{"id":4057,"date":"2026-02-13T06:40:56","date_gmt":"2026-02-13T06:40:56","guid":{"rendered":"https:\/\/fooddy.in\/blog\/?p=4057"},"modified":"2026-02-13T06:42:00","modified_gmt":"2026-02-13T06:42:00","slug":"zomato-bangalore-restaurants-dataset","status":"publish","type":"post","link":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/","title":{"rendered":"Zomato Bangalore Restaurants Dataset | Full Guide &#038; Insights for 2026"},"content":{"rendered":"\n<p>The Zomato Bangalore restaurants dataset is a structured snapshot of zomato bangalore restaurants, including ratings, pricing, cuisines, and ordering features that support restaurant analytics, recommendation prototypes, and locality-level food trend studies.<\/p>\n\n\n\n<p>In practice, the dataset is used to compare neighborhoods, estimate price bands, profile popular cuisines, and test models that predict ratings or cost. It is also commonly used to build dashboards that summarize restaurant density, online ordering coverage, and table-booking availability.<\/p>\n\n\n\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.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The dataset contains over 51,000 Bengaluru restaurant rows with fields like location, cuisines, rating, votes, and approximate cost for two.<\/li>\n\n\n\n<li>It is reliable for aggregate patterns such as price bands, cuisine mix, and locality density, but not for real-time menus or new openings.<\/li>\n\n\n\n<li>A stable workflow removes duplicates, converts rating strings into numeric values, standardizes cost formatting, and treats multi-valued cuisine fields correctly before analysis.<\/li>\n<\/ul>\n\n\n\n<style>\n  .stat-grid {\n    display: grid;\n    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));\n    gap: 22px;\n    margin: 32px 0;\n  }\n  .stat-box {\n    border: 1px solid #eee;\n    border-radius: 16px;\n    padding: 22px 24px;\n    background: #ffffff;\n  }\n  .stat-heading {\n    font-size: 15px;\n    font-weight: 700;\n    color: #111;\n    margin-bottom: 14px;\n  }\n  .stat-main {\n    display: flex;\n    align-items: baseline;\n    gap: 10px;\n    margin-bottom: 12px;\n  }\n  .stat-value {\n    font-size: 44px;\n    font-weight: 800;\n    color: #ff711e;\n    animation: blinkMove 1.6s infinite ease-in-out;\n    line-height: 1;\n    white-space: nowrap;\n  }\n  .stat-unit {\n    font-size: 15px;\n    font-weight: 700;\n    color: #111;\n  }\n  .stat-text {\n    font-size: 14px;\n    color: #444;\n    font-weight: 600;\n    line-height: 1.55;\n    margin-bottom: 10px;\n  }\n  .stat-source {\n    font-size: 13px;\n    font-weight: 600;\n    color: #666;\n  }\n  .stat-source a {\n    color: #ff711e;\n    font-weight: 700;\n    text-decoration: none;\n    word-break: break-word;\n  }\n  .stat-source a:hover {\n    text-decoration: underline;\n  }\n  @keyframes blinkMove {\n    0%   { opacity: 0; transform: translateX(12px) scale(0.95); }\n    25%  { opacity: 1; transform: translateX(0) scale(1); }\n    75%  { opacity: 1; transform: translateX(0) scale(1); }\n    100% { opacity: 0; transform: translateX(-12px) scale(0.95); }\n  }\n  @media (prefers-reduced-motion: reduce) {\n    .stat-value { animation: none; }\n  }\n<\/style>\n\n<div class=\"stat-grid\">\n\n<!-- CARD 1 -->\n<div class=\"stat-box\">\n<div class=\"stat-heading\">Zomato Bangalore Dataset Size<\/div>\n<div class=\"stat-main\">\n<div class=\"stat-value\">51,717<\/div>\n<div class=\"stat-unit\">Records<\/div>\n<\/div>\n<div class=\"stat-text\">The publicly available Zomato Bangalore restaurants dataset contains 51,717 listings with 17 structured attributes including rating, votes, cuisines, and cost.<\/div>\n<div class=\"stat-source\">\n      Source: <a href=\"https:\/\/www.opendatabay.com\/data\/consumer\/d4bcfedf-0737-4f29-85a0-07645286cbe0\" target=\"_blank\" rel=\"nofollow noopener\">OpenDataBay<\/a>\n<\/div>\n<\/div>\n\n<!-- CARD 2 -->\n<div class=\"stat-box\">\n<div class=\"stat-heading\">Highest Restaurant Density In Bengaluru<\/div>\n<div class=\"stat-main\">\n<div class=\"stat-value\">5,124<\/div>\n<div class=\"stat-unit\">Restaurants (BTM)<\/div>\n<\/div>\n<div class=\"stat-text\">BTM Layout ranks as the locality with the highest restaurant count in Bengaluru based on spatial analysis of Zomato restaurant listings.