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
Inputs.table(data)
chart2 = Choropleth(data, {
id: (d) => d.name, // country name, e.g. Zimbabwe
value: (d) => d.contributors_per_100k, // health-adjusted life expectancy
range: d3.interpolateBuPu,
features: countries,
featureId: (d) => d.properties.name, // i.e., not ISO 3166-1 numeric
borders: countrymesh,
projection: d3.geoEqualEarth(),
width
})
total_contributors = d3.sum(data_all, (d) => d.count_total_contributors)
data_all = data_raw[0]
world = FileAttachment("countries-50m.json").json()
countries = topojson.feature(world, world.objects.countries)
countrymesh = topojson.mesh(world, world.objects.countries, (a, b) => a !== b)
chart32 = Choropleth(intellij2021, {
id: (d) => d.name, // country name, e.g. Zimbabwe
value: (d) => d.countshare, // health-adjusted life expectancy
range: d3.interpolateBuPu,
features: countries,
featureId: (d) => d.properties.name, // i.e., not ISO 3166-1 numeric
borders: countrymesh,
projection: d3.geoEqualEarth(),
width
})
_sharing_data_outside = FileAttachment("2021_sharing_data_outside.csv").csv({
typed: true
})
Inputs.table(_sharing_data_outside)
_sharing_data_outside.filter(function (d) {
return d["country.live"] != null;
})
pythondevcountries = d3
.flatRollup(
_sharing_data_outside.filter(function (d) {
return d["country.live"] != null;
}),
(v) => v.length,
(d) => d["country.live"]
)
.map(([Country, Count]) => ({ Country, Count }))
total_respondends = d3.sum(pythondevcountries, (d) => d.Count)
intellij2021 = pythondevcountries.map((d) => ({
name: rename.get(d.Country) || d.Country,
count: +d.Count,
countshare: +d.Count / total_respondends
}))
import {Choropleth, Legend} from "@d3/choropleth"
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