Skip to main content
SimilarExport ZIP
100%

Chennai Restaurants

✓ Published0🌍 Public
AAkanksha
Last edited Jan 23, 2024
Created on Jan 23, 2024

This example displays the first row of the Chennai Restaurants dataset as a formatted JSON object. The code imports the data from a CSV file, converts quantitative attributes like bill length and body mass to numbers, and uses a preformatted HTML block to render the output. It relies on the D3.js library for data loading and formatting, with the visualization built entirely through DOM manipulation rather than canvas or SVG rendering.

AI-generated description

The Penguins Dataset, loaded and parsed as CSV.

Challenge

  • Find 3 datasets on a topic you are interested in
  • For each of those, fork this viz and replace the data
  • Parse the quantitative attributes into numbers
  • Update README.md to indicate the original data source
MIT Licensed

Similar vizzes

Loading thumbnail…

Chennai restaurants

This example reads a CSV of Chennai restaurant data and converts numerical fields like `Price_for_2`, `Dining_Rating`, `Dining_Rating_Count`, and `Delivery_Rating` to numbers. It then displays the first record as a formatted JSON object within a `<pre>` element using the Vega-Lite data loading and export API. The visualization is a static, text-based preview of the dataset, showcasing the raw structure of the data without any graphical encoding. The implementation relies on JavaScript string interpolation to set the font size and inject the JSON string into the container’s HTML.

AAkanksha
88% match
Loading thumbnail…

Chennai restaurants

This visualization displays the first record from a penguins dataset as a JSON object in a pre-formatted text block. The code loads data from a local CSV file, converts its quantitative attributes to numbers using unary plus operators, and exports both the parsed data and a main function. The main function sets a large font size and inserts the JSON representation of the first data row into the container. It relies on the browser's native JSON.stringify method and HTML pre element for rendering. The data originates from the palmerpenguins GitHub repository.

AAkanksha
87% match
Loading thumbnail…

Fork of Chennai restaurants

This fork of the Chennai restaurants visualization displays a single JSON record from the loaded penguins dataset, parsed into numeric attributes. The code imports data from a CSV file, converts measurement columns to numbers using unary plus operators, and renders the first data object as a formatted JSON string within an HTML `<pre>` element. The example uses raw JavaScript string interpolation to build the output, with no external visualization libraries.

AAkanksha
81% match
Loading thumbnail…

Parallel Coordinates with Brushing - Restaurant ratings

This interactive parallel coordinates plot visualizes restaurant ratings and pricing across Chennai neighborhoods, sourced from Zomato data. Users brush along any axis to filter the polylines, revealing patterns in dining and delivery ratings relative to cost. The visualization uses D3’s `brushY` and transitions for smooth filtering, with each axis scaled appropriately for its data type, allowing comparisons of categorical locations and quantitative scores.

AAkanksha
66% match
Loading thumbnail…

restaurant-orders-data

This visualization displays raw restaurant order data from a Kaggle dataset, presenting each record as a JSON object. The example loads the CSV file asynchronously and parses it using D3's `csvParse` function, mapping string attributes like "Order ID" and "Payment Method" to cleaner property names while converting numeric fields such as "Quantity" and "Price" to numbers. The `viz.js` module uses D3's `select` and `join` operations to render the parsed data as a formatted `<pre>` element, with the initial state showing "LOADING" until the fetch completes. The interaction pattern relies on a simple state management system where `setState` triggers a re-render of the visualization.

Ccsabavath@wpi.edu
66% match
Loading thumbnail…

Chennai Restaurants

This example renders a large "Hello World!" message inside a VizHub V3 Runtime container. The main function from index.js sets the container's inner HTML with a dynamic font size, displaying the text centered with inline styling. It demonstrates the basic structure of a VizHub visualization, where the entry point is the exported main function and no index.html is required, supporting continuous hot reloading with interactive widgets.

AAkanksha
65% match
Loading thumbnail…

Fast Food Restaurants Across America_2

This example displays basic dataset information and a preview of the first 10 rows from a CSV file containing 10,000 fast food restaurant locations across America. It uses D3 v5’s `csv` method to load the data, then appends `<pre>` elements to the body showing the row count, column count, file size in kilobytes, and a JSON-formatted sample of the records. The visualization relies on D3’s `select`, `csv`, and `csvFormat` APIs, with styling applied via CSS.

NNTCHANG1289
64% match
Loading thumbnail…

Fast Food Restaurants Across America_1

This example presents a console-style summary of a dataset containing 10,000 fast food restaurant locations across America. The visualization displays the total row count, column count, file size in kilobytes, and a JSON-formatted preview of the first ten records. The code uses D3’s `csv` API to load the remote CSV file and `select` to access the DOM, appending multiple `pre` elements to the body. The data originates from a Kaggle dataset, and the minimal, monochrome styling with green text on a black background evokes a terminal interface.

NNTCHANG1289
63% match