The avatar for @rstorni
Ramiro Storni
Published
Edited
Jul 7, 2022
Fork of Visualize a data frame with Observable, in Jupyter
11
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fullText = FileAttachment("iris_data.csv").text()
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lines = (await FileAttachment("iris_data.csv").text()).split('\n')
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(await FileAttachment("iris_data.csv").text()).split('\n').map(line => line.split(','))
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d3 = require("d3-dsv@1")
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irisData = d3.csvParse(await FileAttachment("iris_data.csv").text(), d3.autoType)
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irisData
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Inputs.table(irisData)
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irisData.filter(d => d.species === 'virginica').map(d => d.petalLength)
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{
const results = [];
for (const datum of irisData) {
if (datum.petalLength < 4) {
results.push(datum)
}
}
return results;
}
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vegaPetals = vl
.markPoint()
.data(input_data)
.encode(
vl.y().fieldQ(y_title),
vl.x().fieldQ(x_title),
vl.color().fieldN(legend)
)
.render()
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input_data = irisData
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y_title = 'petalLength'
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x_title = 'petalWidth'
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legend = 'species'
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petalLengths = irisData.map(sample => sample.petalLength)
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import { chart } with { petalLengths as data } from "@d3/histogram"
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chart
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simpleHtml = {
const sigils = {
setosa: '🌸',
versicolor: '🌼',
virginica: '🌺',
};
const sortedIrises = irisData.slice(0); // note making a copy here to sort
// because mutation of data from other cells is frowned upon in Observable
sortedIrises.sort((a, b) => a.petalLength - b.petalLength)
const flowers = sortedIrises.map(flower => html`
<span style="font-size: ${flower.petalLength * scale}px">${sigils[flower.species]}</span>`)
return html`<div style="height: 300px; overflow: scroll;">${flowers}</div>`
}
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viewof scale = slider({min: 1, max: 20, value:7, step: 0.5})
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md`**Plot**: The fastest way to make a useful, great-looking plot in Observable is to use [Plot](https://observablehq.com/@observablehq/plot).`
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dataSubset = input_data
.filter(data =>
data[specified_feature_1] >= sepalLengthLimits[0] &&
data[specified_feature_1] <= sepalLengthLimits[1])
.filter(data =>
data[specified_feature_2] >= sepalWidthLimits[0] &&
data[specified_feature_2] <= sepalWidthLimits[1])
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specified_feature_1 = "sepalLength"
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specified_feature_2 = "sepalWidth"
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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.
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