n<td>Both<\/td>\r\n<td>Wood Fired Pizza, Craft Beers, Pasta<\/td>\r\n<td>2010<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>The Hole in the Wall Cafe<\/strong><\/td>\r\n<td>Koramangala<\/td>\r\n<td>Cafe, Continental, Italian<\/td>\r\n<td>4.4<\/td>\r\n<td>\u20b9800<\/td>\r\n<td>45-55 mins<\/td>\r\n<td>Both<\/td>\r\n<td>Breakfast Platters, Burgers, Pasta, Coffee<\/td>\r\n<td>2013<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Nagarjuna<\/strong><\/td>\r\n<td>Residency Road<\/td>\r\n<td>Andhra, South Indian, Biryani<\/td>\r\n<td>4.2<\/td>\r\n<td>\u20b9600<\/td>\r\n<td>50-60 mins<\/td>\r\n<td>Non-Veg<\/td>\r\n<td>Andhra Meals, Gongura Chicken, Natukodi Biryani<\/td>\r\n<td>1989<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Airlines Hotel<\/strong><\/td>\r\n<td>Lavelle Road<\/td>\r\n<td>South Indian, North Indian, Chinese<\/td>\r\n<td>4.1<\/td>\r\n<td>\u20b9500<\/td>\r\n<td>40-50 mins<\/td>\r\n<td>Non-Veg<\/td>\r\n<td>Chicken Ghee Roast, Rava Fry, Mutton Chops<\/td>\r\n<td>1960s<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Shivaji Military Hotel<\/strong><\/td>\r\n<td>Jayanagar<\/td>\r\n<td>South Indian, Non-Veg<\/td>\r\n<td>4.3<\/td>\r\n<td>\u20b9400<\/td>\r\n<td>45-55 mins<\/td>\r\n<td>Non-Veg<\/td>\r\n<td>Mutton Curry, Ragi Mudde, Koli Saaru<\/td>\r\n<td>1950s<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Koshy&#8217;s<\/strong><\/td>\r\n<td>St Marks Road<\/td>\r\n<td>Multi-cuisine, Continental, Indian<\/td>\r\n<td>4<\/td>\r\n<td>\u20b9700<\/td>\r\n<td>50-60 mins<\/td>\r\n<td>Both<\/td>\r\n<td>English Breakfast, Mutton Chops, Pancakes<\/td>\r\n<td>1940<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>CTR (Central Tiffin Room)<\/strong><\/td>\r\n<td>Malleshwaram<\/td>\r\n<td>South Indian, Breakfast<\/td>\r\n<td>4.5<\/td>\r\n<td>\u20b9300<\/td>\r\n<td>35-45 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Benne Masala Dosa, Khara Bath, Filter Coffee<\/td>\r\n<td>1920s<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Veena Stores<\/strong><\/td>\r\n<td>Malleshwaram<\/td>\r\n<td>South Indian, Breakfast, Snacks<\/td>\r\n<td>4.3<\/td>\r\n<td>\u20b9250<\/td>\r\n<td>30-40 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Masala Dosa, Khara Bath, Kesari Bath<\/td>\r\n<td>1950s<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Glen&#8217;s Bakehouse<\/strong><\/td>\r\n<td>Indiranagar<\/td>\r\n<td>Bakery, Desserts, Continental<\/td>\r\n<td>4.6<\/td>\r\n<td>\u20b9900<\/td>\r\n<td>40-50 mins<\/td>\r\n<td>Both<\/td>\r\n<td>Croissants, Cheesecake, Quiche, Sandwiches<\/td>\r\n<td>2010<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Ebony Restaurant<\/strong><\/td>\r\n<td>MG Road<\/td>\r\n<td>North Indian, Continental, Multi-cuisine<\/td>\r\n<td>4.2<\/td>\r\n<td>\u20b91500<\/td>\r\n<td>55-65 mins<\/td>\r\n<td>Both<\/td>\r\n<td>Tandoori Platter, Dal Makhani, Grilled Chicken<\/td>\r\n<td>1980s<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Brahmins Coffee Bar<\/strong><\/td>\r\n<td>Basavanagudi<\/td>\r\n<td>South Indian, Breakfast<\/td>\r\n<td>4.4<\/td>\r\n<td>\u20b9200<\/td>\r\n<td>25-35 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Idli Vada, Ghee Podi Dosa, Filter Coffee<\/td>\r\n<td>1965<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Corner House Ice Cream<\/strong><\/td>\r\n<td>Koramangala<\/td>\r\n<td>Ice Cream, Desserts<\/td>\r\n<td>4.5<\/td>\r\n<td>\u20b9400<\/td>\r\n<td>30-40 mins<\/td>\r\n<td>Veg Only<\/td>\r\n<td>Death by Chocolate, Sundaes, Hot Chocolate Fudge<\/td>\r\n<td>1993<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Arbor Brewing Company<\/strong><\/td>\r\n<td>JP Nagar<\/td>\r\n<td>American, Continental, Pizza, Craft Beer<\/td>\r\n<td>4.4<\/td>\r\n<td>\u20b92000<\/td>\r\n<td>Not Available<\/td>\r\n<td>Both<\/td>\r\n<td>Craft Beers, Wood-Fired Pizza, BBQ Wings<\/td>\r\n<td>2012<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Biere Club<\/strong><\/td>\r\n<td>Koramangala<\/td>\r\n<td>Continental, North Indian, Craft Beer<\/td>\r\n<td>4.3<\/td>\r\n<td>\u20b91600<\/td>\r\n<td>Not Available<\/td>\r\n<td>Both<\/td>\r\n<td>Craft Beers, Kebabs, Burgers, Pizza<\/td>\r\n<td>2012<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p>&nbsp;<\/p>\r\n<h2 class=\"wp-block-heading\"><strong>Typical Columns In A Swiggy Bangalore Restaurants Dataset<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Swiggy Reviews Dataset Fields<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Swiggy Reviews Dataset In Bangalore<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>How To Avoid Review Bias<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Swiggy Bangalore Restaurants Dataset For Locality Pages<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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 \u201cswiggy bangalore restaurants\u201d plus an area name while keeping the logic explainable.