Fork of Loading and Parsing CSV Data

Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing CSV data from a remote URL using D3 v5 and React. It displays the size of the dataset in kilobytes, the number of rows, and the number of columns in a large text format. The code uses `d3.csv` to fetch and parse the data, alongside an alternative approach using the Fetch API with async/await and `d3.csvParse`. The data source is a CSV file containing CSS named colors hosted on GitHub Gist.

SSteven Pearce

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote URL, displaying the file size in kilobytes, row count, and column count as plain text. It uses the `d3.csv` function from D3 v5 to fetch and parse the CSS Named Colors dataset. The code also includes commented-out alternatives using `fetch`, async/await, and `d3.csvParse`, showing different approaches to handling asynchronous data loading. The visualization itself is minimal, prioritizing the data-loading process over graphical representation.

Aalfredwang0707

Fork of Loading and Parsing CSV Data

This example demonstrates the loading and parsing of CSV data from a remote URL containing CSS named colors, displaying the file size, row count, and column count as large text. The visualization uses d3.csv to fetch and parse the data, then d3.csvFormat to calculate its size, with an alternative commented-out approach using the Fetch API and async/await alongside d3.csvParse. The output is rendered into a pre element via JavaScript, showcasing both the modern d3.v5 API and traditional text-based data processing.

RRoberto

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3 v5. It fetches a CSS Named Colors dataset from a GitHub gist and displays its size (in kilobytes), row count, and column count in a large text area. The primary implementation uses `d3.csv` with promise handling, while commented-out alternatives show equivalent approaches using `fetch` with `async`/`await` and `d3.csvParse`. The visualization is minimal, focusing on data loading mechanics rather than graphical representation.

Kkiranshashi

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse a CSV file containing CSS named colors, displaying the file size, row count, and column count as large text on the page. The code uses the D3 v5 library’s `d3.csvParse` method to convert fetched text into a data array, while the data is retrieved via the Fetch API with `async` and `await` syntax. The source CSV is hosted as a GitHub Gist, and the visualization emphasizes the parsing workflow rather than graphical rendering.

JJulia

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote source using D3.js. It displays the file size, row count, and column count of the CSS Named Colors dataset in a large text format. The code uses `d3.csv` to fetch and parse the data asynchronously, then accesses the resulting parsed array and its `.columns` property. An alternative approach using `fetch`, `async`/`await`, and `d3.csvParse` is provided in comments, along with a deprecated callback-based "pyramid of doom" pattern.

CCatalina Vajiac

Kkartheekgurijala2023@gmail.com

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote URL, displaying the file size, row count, and column count in a web page. It uses the D3.js library’s `d3.csv` method to fetch and parse the data asynchronously, then formats the result with `d3.csvFormat` to calculate the byte size. The code also includes commented-out alternatives using the Fetch API with `async`/`await` and a nested promise chain, illustrating different approaches to handling asynchronous data loading. The data source is a CSV file containing CSS named colors, hosted as a GitHub gist.

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote URL, displaying the file size, row count, and column count of the CSS Named Colors dataset. The code uses D3 v5’s `d3.csv` method to fetch and parse the data asynchronously, then formats the summary using `d3.csvFormat` and `data.columns`. A commented-out alternative shows parsing via the Fetch API with `async`/`await` and `d3.csvParse`. The results are rendered as large text inside an HTML `<pre>` element.

GGordon Yang