temperature.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
temperature.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
// Call the variable by its name and it will return an array of objects.
// You will need to expand the returned array with your mouse to match the template picture; the view will be collapsed again if you refresh the page; this is OK.
data_filtered
Inputs.table(data_autoImported)
Inputs.table( data_filtered, { width: 140 } )
data_raw = FileAttachment("temperature.csv").csv()
Inputs.table(data_raw)
// Step 1: Import the SummaryTable function from:
// https://observablehq.com/@observablehq/summary-table
// Feel free to look around on that page and examine what the function is about, what it can do, and the instructions to import/use it.
import { SummaryTable } from "@observablehq/summary-table"
// Step 2: Use the SummaryTable() function to get a summary table of the data.
SummaryTable( data_raw )
data_autoTyped = FileAttachment("temperature.csv").csv( {typed: true} )
Inputs.table(data_autoTyped)
SummaryTable(data_autoTyped)
data_autoTyped[0]["date"].constructor.name
data_manuallyTyped[0].temperature.constructor.name// ... your code here
parseTime = d3.timeParse("%Y-%m-%d")
parseTime("2011-10-02")
data_manuallyTyped = FileAttachment('temperature.csv').csv() // no `{typed: true}` in `csv()`
.then( data => {
data.forEach( d => {
d.date = parseTime(d.date) // from string to Date object
d.temperature = (d.temperature / 1.8) - 32 // from Fahrenheit to Celsius
})
return data
}
)
SummaryTable(data_manuallyTyped)
data_autoTyped[0].date.constructor.name// ... your code here
data_manuallyTyped[0].temperature.constructor.name// ... your code here
imageToDo
openAllLinksInNewTab
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.