<\/div>\n<div class=\"stat-source\">\n      Source: <a href=\"https:\/\/medium.com\/@harinivas278\/spatial-data-analysis-on-zomato-restaurants-in-bengaluru-1b6b0fc2cb66\" target=\"_blank\" rel=\"nofollow noopener\">Medium Spatial Analysis Study<\/a>\n<\/div>\n<\/div>\n\n<!-- CARD 3 -->\n<div class=\"stat-box\">\n<div class=\"stat-heading\">Total Estimated Restaurants In Bengaluru<\/div>\n<div class=\"stat-main\">\n<div class=\"stat-value\">12,000+<\/div>\n<div class=\"stat-unit\">Restaurants<\/div>\n<\/div>\n<div class=\"stat-text\">Data-driven portfolio analyses estimate that Bengaluru hosts over 12,000 active restaurants across multiple cuisine categories.<\/div>\n<div class=\"stat-source\">\n      Source: <a href=\"https:\/\/www.datascienceportfol.io\/nityagupta\/projects\/1\" target=\"_blank\" rel=\"nofollow noopener\">DataSciencePortfolio<\/a>\n<\/div>\n<\/div>\n\n<!-- CARD 4 -->\n<div class=\"stat-box\">\n<div class=\"stat-heading\">Restaurants Without Table Booking<\/div>\n<div class=\"stat-main\">\n<div class=\"stat-value\">85%<\/div>\n<div class=\"stat-unit\">Listings<\/div>\n<\/div>\n<div class=\"stat-text\">Approximately 85% of Bengaluru restaurant listings in the Zomato dataset do not support table booking through the platform.<\/div>\n<div class=\"stat-source\">\n      Source: <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2022\/09\/exploratory-data-analysis-of-zomato-bangalore-restaurants\/\" target=\"_blank\" rel=\"nofollow noopener\">Analytics Vidhya<\/a>\n<\/div>\n<\/div>\n\n<\/div>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What The Dataset Is And What It Is Not<\/strong><\/h2>\n\n\n\n<p>The dataset is a tabular collection of restaurant attributes compiled from Zomato listings for Bengaluru.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>It is not an official, continuously updated Zomato feed. It does not represent every restaurant in Bangalore at a single point in time, and it cannot guarantee current availability or pricing.<\/li>\n\n\n\n<li>It works best as a learning and analytics dataset. It enables reproducible experiments on restaurant metadata rather than functioning as a live city directory.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Where The Dataset Comes From<\/strong><\/h3>\n\n\n\n<p>Most public versions trace back to a Kaggle release that is widely referenced by EDA notebooks and GitHub repositories.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The commonly cited structure includes about <strong>51,717 rows and 17 columns<\/strong> in the raw file. After cleaning and feature selection, most analyses reduce this to a smaller, modeling-ready dataset.<\/li>\n\n\n\n<li>Some projects scrape smaller custom subsets instead of using Kaggle. These are easier to control but may introduce sampling bias.<\/li>\n<\/ul>\n\n\n\n<p><strong>Related &#8211; <a href=\"https:\/\/fooddy.in\/blog\/bengaluru-restaurants-statistics\/\">Bengaluru Restaurants Statistics<\/a><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How To Get It For Free And Keep It Reproducible<\/strong><\/h2>\n\n\n\n<p>The phrase Zomato Bangalore restaurants dataset free typically refers to downloading the publicly available CSV from a dataset hub.<\/p>\n\n\n\n<p>Reproducibility depends on version control. A stable setup includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The original raw CSV<\/li>\n\n\n\n<li>A cleaned CSV with documented transformations<\/li>\n\n\n\n<li>A notebook or script that logs every preprocessing step<\/li>\n<\/ul>\n\n\n\n<p>Pinning a specific file version prevents silent changes in row counts or schema.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What Columns Typically Exist In The Kaggle-Style Dataset<\/strong><\/h3>\n\n\n\n<p>The dataset is typically described as containing 17 attributes.<\/p>\n\n\n\n<p>url, address, name, online_order, book_table, rate, votes, phone, location, rest_type, dish_liked, cuisines, approx_cost(for two people), reviews_list, menu_item, listed_in(type), listed_in(city)<\/p>\n\n\n\n<p>These fields combine numeric signals, categorical descriptors, and text-based attributes. Many projects later drop reviews_list and menu_item to simplify modeling.