Code - Import librairies¶

import glob, os    
from datetime import date

# Plotly offline
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

import warnings
warnings.filterwarnings('ignore')

# Import lecture/stockage données
import numpy as np
import pandas as pd

# Visualisation
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
import calmap
import folium
from IPython.core.display import display, HTML

# Palette
colorGrey = '#393e46'
colorRed = '#ff2e63' 
colorCyan = '#21bf73' 
colorYellow = '#fe9801'

Code - Chargement des données¶

# On charge et map l'ensemble des csv en un seul
# dfGlobalDaily = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "../COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/*.csv"))))

# dfGlobalDailyLatest = pd.read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed/" + str(d1) + ".csv")
dfGlobalDailyLatest = pd.read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/03-23-2020.csv")

# Chargement des times series pour les cas confirmés
dfGlobalSeriesConfirmed = pd.read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")

# Chargement des times series pour les cas décédés
dfGlobalSeriesDeaths = pd.read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv")

# Chargement des times series pour les cas soignés
dfGlobalSeriesRecovered = pd.read_csv("../COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv")

Cas Confirmé/Morts/Soignés par pays¶

Code¶

# Cas ACTIVE = cas confirmé - cas décédés - cas soignés
dfGlobalDailyLatest['Active'] = dfGlobalDailyLatest['Confirmed'] - dfGlobalDailyLatest['Deaths'] - dfGlobalDailyLatest['Recovered']
globalAll = dfGlobalDailyLatest.groupby(['Country_Region'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum()

classement = globalAll.sort_values(by='Confirmed', ascending=False)
classement = classement.reset_index(drop=False)
classement.style.background_gradient(cmap='Reds')

Nombre de pays contaminé au virus au cours du temps¶

Code¶

Top 10 pays¶

display(HTML('<div class="flourish-embed flourish-bar-chart-race" data-src="visualisation/1619129"><script src="https://public.flourish.studio/resources/embed.js"></script></div>'))

Décédés¶

fig = px.bar(classement.sort_values('Deaths', ascending=False).head(10).sort_values('Deaths', ascending=True), 
             x="Deaths", y="Country_Region", text='Deaths', orientation='h', 
             width=800, height=500, range_x = [0, max(classement['Deaths'])+500])
fig.update_traces(marker_color=colorRed, opacity=0.6, textposition='outside')
iplot(fig)

Actifs¶

top10Active = px.bar(classement.sort_values('Active', ascending=False).head(10).sort_values('Active', ascending=True), 
             x="Active", y="Country_Region", text='Active', orientation='h', 
             width=800, height=500, range_x = [0, max(classement['Active'])+5000])
top10Active.update_traces(marker_color=colorGrey, opacity=0.6, textposition='outside')
iplot(top10Active)

Confirmés¶

top10Confirmed = px.bar(classement.sort_values('Confirmed', ascending=False).head(10).sort_values('Confirmed', ascending=True), 
             x="Confirmed", y="Country_Region", text='Confirmed', orientation='h', 
             width=800, height=500, range_x = [0, max(classement['Confirmed'])+8000])
top10Confirmed.update_traces(marker_color=colorYellow, opacity=0.6, textposition='outside')
iplot(top10Confirmed)

Soignés¶

top10Recovered = px.bar(classement.sort_values('Recovered', ascending=False).head(10).sort_values('Recovered', ascending=True), 
             x="Recovered", y="Country_Region", text='Recovered', orientation='h', 
             width=800, height=500, range_x = [0, max(classement['Recovered'])+6000])
top10Recovered.update_traces(marker_color=colorCyan, opacity=0.6, textposition='outside')
iplot(top10Recovered)

Comparatif de tout cas confondu¶

# Gestion du tableau des cas confirmés
tmpConfirmed = dfGlobalSeriesConfirmed
tmpConfirmed = tmpConfirmed.drop(columns=['Lat', 'Long'])
tmpConfirmed = tmpConfirmed.groupby(['Country/Region']).sum().reset_index()
tmpConfirmed = tmpConfirmed.drop(columns=['Country/Region'])
tmpConfirmed = tmpConfirmed.sum().reset_index()
tmpConfirmed = tmpConfirmed.rename(columns={'index': 'Date', 0: 'Confirmed'})

