Skip to main content
SimilarExport ZIP
100%

Camera Dataset

✓ Published0🌍 Public
Wwenleicao
Last edited Sep 27, 2019
Created on Aug 31, 2019

This example loads a camera dataset from a remote CSV file and displays basic summary statistics: file size in kilobytes, row count, and column count. The visualization is intentionally minimal, showing only text in a large <pre> element. It uses the D3 v5 library, specifically the `d3.csv` method to parse the data and `d3.csvFormat` to measure the raw CSV length. The code also includes commented alternatives using the Fetch API with `async/await` and nested promises, demonstrating different approaches to asynchronous data loading. The dataset contains camera specifications such as model, release date, resolution, zoom range, weight, and price.

AI-generated description

This dataset is about camera, different brand, model and configuration and corresponding price. orginal link is here

Model Release date Max resolution Low resolution Effective pixels Zoom wide (W) Zoom tele (T) Normal focus range Macro focus range Storage included Weight (inc. batteries) Dimensions Price

MIT Licensed

Similar vizzes

Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates loading and parsing a CSV file from a remote URL using D3.js v5. It fetches a dataset of CSS named colors and displays key statistics about the file in a large text element. The visualization reports the file size in kilobytes, the total number of rows, and the number of columns. The code uses the `d3.csv` function to asynchronously fetch and parse the data, then leverages `d3.csvFormat` to calculate the file size. An alternative commented-out approach using the Fetch API, `async`/`await`, and `d3.csvParse` is also shown, along with a basic nested-callback example.

Mmscoop16
71% match
Loading thumbnail…

Load and Parse CSV

This example demonstrates loading and parsing a CSV file from a remote URL, then displaying basic dataset statistics as plain text. The program uses d3.csv to fetch and parse a named-color table, then reports the row count, column count, and approximate file size using d3.csvFormat to measure the formatted data. The page’s HTML pre element updates with these three values. It also includes commented alternatives using async/await with fetch and d3.csvParse, as well as nested promise callbacks, illustrating different approaches to asynchronous CSV handling.

Aavcwisesa
70% match
Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse a remote CSV file, displaying summary statistics about the dataset in large text. It fetches named CSS color data from a GitHub gist using `d3.csv`, then computes and shows the file size in kilobytes, the total number of rows, and the column count. The code uses D3 v7’s `d3.csv` promise API and `d3.csvFormat` to measure the parsed data’s size, appending the results to a `<pre>` element with DOM methods. The visualization is purely textual, highlighting basic data inspection after asynchronous loading.

GGabriel
70% match
Loading thumbnail…

Loading data with D3

This example demonstrates how to load and inspect a remote CSV dataset using D3. It fetches a tidy version of the Anscombe’s quartet from a GitHub gist and displays three summary metrics in large text: the file size in kilobytes, the number of rows, and the number of columns. The code uses `d3.csv` to asynchronously parse the data, `d3.csvFormat` to calculate the raw size, and the `data.length` and `data.columns.length` properties to report the dataset’s dimensions.

Rrvdv
70% match
Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3 v5. It displays the file size, row count, and column count of a wildlife trade dataset pulled from a GitHub gist. The primary approach uses `d3.csv`, which fetches and parses the CSV in one step, while commented-out alternatives show `fetch` with `async`/`await` combined with `d3.csvParse`. The results appear as large text in a `pre` element, providing a simple, immediate visual summary of the dataset’s basic dimensions.

KKarenNg
70% match
Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3 v5. It displays the file size, row count, and column count of a wildlife trade dataset pulled from a GitHub gist. The primary approach uses `d3.csv`, which fetches and parses the CSV in one step, while commented-out alternatives show `fetch` with `async`/`await` combined with `d3.csvParse`. The results appear as large text in a `pre` element, providing a simple, immediate visual summary of the dataset’s basic dimensions.

KKarenNg
70% match
Loading thumbnail…

Loading and Parsing CSV Data with D3

This example demonstrates loading a remote CSV file and displaying basic dataset statistics. It fetches CSS named colors from a GitHub gist and shows the file size in kilobytes, the total number of rows, and the column count. The visualization uses D3 v7’s `d3.csv` method to parse the data asynchronously, then `d3.csvFormat` to calculate the raw text length, with results rendered to a `<pre>` element. The code also includes commented alternatives using `fetch` and `d3.csvParse`.

AA
70% match
Loading thumbnail…

Parsing CSV Data with D3

This visualization demonstrates how to parse CSV data using D3.js by fetching a remote file and displaying summary statistics. The page shows the dataset’s row count, column count, and file size in kilobytes within a large text element. It uses D3’s `d3.csv()` function to load and parse the CSV from a GitHub gist, then formats the data with `d3.csvFormat()` to calculate its size. The example also includes commented-out code using `fetch()`, `async`/`await`, and `d3.csvParse()` as an alternative parsing approach.

TTetiana Humeniuk
70% match