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Data Quality Issues That Appear In Real Projects<\/strong><\/h3>\n\n\n\n<p>The dataset mixes numeric fields with text-heavy columns, which creates formatting inconsistencies.<\/p>\n\n\n\n<p>Ratings often include values such as \u201cNEW\u201d, \u201c-\u201d, or numeric values followed by \u201c\/5\u201d. These must be standardized before aggregation.<\/p>\n\n\n\n<p>Cost fields include commas and must be converted into numeric form. Duplicate records also appear. One documented walkthrough reports removing 124 duplicates and reducing the dataset to 51,593 rows across selected columns.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Cleaning And Normalization That Keeps Analysis Stable<\/strong><\/h2>\n\n\n\n<p>Cleaning must be deterministic to ensure reproducibility.<\/p>\n\n\n\n<p><strong>Most workflows follow these steps:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Standardize column names and remove whitespace.<\/li>\n\n\n\n<li>Remove exact duplicates and check for outlet-level duplicates by name and address.<\/li>\n\n\n\n<li>Convert rate into a numeric float by stripping formatting artifacts.<\/li>\n\n\n\n<li>Convert votes and cost fields into integers.<\/li>\n\n\n\n<li>Normalize multi-valued columns such as cuisines and rest_type.<\/li>\n<\/ol>\n\n\n\n<p>This structured preparation allows consistent results across notebooks and dashboards.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Analytical Questions The Dataset Answers Well<\/strong><\/h3>\n\n\n\n<p>The dataset supports locality-based comparisons, including which neighborhoods contain the highest concentration of restaurants.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>It supports cuisine frequency analysis, such as identifying dominant cuisine combinations across Bengaluru.<\/li>\n\n\n\n<li>It also enables ordering behavior insights using online_order and book_table flags as proxies.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Patterns That Repeatedly Show Up In Bangalore-Focused EDA<\/strong><\/h2>\n\n\n\n<p>Repeated analyses show BTM as one of the highest restaurant-density localities. Koramangala 7th Block also frequently appears near the top.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cuisine frequency charts commonly highlight North Indian, Chinese, and South Indian as dominant categories.<\/li>\n\n\n\n<li>Ordering-related findings suggest that online ordering is widely available, while table booking is less common. One walkthrough estimated that approximately 85% of listings do not support table booking through Zomato.<\/li>\n\n\n\n<li>Cost distributions often show clustering within the \u20b9300\u2013\u20b9400 range for average cost for two, with higher-cost corridors appearing in central business zones.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Using The Dataset For \u201cBest Restaurants\u201d Questions Without Making Weak Claims<\/strong><\/h3>\n\n\n\n<p>The keyword best restaurants in bangalore zomato appears frequently, but the dataset cannot certify real-world \u201cbest\u201d without verification.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A transparent scoring rule defines best within the dataset. A practical approach removes rows with missing ratings, keeps listings above 4.0 rating with at least 500 votes, and sorts by rating and vote count.<\/li>\n\n\n\n<li>This creates a reliable \u201ctop-rated in dataset\u201d subset without overstating current accuracy.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Locality Context: Koramangala And The Zomato Ecosystem<\/strong><\/h2>\n\n\n\n<p>Koramangala is a central area in Bangalore\u2019s food-tech narrative and appears frequently in dataset explorations.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The phrase zomato office koramangala is often associated with the broader ecosystem presence in this region.