# Gestion du tableau des cas décédés
tmpDeaths = dfGlobalSeriesDeaths
tmpDeaths = tmpDeaths.drop(columns=['Lat', 'Long'])
tmpDeaths = tmpDeaths.groupby(['Country/Region']).sum().reset_index()
tmpDeaths = tmpDeaths.drop(columns=['Country/Region'])
tmpDeaths = tmpDeaths.sum().reset_index()
tmpDeaths = tmpDeaths.rename(columns={'index': 'Date', 0: 'Deaths'})

# Gestion du tableau des cas soignés
tmpRecovered = dfGlobalSeriesRecovered
tmpRecovered = tmpRecovered.drop(columns=['Lat', 'Long'])
tmpRecovered = tmpRecovered.groupby(['Country/Region']).sum().reset_index()
tmpRecovered = tmpRecovered.drop(columns=['Country/Region'])
tmpRecovered = tmpRecovered.sum().reset_index()
tmpRecovered = tmpRecovered.rename(columns={'index': 'Date', 0: 'Recovered'})

# On join nos trois tableaux
tmpAll = pd.merge(tmpConfirmed, tmpDeaths, on='Date').merge(tmpRecovered, on='Date')

# On ajoute notre colonne des cas encore malade
tmpAll['Active'] = tmpAll['Confirmed'] - tmpAll['Deaths'] - tmpAll['Recovered']

figAll = tmpAll.melt(id_vars="Date", 
                                 value_vars=['Confirmed', 'Deaths', 'Recovered', 'Active'],
                                value_name='Nombre de cas',
                              var_name='Type')
figFrSeriesAll = px.bar(figAll, 
             x="Date", 
             y="Nombre de cas", 
             color='Type',
             title='Mondial',
             barmode='group',
             color_discrete_sequence=[colorYellow, colorRed, colorCyan, colorGrey])
iplot(figFrSeriesAll)

Taux mortalité pour 100 cas confirmés¶

top10MortalityRate = classement

# Ajout de la nouvelle colonne tauxMortalité = (Morts/Confirmé)*100, arrondi à deux decimals
top10MortalityRate['Mortality Rate'] = round((top10MortalityRate['Deaths']/top10MortalityRate['Confirmed'])*100, 2)

# On prend que les pays qui ont plus de 100 cas confirmés
temp = top10MortalityRate[top10MortalityRate['Confirmed']>100]
temp = temp.sort_values('Mortality Rate', ascending=False)

fig = px.bar(temp.sort_values('Mortality Rate', ascending=False).head(15).sort_values('Mortality Rate', ascending=True), 
             x="Mortality Rate", y="Country_Region", text='Mortality Rate', orientation='h', 
             width=800, height=500, range_x = [0, 12])
fig.update_traces(marker_color=colorRed, opacity=0.6, textposition='outside')
iplot(fig)

Comparatif taux de létalité - France/Chine/Italie¶

fraSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'France') & (dfGlobalSeriesConfirmed['Province/State'] == 'France')]
fraSeriesConfirmed = fraSeriesConfirmed.drop(columns=['Lat', 'Long'])
fraSeriesDeaths = dfGlobalSeriesDeaths[(dfGlobalSeriesDeaths['Country/Region'] == 'France') & (dfGlobalSeriesDeaths['Province/State'] == 'France')]
fraSeriesDeaths = fraSeriesDeaths.drop(columns=['Lat', 'Long'])
fraSeriesAll = pd.concat([fraSeriesConfirmed, fraSeriesDeaths])
fraSeriesAll = fraSeriesAll.drop(columns=['Province/State', 'Country/Region'])
fraSeriesAll = fraSeriesAll.reset_index()
fraSeriesAll = fraSeriesAll.drop(columns=['index'])
fraSeriesAll = fraSeriesAll.transpose()
fraSeriesAll = fraSeriesAll.rename(columns={0: 'Confirmed', 1: 'Deaths'})
fraSeriesAll = fraSeriesAll.reset_index()
fraSeriesAll = fraSeriesAll.rename(columns={'index' : 'Date'})
fraSeriesAll['France'] = round((fraSeriesAll['Deaths']/fraSeriesAll['Confirmed']), 3)*100
#fraSeriesAll = fraSeriesAll.fillna({'Mortality Rate': 0.00})

itaSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'Italy')]
itaSeriesConfirmed = itaSeriesConfirmed.drop(columns=['Lat', 'Long'])
itaSeriesDeaths = dfGlobalSeriesDeaths[(dfGlobalSeriesDeaths['Country/Region'] == 'Italy')]
itaSeriesDeaths = itaSeriesDeaths.drop(columns=['Lat', 'Long'])
itaSeriesAll = pd.concat([itaSeriesConfirmed, itaSeriesDeaths])
itaSeriesAll = itaSeriesAll.drop(columns=['Province/State', 'Country/Region'])
itaSeriesAll = itaSeriesAll.reset_index()
itaSeriesAll = itaSeriesAll.drop(columns=['index'])
itaSeriesAll = itaSeriesAll.transpose()
itaSeriesAll = itaSeriesAll.rename(columns={0: 'Confirmed', 1: 'Deaths'})
itaSeriesAll = itaSeriesAll.reset_index()
itaSeriesAll = itaSeriesAll.rename(columns={'index' : 'Date'})
itaSeriesAll['Italie'] = round((itaSeriesAll['Deaths']/itaSeriesAll['Confirmed']), 3)*100
#itaSeriesAll = itaSeriesAll.fillna({'Mortality Rate': 0.00})


chiSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'China')]
chiSeriesConfirmed = chiSeriesConfirmed.drop(columns=['Lat', 'Long', 'Country/Region', 'Province/State'])
chiSeriesDeaths = dfGlobalSeriesDeaths[(dfGlobalSeriesDeaths['Country/Region'] == 'China')]
chiSeriesDeaths = chiSeriesDeaths.drop(columns=['Lat', 'Long', 'Country/Region', 'Province/State'])

chiSeriesConfirmed = chiSeriesConfirmed.sum().reset_index()
chiSeriesConfirmed = chiSeriesConfirmed.rename(columns={'index': 'Date', 0: 'Confirmed'})

chiSeriesDeaths = chiSeriesDeaths.sum().reset_index()
chiSeriesDeaths = chiSeriesDeaths.rename(columns={'index': 'Date', 0: 'Deaths'})

chiSeriesAll = pd.merge(chiSeriesConfirmed, chiSeriesDeaths, on='Date')
chiSeriesAll['Chine'] = round((chiSeriesAll['Deaths']/chiSeriesAll['Confirmed']), 3)*100


tempAll = pd.merge(chiSeriesAll[['Date', 'Chine']], itaSeriesAll[['Date', 'Italie']], on='Date').merge(fraSeriesAll[['Date', 'France']], on='Date')
#tempAll = tempAll.fillna(0.0)

tempAll = tempAll.melt(id_vars='Date', value_vars=['Chine', 'France', 'Italie'], 
                 var_name='Pays', value_name='Taux')
fig = px.line(tempAll, x="Date", y="Taux", color='Pays', log_y=True, 
              title='Taux de létalité', color_discrete_sequence=['#d21629', '#162cd2', '#008000'])
iplot(fig)

Comparatif taux de guerison - France/Chine/Italie¶

fraSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'France') & (dfGlobalSeriesConfirmed['Province/State'] == 'France')]
fraSeriesConfirmed = fraSeriesConfirmed.drop(columns=['Lat', 'Long'])
fraSeriesRecovered = dfGlobalSeriesRecovered[(dfGlobalSeriesRecovered['Country/Region'] == 'France') & (dfGlobalSeriesRecovered['Province/State'] == 'France')]
fraSeriesRecovered = fraSeriesRecovered.drop(columns=['Lat', 'Long'])
fraSeriesAll = pd.concat([fraSeriesConfirmed, fraSeriesRecovered])
fraSeriesAll = fraSeriesAll.drop(columns=['Province/State', 'Country/Region'])
fraSeriesAll = fraSeriesAll.reset_index()
fraSeriesAll = fraSeriesAll.drop(columns=['index'])
fraSeriesAll = fraSeriesAll.transpose()
fraSeriesAll = fraSeriesAll.rename(columns={0: 'Confirmed', 1: 'Recovered'})
fraSeriesAll = fraSeriesAll.reset_index()
fraSeriesAll = fraSeriesAll.rename(columns={'index' : 'Date'})
cmp10Fra = fraSeriesAll
fraSeriesAll['France'] = round((fraSeriesAll['Recovered']/fraSeriesAll['Confirmed']), 3)*100
#fraSeriesAll = fraSeriesAll.fillna({'Mortality Rate': 0.00})

itaSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'Italy')]
itaSeriesConfirmed = itaSeriesConfirmed.drop(columns=['Lat', 'Long'])
itaSeriesRecovered = dfGlobalSeriesRecovered[(dfGlobalSeriesRecovered['Country/Region'] == 'Italy')]
itaSeriesRecovered = itaSeriesRecovered.drop(columns=['Lat', 'Long'])
itaSeriesAll = pd.concat([itaSeriesConfirmed, itaSeriesDeaths])
itaSeriesAll = itaSeriesAll.drop(columns=['Province/State', 'Country/Region'])
itaSeriesAll = itaSeriesAll.reset_index()
itaSeriesAll = itaSeriesAll.drop(columns=['index'])
itaSeriesAll = itaSeriesAll.transpose()
itaSeriesAll = itaSeriesAll.rename(columns={0: 'Confirmed', 1: 'Recovered'})
itaSeriesAll = itaSeriesAll.reset_index()
itaSeriesAll = itaSeriesAll.rename(columns={'index' : 'Date'})
cmp10Ita = itaSeriesAll
itaSeriesAll['Italie'] = round((itaSeriesAll['Recovered']/itaSeriesAll['Confirmed']), 3)*100
#itaSeriesAll = itaSeriesAll.fillna({'Mortality Rate': 0.00})


chiSeriesConfirmed = dfGlobalSeriesConfirmed[(dfGlobalSeriesConfirmed['Country/Region'] == 'China')]
chiSeriesConfirmed = chiSeriesConfirmed.drop(columns=['Lat', 'Long', 'Country/Region', 'Province/State'])
chiSeriesRecovered = dfGlobalSeriesRecovered[(dfGlobalSeriesRecovered['Country/Region'] == 'China')]
chiSeriesRecovered = chiSeriesRecovered.drop(columns=['Lat', 'Long', 'Country/Region', 'Province/State'])

chiSeriesConfirmed = chiSeriesConfirmed.sum().reset_index()
chiSeriesConfirmed = chiSeriesConfirmed.rename(columns={'index': 'Date', 0: 'Confirmed'})

chiSeriesRecovered = chiSeriesRecovered.sum().reset_index()
chiSeriesRecovered = chiSeriesRecovered.rename(columns={'index': 'Date', 0: 'Recovered'})

chiSeriesAll = pd.merge(chiSeriesConfirmed, chiSeriesRecovered, on='Date')
cmp10Chi = chiSeriesAll
chiSeriesAll['Chine'] = round((chiSeriesAll['Recovered']/chiSeriesAll['Confirmed']), 3)*100


tempAll = pd.merge(chiSeriesAll[['Date', 'Chine']], itaSeriesAll[['Date', 'Italie']], on='Date').merge(fraSeriesAll[['Date', 'France']], on='Date')
#tempAll = tempAll.fillna(0.0)

tempAll = tempAll.melt(id_vars='Date', value_vars=['Chine', 'France', 'Italie'], 
                 var_name='Pays', value_name='Taux')
fig = px.line(tempAll, x="Date", y="Taux", color='Pays', log_y=True, 
              title='Taux de guérison', color_discrete_sequence=['#d21629', '#162cd2', '#008000'])
iplot(fig)

Courbe comparative des évolutions des cas - Chine/France/Italie¶

On compare les données à partir de 10 cas positifs

Cas confirmé¶

cmp10Ita = itaSeriesConfirmed
cmp10Ita = cmp10Ita.drop(columns=['Province/State', 'Country/Region'])
cmp10Ita = cmp10Ita.transpose()
cmp10Ita = cmp10Ita.rename(columns={16: 'Italie'})
cmp10Ita = cmp10Ita[(cmp10Ita['Italie'] > 9)]
cmp10Ita.index = pd.RangeIndex(len(cmp10Ita.index))

cmp10Fra = fraSeriesConfirmed
cmp10Fra = cmp10Fra.drop(columns=['Province/State', 'Country/Region'])
cmp10Fra = cmp10Fra.transpose()
cmp10Fra = cmp10Fra.rename(columns={157: 'France'})
cmp10Fra = cmp10Fra[(cmp10Fra['France'] > 9)]
cmp10Fra.index = pd.RangeIndex(len(cmp10Fra.index))

cmp10Chi = chiSeriesConfirmed

cmp10Chi = cmp10Chi.rename(columns={'Confirmed': "Chine"})
cmp10Chi = cmp10Chi[(cmp10Chi['Chine'] > 9)]
cmp10Chi.index = pd.RangeIndex(len(cmp10Chi.index))


cmp10Global = pd.concat([cmp10Chi, cmp10Fra, cmp10Ita], axis=1)
cmp10Global = cmp10Global.reset_index()
cmp10Global = cmp10Global.rename(columns={'index': "Jours"})
cmp10GlobalShifted = cmp10Global

cmp10Global = cmp10Global.melt(id_vars='Jours', value_vars=['Chine', 'France', 'Italie'], 
                 var_name='Legende', value_name='Value')

iplot(px.line(cmp10Global, x="Jours", y="Value", color='Legende', log_y=True, 
              title='Evolution de l\'épidemie à partir de 10 cas infectés' , color_discrete_sequence=[colorCyan, colorRed, colorYellow]))

