Head coach data science @neuefische, @pyladieshamburg organizer , vice chair on the @python Software Verband, managing member @PSF and part of the D&I PSF work group
Public
Edited
Jan 18
Paused
1 star
yearlyOcean.map((d) => d.Year)
d3.max([
d3.max(yearlyLand, (d) => d.Anomaly),
d3.max(yearlyOcean, (d) => d.Anomaly)
])
d3.min([
d3.min(yearlyLand, (d) => d.Anomaly),
d3.min(yearlyOcean, (d) => d.Anomaly)
])
d3.min(yearlyLand, (d) => d.Anomaly)
(laextent[1] - laextent[0]) / (ocextent[1] - ocextent[0])
ocextent = d3.extent(yearlyOcean, (d) => d.Anomaly)
yearAxis = (g, H) => {
g.attr("class", "arc_label").attr(
"transform",
`translate(0, ${H - margin.bottom + 20})`
);
g.append("text")
.attr("class", "subheader")
.attr("x", margin.right)
.attr("text-anchor", "start")
.text(d3.min(yearlyLand, (d) => d.Year));
g.append("text")
.attr("class", "subheader")
.attr("x", width - margin.left)
.attr("text-anchor", "end")
.text(d3.max(yearlyLand, (d) => d.Year));
}
height = (width * 2) / 3.3
yearlyOcean = cleancsv(dirtyocean, 4)
dirtyland = d3.text(
"https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series/globe/land/ann/2/1850-2023/data.csv?trend=true&trend_base=10&begtrendyear=1982&endtrendyear=2022"
)
yearlyLand = cleancsv(dirtyland, 4)
function cleancsv(dirtycsv, rowcount) {
const rows = d3.csvParseRows(dirtycsv),
comments = rows
.splice(0, rowcount) // <= 2 is the number of lines to consider as comments
.flat()
.join(" "),
columns = rows.splice(0, 1).flat();
return Object.assign(
rows.map((row) =>
Object.fromEntries(columns.map((name, i) => [name, Number(row[i])]))
),
{ columns, comments }
);
}
colors = ({
background: "#f9f6e9", //"white", //"#FAEFD1",
textcolor: "#042c44",
hot: "#f4352c",
cold: "#042c44",
lighthot: "#fdeae9",
lightcold: "#e5e9ec"
})
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.