<\/li>\n\n\n\n<li>For locality-based analysis, Koramangala functions as a high-density anchor area for comparison with BTM, Indiranagar, HSR Layout, Whitefield, and CBD corridors.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Restaurant Discovery Logic That Works With This Dataset<\/strong><\/h3>\n\n\n\n<p>Restaurant discovery models usually apply a limited number of interpretable filters.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A stable configuration includes locality, cuisine, cost band, and a vote-weighted reliability score.<\/li>\n\n\n\n<li>One basic scoring structure uses a normalized rating multiplied by the logarithm of votes, reducing the dominance of low-vote outliers.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A Compact Table Of Common Dataset Variants And Their Tradeoffs<\/strong><\/h2>\n\n\n\n<div class=\"TyagGW_tableContainer\">\n<div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\">\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"7146\" data-end=\"7581\">\n<thead data-start=\"7146\" data-end=\"7212\">\n<tr data-start=\"7146\" data-end=\"7212\">\n<th class=\"\" data-start=\"7146\" data-end=\"7164\" data-col-size=\"sm\">Dataset Variant<\/th>\n<th class=\"\" data-start=\"7164\" data-end=\"7180\" data-col-size=\"sm\">Typical Scale<\/th>\n<th class=\"\" data-start=\"7180\" data-end=\"7191\" data-col-size=\"sm\">Strength<\/th>\n<th class=\"\" data-start=\"7191\" data-end=\"7212\" data-col-size=\"sm\">Common Limitation<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"7278\" data-end=\"7581\">\n<tr data-start=\"7278\" data-end=\"7382\">\n<td data-start=\"7278\" data-end=\"7307\" data-col-size=\"sm\">Kaggle-Style Bengaluru CSV<\/td>\n<td data-start=\"7307\" data-end=\"7331\" data-col-size=\"sm\">~51k rows, 17 columns<\/td>\n<td data-start=\"7331\" data-end=\"7356\" data-col-size=\"sm\">Broad coverage for EDA<\/td>\n<td data-start=\"7356\" data-end=\"7382\" data-col-size=\"sm\">Not a real-time snapshot<\/td>\n<\/tr>\n<tr data-start=\"7383\" data-end=\"7488\">\n<td data-start=\"7383\" data-end=\"7409\" data-col-size=\"sm\">Notebook-Cleaned Subset<\/td>\n<td data-start=\"7409\" data-end=\"7436\" data-col-size=\"sm\">~51k rows, fewer columns<\/td>\n<td data-start=\"7436\" data-end=\"7459\" data-col-size=\"sm\">Cleaner for modeling<\/td>\n<td data-start=\"7459\" data-end=\"7488\" data-col-size=\"sm\">Loses text-heavy metadata<\/td>\n<\/tr>\n<tr data-start=\"7489\" data-end=\"7581\">\n<td data-start=\"7489\" data-end=\"7520\" data-col-size=\"sm\">Custom Scrape Project Subset<\/td>\n<td data-start=\"7520\" data-end=\"7534\" data-col-size=\"sm\">~3k records<\/td>\n<td data-start=\"7534\" data-end=\"7558\" data-col-size=\"sm\">Controlled extraction<\/td>\n<td data-start=\"7558\" data-end=\"7581\" data-col-size=\"sm\">Smaller sample size<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\n\n\n<p>Each variant reflects a specific capture and cleaning pipeline rather than absolute ground truth.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How \u201cZomato Gold\u201d Fits Into Dataset-Driven Work<\/strong><\/h3>\n\n\n\n<p>The phrase zomato gold restaurants bangalore suggests membership-linked restaurants, but public datasets generally do not include eligibility flags.<\/p>\n\n\n\n<p>Dataset-based selection instead focuses on rating thresholds, locality, and cost band before verifying offers within the app.<\/p>\n\n\n\n<p>This preserves analytical accuracy while acknowledging dataset limits.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How To Use The Dataset In 2026 Without Making It Stale<\/strong><\/h3>\n\n\n\n<p>The keyword Zomato bangalore restaurants dataset 2026 is best addressed through updated methodology rather than claiming updated rows.<\/p>\n\n\n\n<p>The dataset remains relevant when used for reproducible experimentation, benchmarking models, and analyzing structural food discovery patterns.<\/p>\n\n\n\n<p>Time-sensitive elements such as menu updates, delivery fees, and promotional eligibility should always be verified separately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Three Practical Use Cases That Match Real Intent<\/strong><\/h3>\n\n\n\n<p>Market scanning involves comparing localities by density, cuisine distribution, and cost bands to identify commercial opportunities.