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 🖥️
data = FileAttachment("mydata@2.csv").csv({ typed: true })
data2 = d3.dsv(";", await FileAttachment("mydata2@2.csv").url(), d3.autoType)
data2
Workbook = FileAttachment("mydata.xlsx").xlsx()
data3 = Workbook.sheet("data", { headers: true })
zip = FileAttachment("mydata.zip").zip()
data4 = zip.file("mydata.csv").csv({ typed: true })
data5 = FileAttachment("mydata.json").json()
Inputs.table(data, { rows: 5 })
data.columns
data.map((d) => d.name)
data.map((d) => d["name"])
data.filter((d) => d.IDH > 0.5)
data.filter((d) => d.nom == "Botswana")
data.sort((a, b) => d3.ascending(a.POP, b.POP))
data.sort((a, b) => d3.descending(a.POP, b.POP))
newdata2 = data.map((d) => ({
id: d.iso3,
nom: d.name,
population: Math.round(d.POP * 1000) // on convertit en milliers et on arrondi
}))
op = aq.op
dt = aq.from(data)
dt.objects()
[...dt]
dt.columnNames()
dt.numCols()
dt.numRows()
html`${dt.slice(0, 5).toHTML()}`
md`${dt.slice(0, 5).toMarkdown()}`
Inputs.table(dt.objects(), { rows: 5 })
dt.array("name")
dt.values("name") // executez cette cellule
dt.get("iso3", 10)
getiso = dt.getter("iso3")
getiso(10)
dt.orderby("POP")
dt.orderby(aq.desc("POP"))
dt.dedupe("SUBREG").objects()
dt.select("name", "POP")
dt.slice(2, 5)
dt.sample(10)
dt.relocate("SUBREG", { after: "nom" })
dt.relocate("name", { before: "iso3" })
dt.derive({ pdppercapita: (d) => d.PIB / d.POP }, { after: "PIB" })
dt.rename({ name: "iso3", iso3: "id" })
dt.filter((d) => d["IDH"] > 0.5)
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()
Inputs.table(x)
dt.groupby("SUBREG").objects({ grouped: true })
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()
Inputs.table(summary)
Par exemple, on a dt1
dt1 = aq.from(data).select("iso3", "name", "POP")
dt2 = aq.from(data2).select("iso3", "ESPVIE", "AGEMED").rename({ iso3: "id" })
dt3 = dt1.join(dt2, ["iso3", "id"])
dt4 = aq
.from(data)
.select("iso3", "name", "POP", "PIB")
.orderby("POP")
.filter((d) => d["POP"] > 50)
.relocate("PIB", { before: "POP" })
mydata@2.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
mydat
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