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Lkocevski
Public
Worksheets: Learn Observable - Luka Kocevski
Edited
May 12, 2023
Fork of Import and Parse Your Dataset (Updated)
Importers
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temperature.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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temperature@1.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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data_filtered
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temperature@1.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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// 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
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Inputs.table(data_autoImported)
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Inputs.table(data_filtered, {width: 140 })
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data_row = FileAttachment("temperature@1.csv").csv()
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Inputs.table(data_row)
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import { SummaryTable } from "@observablehq/summary-table"


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SummaryTable(data_row)
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data_autoTyped = FileAttachment("temperature@1.csv").csv( {typed:true})
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Inputs.table(data_autoTyped)
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SummaryTable(data_autoTyped)
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data_autoTyped[0]["date"].constructor.name
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parseTime = d3.timeParse("%Y-%m-%d")
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parseTime("2011-10-02")
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data_manuallyTyped = FileAttachment('temperature@1.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
}
)
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SummaryTable(data_manuallyTyped)
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imageToDo
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openAllLinksInNewTab
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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.
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