<\/p>\n\n\n\n<p>Restaurant concept testing evaluates underrepresented cuisines in selected neighborhoods before investment decisions.<\/p>\n\n\n\n<p>Product prototyping uses the dataset to build recommendation engines and dashboards that simulate food discovery workflows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Legal, Ethical, And Terms-Of-Use Considerations<\/strong><\/h3>\n\n\n\n<p>The dataset is best used for research and aggregate insights rather than redistributing raw contact details.<\/p>\n\n\n\n<p>Fields such as phone numbers and URLs should be excluded from public dashboards where possible.<\/p>\n\n\n\n<p>Re-scraping workflows must consider rate limits and structural bias introduced by anti-bot protections.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>The Zomato Bangalore restaurants dataset is a practical foundation for Bengaluru restaurant analytics, particularly for locality comparisons, cuisine profiling, and reproducible recommendation baselines.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>It is strongest for aggregate pattern analysis and weakest as a live restaurant directory.<\/li>\n\n\n\n<li>A structured cleaning pipeline and transparent scoring rules transform the dataset into a reusable asset for Bangalore-focused analytics projects in 2026 and beyond.<\/li>\n<\/ul>\n\n\n\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Zomato Bangalore restaurants dataset is a structured snapshot of zomato bangalore restaurants, including ratings, pricing, cuisines, and ordering features that support restaurant analytics, recommendation prototypes, and locality-level food trend studies. In practice, the dataset is used to compare neighborhoods, estimate price bands, profile popular cuisines, and test models that predict ratings or cost. It [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":4058,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[25],"tags":[],"class_list":["post-4057","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>Zomato Bangalore Restaurants Dataset \u2013 51K Records<\/title>\n<meta name=\"description\" content=\"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.\" \/>\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\/zomato-bangalore-restaurants-dataset\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Zomato Bangalore Restaurants Dataset \u2013 51K Records\" \/>\n<meta property=\"og:description\" content=\"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/\" \/>\n<meta property=\"og:site_name\" content=\"Fooddy\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-13T06:40:56+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-13T06:42:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"putta srujan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"putta srujan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/\",\"url\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/\",\"name\":\"Zomato Bangalore Restaurants Dataset \u2013 51K Records\",\"isPartOf\":{\"@id\":\"https:\/\/fooddy.in\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png\",\"datePublished\":\"2026-02-13T06:40:56+00:00\",\"dateModified\":\"2026-02-13T06:42:00+00:00\",\"author\":{\"@id\":\"https:\/\/fooddy.in\/blog\/#\/schema\/person\/209cba36e2bc397bc4b0cba7d319afe8\"},\"description\":\"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.\",\"breadcrumb\":{\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage\",\"url\":\"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png\",\"contentUrl\":\"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png\",\"width\":1200,\"height\":600,\"caption\":\"Zomato Bangalore Restaurants Dataset\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/fooddy.in\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Zomato Bangalore Restaurants Dataset | Full Guide &#038; Insights for 2026\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fooddy.in\/blog\/#website\",\"url\":\"https:\/\/fooddy.in\/blog\/\",\"name\":\"Fooddy\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fooddy.in\/blog\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/fooddy.in\/blog\/#\/schema\/person\/209cba36e2bc397bc4b0cba7d319afe8\",\"name\":\"putta