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Data Quality Issues In Real Projects<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Price And Time Formatting<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Duplicate And Outlet Inflation<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Cleaning Workflow That Stays Stable<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Normalizing Multi-Value Cuisines<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Cuisine fields should be split by commas into arrays for filtering. For frequency analysis, cuisines can be exploded into one-cuisine-per-row so \u201cBiryani\u201d and \u201cNorth Indian\u201d are counted consistently.<\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Swiggy Bangalore Restaurants: Questions The Dataset Answers Well<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Delivery-Time Segmentation<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Number Of Restaurants In Bangalore: What To Say Safely<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>The keyword Number of restaurants in Bangalore is common, but a single dataset rarely proves the true citywide total. A safer statement is \u201crestaurants in the dataset\u201d and \u201cunique outlets after de-duplication,\u201d then comparing density across neighborhoods.<\/p>\r\n\r\n\r\n\r\n<h3 class=\"wp-block-heading\"><strong>Why Counts Change Over Time<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Dataset Variant Table For Choosing The Right Source<br \/><br \/><\/strong><\/h2>\r\n\r\n\r\n\r\n<div class=\"TyagGW_tableContainer\">\r\n<div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\">\r\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"8348\" data-end=\"8730\">\r\n<thead data-start=\"8348\" data-end=\"8413\">\r\n<tr data-start=\"8348\" data-end=\"8413\">\r\n<th class=\"\" data-start=\"8348\" data-end=\"8366\" data-col-size=\"sm\">Dataset Variant<\/th>\r\n<th class=\"\" data-start=\"8366\" data-end=\"8381\" data-col-size=\"sm\">Typical Unit<\/th>\r\n<th class=\"\" data-start=\"8381\" data-end=\"8392\" data-col-size=\"sm\">Best Use<\/th>\r\n<th class=\"\" data-start=\"8392\" data-end=\"8413\" data-col-size=\"sm\">Common Limitation<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody data-start=\"8432\" data-end=\"8730\">\r\n<tr data-start=\"8432\" data-end=\"8543\">\r\n<td data-start=\"8432\" data-end=\"8464\" data-col-size=\"sm\">Bengaluru restaurant snapshot<\/td>\r\n<td data-start=\"8464\" data-end=\"8484\" data-col-size=\"sm\">Restaurant outlet<\/td>\r\n<td data-start=\"8484\" data-end=\"8511\" data-col-size=\"sm\">Locality and cuisine EDA<\/td>\r\n<td data-start=\"8511\" data-end=\"8543\" data-col-size=\"sm\">Not real-time; sampling bias<\/td>\r\n<\/tr>\r\n<tr data-start=\"8544\" data-end=\"8635\">\r\n<td data-start=\"8544\" data-end=\"8574\" data-col-size=\"sm\">India-wide menu-item export<\/td>\r\n<td data-start=\"8574\" data-end=\"8586\" data-col-size=\"sm\">Menu item<\/td>\r\n<td data-start=\"8586\" data-end=\"8610\" data-col-size=\"sm\">Menu pricing analysis<\/td>\r\n<td data-start=\"8610\" data-end=\"8635\" data-col-size=\"sm\">Weaker locality focus<\/td>\r\n<\/tr>\r\n<tr data-start=\"8636\" data-end=\"8730\">\r\n<td data-start=\"8636\" data-end=\"8667\" data-col-size=\"sm\">Delivery-time focused scrape<\/td>\r\n<td data-start=\"8667\" data-end=\"8687\" data-col-size=\"sm\">Restaurant outlet<\/td>\r\n<td data-start=\"8687\" data-end=\"8708\" data-col-size=\"sm\">Speed segmentation<\/td>\r\n<td data-start=\"8708\" data-end=\"8730\" data-col-size=\"sm\">Higher missingness<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p>&nbsp;<\/p>\r\n<\/div>\r\n<\/div>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":4065,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[25],"tags":[],"class_list":["post-4062","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-restaurants-statistics"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Swiggy Bangalore Restaurants Dataset \u2013 Stats &amp; Schema<\/title>\n<meta name=\"description\" content=\"Explore the Swiggy Bangalore restaurants dataset with structure, review signals, sales proxies, and locality insights for 2026 analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fooddy.in\/blog\/swiggy-bangalore-restaurants-dataset\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Swiggy Bangalore Restaurants Dataset \u2013 Stats &amp; 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