Tracking coronavirus total cases, deaths and new cases in Europe by country.
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print('''
Example of using jupyter notebook, pandas (data transformations), jinja2 (html, visual)
to create visual dashboards with fastpages
You see also the live version on https://gramener.com/enumter/covid19/europe.html
''')
Example of using jupyter notebook, pandas (data transformations), jinja2 (html, visual) to create visual dashboards with fastpages You see also the live version on https://gramener.com/enumter/covid19/europe.html
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import numpy as np
import pandas as pd
from jinja2 import Template
from IPython.display import HTML
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from pathlib import Path
if not Path('covid_overview.py').exists():
! wget https://raw.githubusercontent.com/pratapvardhan/notebooks/master/covid19/covid_overview.py
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import covid_overview as covid
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COL_REGION = 'Country/Region'
europe_countries = covid.mapping['df'].pipe(lambda d: d[d['Continent'].eq('Europe')])['Name'].values
filter_europe = lambda d: d[d['Country/Region'].isin(europe_countries)]
kpis_info = [
{'title': 'Italy', 'prefix': 'IT'},
{'title': 'Spain', 'prefix': 'SP'},
{'title': 'Germany', 'prefix': 'GE'}]
data = covid.gen_data(region=COL_REGION, filter_frame=filter_europe, kpis_info=kpis_info)
#hide_input
template = Template(covid.get_template(covid.paths['overview']))
dt_cols, LAST_DATE_I = data['dt_cols'], data['dt_last']
html = template.render(
D=data['summary'], table=data['table'],
newcases=data['newcases'].loc[:, dt_cols[LAST_DATE_I - 40]:dt_cols[LAST_DATE_I]],
COL_REGION=COL_REGION,
KPI_CASE='Europe',
KPIS_INFO=kpis_info,
LEGEND_DOMAIN=[5, 50, 500, np.inf],
np=np, pd=pd, enumerate=enumerate)
HTML(f'<div>{html}</div>')
Updated on March 27, 2020 ( +change since 5 days ago.)
In the last 5 days, 149,511 new Coronavirus cases have been reported in the Europe. Of which 27,360 (18%) are from Italy. Spain has reported 36,951 new cases in the last 5 days.
Country | New Cases | Total Cases | Deaths | Fatality | ||||
---|---|---|---|---|---|---|---|---|
Feb. 16
Mar. 27
|
(+NEW) since Mar, 22 | |||||||
Italy | 86,498 | (+27,360) | 9,134 | (+3,658) | 10.6% | |||
Spain | 65,719 | (+36,951) | 5,138 | (+3,366) | 7.8% | |||
Germany | 50,871 | (+25,998) | 342 | (+248) | 0.7% | |||
France | 33,402 | (+17,159) | 1,997 | (+1,321) | 6.0% | |||
United Kingdom | 14,745 | (+9,000) | 761 | (+479) | 5.2% | |||
Switzerland | 12,928 | (+5,454) | 231 | (+133) | 1.8% | |||
Netherlands | 8,647 | (+4,430) | 547 | (+367) | 6.3% | |||
Austria | 7,657 | (+4,075) | 58 | (+42) | 0.8% | |||
Belgium | 7,284 | (+3,883) | 289 | (+214) | 4.0% | |||
Portugal | 4,268 | (+2,668) | 76 | (+62) | 1.8% | |||
Norway | 3,755 | (+1,370) | 19 | (+12) | 0.5% | |||
Sweden | 3,069 | (+1,135) | 105 | (+84) | 3.4% | |||
Czechia | 2,279 | (+1,159) | 9 | (+8) | 0.4% | |||
Denmark | 2,200 | (+686) | 52 | (+39) | 2.4% | |||
Ireland | 2,121 | (+1,215) | 22 | (+18) | 1.0% | |||
Luxembourg | 1,605 | (+807) | 15 | (+7) | 0.9% | |||
Poland | 1,389 | (+755) | 16 | (+9) | 1.2% | |||
Romania | 1,292 | (+859) | 26 | (+23) | 2.0% | |||
Finland | 1,041 | (+415) | 7 | (+6) | 0.7% | |||
Russia | 1,036 | (+669) | 4 | (+3) | 0.4% | |||
Greece | 966 | (+342) | 28 | (+13) | 2.9% | |||
Iceland | 890 | (+322) | 2 | (+1) | 0.2% | |||
Slovenia | 632 | (+218) | 9 | (+7) | 1.4% | |||
Croatia | 586 | (+332) | 3 | (+2) | 0.5% | |||
Estonia | 575 | (+249) | 1 | (+1) | 0.2% | |||
Serbia | 457 | (+235) | 1 | (+0) | 0.2% | |||
Lithuania | 358 | (+215) | 5 | (+4) | 1.4% | |||
Ukraine | 310 | (+237) | 5 | (+2) | 1.6% | |||
Hungary | 300 | (+169) | 10 | (+4) | 3.3% | |||
Bulgaria | 293 | (+106) | 3 | (+0) | 1.0% | |||
Latvia | 280 | (+141) | 0 | (+0) | 0.0% | |||
Slovakia | 269 | (+84) | 0 | (+0) | 0.0% | |||
Andorra | 267 | (+154) | 3 | (+2) | 1.1% | |||
Bosnia and Herzegovina | 237 | (+111) | 4 | (+3) | 1.7% | |||
San Marino | 223 | (+48) | 21 | (+1) | 9.4% | |||
North Macedonia | 219 | (+104) | 3 | (+2) | 1.4% | |||
Moldova | 199 | (+105) | 2 | (+1) | 1.0% | |||
Albania | 186 | (+97) | 8 | (+6) | 4.3% | |||
Malta | 139 | (+49) | 0 | (+0) | 0.0% | |||
Belarus | 94 | (+18) | 0 | (+0) | 0.0% | |||
Kosovo | 86 | (+86) | 1 | (+1) | 1.2% | |||
Liechtenstein | 56 | (+19) | 0 | (+0) | 0.0% | |||
Monaco | 42 | (+19) | 0 | (+0) | 0.0% | |||
Holy See | 4 | (+3) | 0 | (+0) | 0.0% |
Visualizations by Pratap Vardhan[^1]
[^1]: Source: "COVID-19 Data Repository by Johns Hopkins CSSE" GitHub repository. Link to notebook, orignal interactive