srujan\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/fooddy.in\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/2a4526bc33e0da9bb4a4331beacaceca6e9fa836abb6fa480dd0465463abcb9a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/2a4526bc33e0da9bb4a4331beacaceca6e9fa836abb6fa480dd0465463abcb9a?s=96&d=mm&r=g\",\"caption\":\"putta srujan\"},\"url\":\"https:\/\/fooddy.in\/blog\/author\/putta-srujan\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Zomato Bangalore Restaurants Dataset \u2013 51K Records","description":"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/","og_locale":"en_US","og_type":"article","og_title":"Zomato Bangalore Restaurants Dataset \u2013 51K Records","og_description":"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.","og_url":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/","og_site_name":"Fooddy","article_published_time":"2026-02-13T06:40:56+00:00","article_modified_time":"2026-02-13T06:42:00+00:00","og_image":[{"width":1200,"height":600,"url":"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png","type":"image\/png"}],"author":"putta srujan","twitter_card":"summary_large_image","twitter_misc":{"Written by":"putta srujan","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/","url":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/","name":"Zomato Bangalore Restaurants Dataset \u2013 51K Records","isPartOf":{"@id":"https:\/\/fooddy.in\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage"},"image":{"@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage"},"thumbnailUrl":"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png","datePublished":"2026-02-13T06:40:56+00:00","dateModified":"2026-02-13T06:42:00+00:00","author":{"@id":"https:\/\/fooddy.in\/blog\/#\/schema\/person\/209cba36e2bc397bc4b0cba7d319afe8"},"description":"Explore the Zomato Bangalore restaurants dataset with 51K+ records, cleaning steps, locality insights, and analysis use cases for 2026.","breadcrumb":{"@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#primaryimage","url":"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png","contentUrl":"https:\/\/fooddy.in\/blog\/wp-content\/uploads\/2026\/02\/Frame-2147229364.png","width":1200,"height":600,"caption":"Zomato Bangalore Restaurants Dataset"},{"@type":"BreadcrumbList","@id":"https:\/\/fooddy.in\/blog\/zomato-bangalore-restaurants-dataset\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/fooddy.in\/blog\/"},{"@type":"ListItem","position":2,"name":"Zomato Bangalore Restaurants Dataset | Full Guide &#038; Insights for 2026"}]},{"@type":"WebSite","@id":"https:\/\/fooddy.in\/blog\/#website","url":"https:\/\/fooddy.in\/blog\/","name":"Fooddy","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fooddy.in\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/fooddy.in\/blog\/#\/schema\/person\/209cba36e2bc397bc4b0cba7d319afe8","name":"putta srujan","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/fooddy.in\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/2a4526bc33e0da9bb4a4331beacaceca6e9fa836abb6fa480dd0465463abcb9a?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/2a4526bc33e0da9bb4a4331beacaceca6e9fa836abb6fa480dd0465463abcb9a?s=96&d=mm&r=g","caption":"putta srujan"},"url":"https:\/\/fooddy.in\/blog\/author\/putta-srujan\/"}]}},"_links":{"self":[{"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/posts\/4057","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/comments?post=4057"}],"version-history":[{"count":2,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/posts\/4057\/revisions"}],"predecessor-version":[{"id":4060,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/posts\/4057\/revisions\/4060"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/media\/4058"}],"wp:attachment":[{"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/media?parent=4057"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/categories?post=4057"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fooddy.in\/blog\/wp-json\/wp\/v2\/tags?post=4057"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}