Shift données France¶

En decalant les données de la france de 17 jours, on peut apercevoir que l'on suit exactement la même courbe de l'évolution de cas infectés de l'Italie

Si on décale aussi la courbe de l'Italie, celle-ci aussi se rapproche sensiblement de l'évolution qu'a connu la Chine

tmp10GlobalShifted = cmp10GlobalShifted.copy()
tmp10GlobalShifted.France = tmp10GlobalShifted.France.shift(-17)

fig10GlobalShifted = tmp10GlobalShifted.melt(id_vars='Jours', value_vars=['Chine', 'France', 'Italie'], 
                 var_name='Legende', value_name='Value')

iplot(px.line(fig10GlobalShifted, x="Jours", y="Value", color='Legende', log_y=True, 
              title='Evolution de l\'épidemie à partir de 10 cas infectés' , color_discrete_sequence=[colorCyan, colorRed, colorYellow]))

source data : https://github.com/CSSEGISandData/COVID-19

	Country_Region	Confirmed	Deaths	Recovered	Active
0	China	81496	3274	72819	5403
1	Italy	63927	6077	7432	50418
2	US	43667	552	0	43115
3	Spain	35136	2311	3355	29470
4	Germany	29056	123	453	28480
5	Iran	23049	1812	8376	12861
6	France	20123	862	2207	17054
7	Korea, South	8961	111	3166	5684
8	Switzerland	8795	120	131	8544
9	United Kingdom	6726	336	140	6250
10	Netherlands	4764	214	3	4547
11	Austria	4474	21	9	4444
12	Belgium	3743	88	401	3254
13	Norway	2621	10	6	2605
14	Canada	2088	25	0	2063
15	Portugal	2060	23	14	2023
16	Sweden	2046	25	16	2005
17	Brazil	1924	34	2	1888
18	Australia	1682	7	119	1556
19	Denmark	1572	24	24	1524
20	Turkey	1529	37	0	1492
21	Malaysia	1518	14	159	1345
22	Israel	1442	1	41	1400
23	Czechia	1236	1	7	1228
24	Japan	1128	42	235	851
25	Ireland	1125	6	5	1114
26	Ecuador	981	18	3	960
27	Luxembourg	875	8	6	861
28	Pakistan	875	6	13	856
29	Poland	749	8	13	728
30	Chile	746	2	11	733
31	Thailand	721	1	52	668
32	Cruise Ship	712	8	567	137
33	Finland	700	1	10	689
34	Greece	695	17	29	649
35	Iceland	588	1	51	536
36	Indonesia	579	49	30	500
37	Romania	576	7	73	496
38	Saudi Arabia	562	0	19	543
39	Singapore	509	2	152	355
40	Qatar	501	0	33	468
41	India	499	10	34	455
42	Philippines	462	33	18	411
43	Slovenia	442	3	0	439
44	Russia	438	1	17	420
45	South Africa	402	0	4	398
46	Peru	395	5	1	389
47	Bahrain	377	2	164	211
48	Egypt	366	19	68	279
49	Estonia	352	0	4	348
50	Mexico	316	3	4	309
51	Croatia	315	1	5	309
52	Panama	313	3	1	309
53	Colombia	277	3	3	271
54	Lebanon	267	4	8	255
55	Iraq	266	23	62	181
56	Argentina	266	4	27	235
57	Serbia	249	3	3	243
58	Dominican Republic	245	3	3	239
59	Armenia	235	0	2	233
60	Algeria	230	17	65	148
61	Bulgaria	201	3	3	195
62	United Arab Emirates	198	2	41	155
63	Taiwan*	195	2	28	165
64	Kuwait	189	0	30	159
65	San Marino	187	20	4	163
66	Slovakia	186	0	7	179
67	Latvia	180	0	1	179
68	Lithuania	179	1	1	177
69	Hungary	167	7	16	144
70	Uruguay	158	0	0	158
71	Costa Rica	158	2	2	154
72	Morocco	143	4	5	134
73	North Macedonia	136	2	1	133
74	Andorra	133	1	1	131
75	Bosnia and Herzegovina	132	1	2	129
76	Jordan	127	0	1	126
77	Vietnam	123	0	17	106
78	Cyprus	116	1	3	112
79	Moldova	109	1	2	106
80	Malta	107	0	2	105
81	Albania	104	4	2	98
82	New Zealand	102	0	0	102
83	Burkina Faso	99	4	5	90
84	Sri Lanka	97	0	2	95
85	Brunei	91	0	2	89
86	Tunisia	89	3	1	85
87	Cambodia	87	0	2	85
88	Belarus	81	0	22	59
89	Senegal	79	0	8	71
90	Venezuela	77	0	15	62
91	Ukraine	73	3	1	69
92	Azerbaijan	72	1	10	61
93	Oman	66	0	17	49
94	Kazakhstan	62	0	0	62
95	Georgia	61	0	8	53
96	Cameroon	56	0	2	54
97	Liechtenstein	51	0	0	51
98	Trinidad and Tobago	51	0	0	51
99	Uzbekistan	46	0	0	46
100	Nigeria	40	1	2	37
101	Afghanistan	40	1	1	38
102	Cuba	40	1	0	39
103	Mauritius	36	2	0	34
104	Rwanda	36	0	0	36
105	Congo (Kinshasa)	36	1	0	35
106	Bangladesh	33	3	5	25
107	Bolivia	27	0	0	27
108	Ghana	27	2	0	25
109	Honduras	27	0	0	27
110	Montenegro	27	1	0	26
111	Cote d'Ivoire	25	0	2	23
112	Monaco	23	0	1	22
113	Paraguay	22	1	0	21
114	Guatemala	20	1	0	19
115	Guyana	20	1	0	19
116	Jamaica	19	1	2	16
117	Togo	18	0	0	18
118	Barbados	17	0	0	17
119	Kyrgyzstan	16	0	0	16
120	Kenya	16	0	0	16
121	Maldives	13	0	5	8
122	Tanzania	12	0	0	12
123	Madagascar	12	0	0	12
124	Ethiopia	11	0	0	11
125	Mongolia	10	0	0	10
126	Uganda	9	0	0	9
127	Equatorial Guinea	9	0	0	9
128	Seychelles	7	0	0	7
129	Haiti	6	0	0	6
130	Benin	5	0	0	5
131	Suriname	5	0	0	5
132	Gabon	5	1	0	4
133	Congo (Brazzaville)	4	0	0	4
134	Bahamas	4	0	0	4
135	Eswatini	4	0	0	4
136	Guinea	4	0	0	4
137	Namibia	4	0	0	4
138	Fiji	3	0	0	3
139	Zambia	3	0	0	3
140	Angola	3	0	0	3
141	Antigua and Barbuda	3	0	0	3
142	Niger	3	0	0	3
143	Cabo Verde	3	0	0	3
144	Central African Republic	3	0	0	3
145	Liberia	3	0	0	3
146	Zimbabwe	3	1	0	2
147	El Salvador	3	0	0	3
148	Saint Lucia	3	0	0	3
149	Djibouti	3	0	0	3
150	Bhutan	2	0	0	2
151	Dominica	2	0	0	2
152	Mauritania	2	0	0	2
153	Gambia	2	1	0	1
154	Sudan	2	1	0	1
155	Nicaragua	2	0	0	2
156	Nepal	2	0	1	1
157	Saint Vincent and the Grenadines	1	0	0	1
158	Somalia	1	0	0	1
159	Mozambique	1	0	0	1
160	Grenada	1	0	0	1
161	Belize	1	0	0	1
162	Syria	1	0	0	1
163	Eritrea	1	0	0	1
164	Chad	1	0	0	1
165	Holy See	1	0	0	1
166	Papua New Guinea	1	0	0	1
167	Timor-Leste	1	0	0	1

Covid-19 - Données MONDIAL

Actualisé tout les soirs à 21h

Code - Import librairies¶

Code - Chargement des données¶

Cas Confirmé/Morts/Soignés par pays¶

Code¶

Nombre de pays contaminé au virus au cours du temps¶

Code¶

Top 10 pays¶

Décédés¶

Actifs¶

Confirmés¶

Soignés¶

Comparatif de tout cas confondu¶

Taux mortalité pour 100 cas confirmés¶

Comparatif taux de létalité - France/Chine/Italie¶

Comparatif taux de guerison - France/Chine/Italie¶

Courbe comparative des évolutions des cas - Chine/France/Italie¶

Cas confirmé¶

Shift données France¶