Challenge of the 2018 Wikidata Hackathon Zurich: "Visualizing Data on Wikidata"
- Get data out of Wikidata
SELECT ?muni ?muniLabel ?district ?districtLabel (COUNT(DISTINCT ?p) as ?properties) (COUNT(?o) as ?statements) ?population
WHERE
{
?muni wdt:P771 ?municode ;
wdt:P31/wdt:P279* wd:Q486972 ;
wdt:P131 wd:Q11943 ;
?p ?o .
MINUS { ?muni rdfs:label ?o }
OPTIONAL { ?muni wdt:P1082 ?population }
OPTIONAL { ?muni wdt:P131 ?district.
?district p:P31 ?statement .
?statement ps:P31 wd:Q15644465 . }
SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
GROUP BY ?muni ?muniLabel ?population ?district ?districtLabel
ORDER BY DESC(?properties)- Manually add the district of Zurich to the city of Zurich
before: https://www.wikidata.org/entity/Q72,Zürich,,,171,2488,415682
after: https://www.wikidata.org/entity/Q72,Zürich,https://www.wikidata.org/entity/Q660732,Zurich District,171,2488,415682
- Convert the CSV to JSON
import pandas as pd
import json
df = pd.read_csv('result.csv')
df.fillna(0, inplace=True)
t = dict()
t['name'] = 'top'
t['children'] = []
for d in df.districtLabel.unique():
o = dict()
o['name'] = d
o['statements'] = 0
o['properties'] = 0
o['population'] = 0
o['children'] = []
t['children'].append(o)
for idx, row in df[df.districtLabel == d].iterrows():
m = dict()
m['name'] = row['muniLabel']
m['statements'] = row['statements']
m['properties'] = row['properties']
m['population'] = row['population']
o['children'].append(m)
with open('data.json', 'w') as outfile:
json.dump(t, outfile, ensure_ascii=False)- Adjust the D3.js Treemap example