3 ms·

https://ourworldindata.org/grapher/total-daily-covid-cases-per-million?tab=map https://ourworldindata.org/grapher/total-daily-covid-cases-p... there you go (ma

https://ourworldindata.org/grapher/total-daily-covid-cases-per-million?tab=map https://ourworldindata.org/grapher/total-daily-covid-cases-p... there you go (may 9) df=pd.read_csv("~/Downloads/total-daily-covid-cases-per-million.csv") df.sort_values(['Date', 'Daily new confirmed cases per million (cases)'])[-20:][['Entity', 'Daily new confirmed cases per million (cases)']].to_string(index=False) Out[14]: Entity Daily new confirmed cases per million (cases) Saudi Arabia 48.860 Armenia 48.933 Belgium 50.994 Gabon 52.118 Portugal 54.233 United Arab Emirates 55.913 Gibraltar 59.363 Sweden 63.569 United Kingdom 68.482 Chile 72.765 Russia 73.314 United States 81.440 North America 84.775 Sao Tome and Principe 95.820 Belarus 98.737 Peru 100.722 Singapore 131.274 Bahrain 143.984 Kuwait 150.097 Qatar 455.041

perl4ever 6y ago
That is interesting, but per capita data is not the answer to what I was interested in originally - what countries is the epidemic growing fastest in is different from in what countries is most of the epidemic's growth happening in.

perl4ever 6y ago

That is interesting, but per capita data is not the answer to what I was interested in originally - what countries is the epidemic growing fastest in is different from in what countries is most of the epidemic's growth happening in.