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Nicolas Lambert
Hi. I'm Nicolas Lambert, research engineer in geographic information science at CNRS (France). My job is to draw maps and teach cartography at the university. I am a geographer 🌎, and not really a developer 🖥️
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Observable pour les geographes
Edited
Apr 5, 2024
Fork of Manipuler les tableau de données
1 star
9
Observable pour les geographes
SommairePetite histoire (incomplète et subjective) de la cartographie et la datavizEt pourquoi pas le JavaScript ?Observable kezako ?
Les tableaux de données
Les données géographiquesLe format SVGLa bibliothèque d3jsFaire des graphiques avec plot (mais pas que)Faire du SIG dans la navigateurFaire des cartes avec bertinFaire des cartes avec geovizL'écosystème spatial du JavaScript
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data = FileAttachment("mydata@2.csv").csv({ typed: true })
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data2 = d3.dsv(";", await FileAttachment("mydata2@2.csv").url(), d3.autoType)
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data2
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Workbook = FileAttachment("mydata.xlsx").xlsx()
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data3 = Workbook.sheet("data", { headers: true })
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zip = FileAttachment("mydata.zip").zip()
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data4 = zip.file("mydata.csv").csv({ typed: true })
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data5 = FileAttachment("mydata.json").json()
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Inputs.table(data, { rows: 5 })
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data.columns
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data.map((d) => d.name)
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data.map((d) => d["name"])
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data.filter((d) => d.IDH > 0.5)
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data.filter((d) => d.nom == "Botswana")
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data.sort((a, b) => d3.ascending(a.POP, b.POP))
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data.sort((a, b) => d3.descending(a.POP, b.POP))
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newdata2 = data.map((d) => ({
id: d.iso3,
nom: d.name,
population: Math.round(d.POP * 1000) // on convertit en milliers et on arrondi
}))
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op = aq.op
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dt = aq.from(data)
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dt.objects()
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[...dt]
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dt.columnNames()
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dt.numCols()
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dt.numRows()
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html`${dt.slice(0, 5).toHTML()}`
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md`${dt.slice(0, 5).toMarkdown()}`
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Inputs.table(dt.objects(), { rows: 5 })
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dt.array("name")
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dt.values("name") // executez cette cellule
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dt.get("iso3", 10)
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getiso = dt.getter("iso3")
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getiso(10)
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dt.orderby("POP")
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dt.orderby(aq.desc("POP"))
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dt.dedupe("SUBREG").objects()
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dt.select("name", "POP")
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dt.slice(2, 5)
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dt.sample(10)
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dt.relocate("SUBREG", { after: "nom" })
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dt.relocate("name", { before: "iso3" })
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dt.derive({ pdppercapita: (d) => d.PIB / d.POP }, { after: "PIB" })
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dt.rename({ name: "iso3", iso3: "id" })
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dt.filter((d) => d["IDH"] > 0.5)
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x = dt
.rollup({
min: (d) => op.min(d["POP"]),
max: (d) => op.max(d["POP"]),
mean: (d) => op.mean(d["POP"]),
median: (d) => op.median(d["POP"]),
sum: (d) => op.sum(d["POP"]),
stdev: (d) => op.stdev(d["POP"]),
count: (d) => op.count()
})
.objects()
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Inputs.table(x)
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dt.groupby("SUBREG").objects({ grouped: true })
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summary = dt
.groupby("SUBREG")
.rollup({
min: (d) => d3.min(d["POP"]),
max: (d) => d3.max(d["POP"]),
mean: (d) => d3.mean(d["POP"]),
median: (d) => d3.median(d["POP"]),
sum: (d) => d3.sum(d["POP"]),
stdev: (d) => d3.stdev(d["POP"]),
count: (d) => d3.count()
})
.objects()
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Inputs.table(summary)
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Par exemple, on a **`dt1`**
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dt1 = aq.from(data).select("iso3", "name", "POP")
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dt2 = aq.from(data2).select("iso3", "ESPVIE", "AGEMED").rename({ iso3: "id" })
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dt3 = dt1.join(dt2, ["iso3", "id"])
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dt4 = aq
.from(data)
.select("iso3", "name", "POP", "PIB")
.orderby("POP")
.filter((d) => d["POP"] > 50)
.relocate("PIB", { before: "POP" })
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mydata@2.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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mydat
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