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plot_your_career = (artiste) => {
return Plot.plot({
width: width,
height: 300,
x: {
nice: true,
label: "Date",
tickFormat: d3.format(".0f")
},
y: { nice: true },
color: {
type: "categorical",
scheme: "tableau10",
legend: true
},
marks: [
Plot.dot(
data_work
.filter(aq.escape((d) => d.itemLabel === artiste))
.orderby("date"),
Plot.dodgeY({
x: (d) => d.year,
title: (d) => d.workLabel + " (" + d.date + ")",
fill: (d) => d.type,
anchor: "middle"
})
)
]
});
}
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plot_your_career("Jeanne Moreau")
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data_work_byartist = data_work.groupby("item", "itemLabel").count().orderby(aq.desc("count"))
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data_work_filtered = data_work.filter(aq.escape(d => d.item === artist2.item)).orderby("date")
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data_work.dedupe().orderby("date").groupby(["item", "itemLabel"]).slice(0,1).join_left(data_artistes, "item").view()
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probability_by_gender = data_work
.dedupe()
.orderby("date")
.groupby(["item", "itemLabel"])
.slice(0,1)
.join_left(data_artistes, "item")
.ungroup()
.groupby(["genderLabel", "type"])
.count()
.ungroup()
.groupby("genderLabel")
.derive({share: d => 100 * d.count/op.sum(d.count)})
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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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