United States: Puerto Rico¶

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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")

Notebook executed on: 26/01/2023 16:45:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="Puerto Rico", weeks=5);

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-01-26T16:45:22.723922 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 200 200 250 250 300 300 7-day incidence rate (per 100K people) 208.9 Puerto Rico, US, last 5 weeks, last data point from 2023-01-25 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 20 40 60 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.0 0.1 0.2 0.3 0.4 daily change normalised per 100K 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) United States: Puerto Rico cases daily growth factor United States: Puerto Rico cases daily growth factor (rolling mean) United States: Puerto Rico estimated R (using cases) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on deaths) United States: Puerto Rico deaths daily growth factor United States: Puerto Rico deaths daily growth factor (rolling mean) United States: Puerto Rico estimated R (using deaths) 26 Dec 02 Jan 09 Jan 16 Jan 23 Jan 0 250 500 750 1000 cases doubling time [days] United States: Puerto Rico doubling time cases (rolling mean) United States: Puerto Rico doubling time deaths (rolling mean) 0 639 1277 1916 daily change United States: Puerto Rico new cases (rolling 7d mean) United States: Puerto Rico new cases 0.00 3.19 6.39 9.58 12.77 daily change United States: Puerto Rico new deaths (rolling 7d mean) United States: Puerto Rico new deaths 0 306 613 919 1225 deaths doubling time [days]
In [4]:
overview(country="US", region="Puerto Rico");

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-01-26T16:45:31.614086 image/svg+xml Matplotlib v3.6.3, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 1000 1000 2000 2000 7-day incidence rate (per 100K people) 208.9 Puerto Rico, US, last data point from 2023-01-25 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 200 400 600 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.0 0.5 1.0 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases)