// ...
// ...
// ...
// ...
// ...
// ...
// ...
// 👆 To produce the result above, uncomment and complete the code below 👇:
// meteorites_temporary = FileAttachment('meteorites.csv').csv({typed: true})
// .then( data => {
// data.forEach( d => {
// ... // parse the Date column using `new Date(year, month, day, hours, minutes, seconds, milliseconds)`
// ... // convert mass from grams to tons
// })
// return data
// }
// )
// ...
// ...
// ...
// ...
// 👆 To produce the result above, uncomment and complete the code below 👇:
// aapl_temp = FileAttachment("aapl.csv").csv({typed: true}).then( dataset => {
// dataset.forEach( d => {
// ... // configure d3.timeParse() to mirror the date format in the data
// ... // use the parser on the date column
// ... // make 'Volume' a number (so, not a string)
// ... // make Open, High, Low columns' values numbers (you need to first get rid of the '$' signs)
// })
// return dataset
// }
// )
// ...
// ...
// ...
// import "Wrangler" and "op" cells from "@observablehq/data-wrangler" notebook
// import "SummaryTable" cell from "@observablehq/summary-table" notebook
import {toc} from "@nebrius/indented-toc"
import {imageToDo} from "@clokman/student-blocks"
imageToDo
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
