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More demos #45
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Scraped USGS earthquakes:
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https://github.com/adolph/getIncidentsGit Hoston incidents. Record looks like this: {
"ActiveIncidentDataTable": [
{
"Agency": "F",
"Address": "WESTLAKE PARK BLVD",
"CrossStreet": "BLK GRISBY RD",
"KeyMap": "488B",
"XCoord": "-95630227",
"YCoord": "29783580",
"CombinedResponse": "F",
"CallTimeOpened": "12/06/2021 17:42",
"IncidentType": "Automatic Alarm",
"AlarmLevel": "0",
"NumberOfUnits": "1",
"Units": "E078;"
}
] So incidents = json.loads(content)["ActiveIncidentDataTable"]
for incident in incidents:
x = incident.pop("XCoord")
y = incident.pop("YCoord")
incident["Latitude"] = float(y) / 1000000 if y else None
incident["Longitude"] = float(x) / 1000000 if x else None
call_time = incident.pop("CallTimeOpened")
date, time = call_time.split(" ")
mm, dd, yyyy = date.split("/")
incident["CallTimeOpened"] = "{}-{}-{} {}".format(yyyy, mm, dd, time)
incident["Units"] = [unit for unit in incident["Units"].split(";") if unit]
yield incident So the recipe is:
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FARA from https://github.com/simonw/fara-history
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https://github.com/mmarvick/scrape-seattle-city-light-outages
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https://github.com/simonw/package-stats
And for releases in that same repo:
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https://github.com/abkfenris/sugarloaf looks like a fun demo for CSV data. |
There's a wealth of interesting data in https://github.com/nychealth/coronavirus-data/tree/master/latest See also https://observablehq.com/@bmschmidt/nyc-test-positivity-december-2021 by https://twitter.com/benmschmidt/status/1473682517312131082 |
Using this thread to collect them - not yet decided if I'll turn these into live demos or just have a piece of documentation suggesting people try these out.
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