def recap_world(dt, previous):
previous = (pd.to_datetime(dt, dayfirst=True) -
pd.to_datetime(previous, dayfirst=True)).days
if dt == time.strftime('%d/%m/%Y'):
dt = (datetime.now() - timedelta(1)).strftime('%d/%m/%Y')
df_recap = GetData.get_recap_by_country(dt, previous=int(previous))
df_confirmed = GetData.get_world('confirmed')
confirmed = df_confirmed.iloc[:, -1].sum()
confirmedp = df_recap['Cases (+)'].sum(axis=0)
df_deaths = GetData.get_world('deaths')
deaths = df_deaths.iloc[:, -1].sum()
deathsp = df_recap['Deaths (+)'].sum(axis=0)
df_recovered = GetData.get_world('recovered')
recovered = df_recovered.iloc[:, -1].sum()
recoveredp = df_recap['Recovered (+)'].sum(axis=0)
france_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="France"]):,}'
france_casesp2 = "{0}".format(france_casesp)
us_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="US"]):,}'
us_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="US"]):,}'
france_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="France"]):,}'
italy_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="Italy"]):,}'
italy_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="Italy"]):,}'
germany_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="Germany"]):,}'
germany_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="Germany"]):,}'
china_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="China"]):,}'
china_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="China"]):,}'
spain_cases = f'{int(df_recap["Cases"][df_recap["Country/Region"]=="Spain"]):,}'
spain_casesp = f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="Spain"]):,}'
return '{:,}'.format(confirmed),'(+ {:,})'.format(confirmedp),'{:,}'.format(deaths),'(+ {:,})'.format(deathsp),'{:,}'.format(recovered),'(+ {:,})'.format(recoveredp),\
'Mise à jour le {} (+ Variations sur les {} derniers jours)'.format(pd.to_datetime(dt).strftime('%d/%m/%Y'),previous),'{:,}'.format(previous),'{:,}'.format(recoveredp),'{}'.format(france_casesp2),\
'{}'.format(china_cases),'(+ {})'.format(china_casesp),'{}'.format(italy_cases),'(+ {})'.format(italy_casesp),'{}'.format(germany_cases),'(+ {})'.format(germany_casesp),\
'{}'.format(spain_cases),'(+ {})'.format(spain_casesp),'{}'.format(us_cases),'(+ {})'.format(us_casesp),'{}'.format(france_cases),'(+ {})'.format(france_casesp2)
def get_top3_country(name):
index_top3 = GetData.get_world(name).groupby('Country/Region').sum().iloc[:,2:].T.iloc[-1].sort_values(ascending = False).index[0:3].values
df_confirmed_world = GetData.get_world('confirmed').groupby('Country/Region').sum().iloc[:,2:].T
df_deaths_world = GetData.get_world('deaths').groupby('Country/Region').sum().iloc[:,2:].T
df_recovered_world = GetData.get_world('recovered').groupby('Country/Region').sum().iloc[:,2:].T
df = pd.DataFrame()
df['Confirmed'] = df_confirmed_world[index_top3].iloc[-1,:]
df['Recovered'] = df_recovered_world[index_top3].iloc[-1,:]
df['Deaths'] = df_deaths_world[index_top3].iloc[-1,:]
return df
def recap_table(dt=dt,previous=5):
df_recap=GetData.get_recap_by_country(dt,previous=previous)
columns = ['Pays','Nouveaux Cas','Total des cas','Total décès','Nouveau décès','Mortalité','Rétabli']
df_H5=pd.DataFrame(columns=columns)
df_H5["Pays"]=x = ['{0}'.format(i) for i in df_recap["Country/Region"]]
df_H5["Total des cas"]=df_recap["Cases"]
df_H5["Total décès"]=df_recap["Deaths"]
df_H5["Mortalité"]=df_H5["Total décès"]/df_H5["Total des cas"]
df_H5["Total décès"]=df_recap["Deaths"]
df_H5["Rétabli"]=df_recap["Recovered"]
df_H5["Nouveaux cas"]=[f'{i:,}'for i in df_recap["Cases (+)"]]
df_H5["Nouveaux cas"]=["(+{0})".format(str(i)) for i in df_H5["Nouveaux cas"]]
df_H5["Nouveau décès"]=[f'{i:,}'for i in df_recap['Deaths (+)']]
df_H5["Nouveau décès"]=["(+{0})".format(str(i)) for i in df_H5["Nouveau décès"]]
df_H5["Mortalité"]=["{:.2%}".format(i) for i in df_H5["Mortalité"]]
df_H5["Rétabli"]=[f'{i:,}'for i in df_H5["Rétabli"]]
df_H5["Total des cas"]=[f'{i:,}'for i in df_H5["Total des cas"]]
df_H5["Total décès"]=[f'{i:,}'for i in df_H5["Total décès"]]
rows =[]
for index,row in df_H5.head(10).iterrows():
rows.append(html.Tr([html.Td(row.Pays,style={'font-weight':'bold','text-align':'center'}),
html.Td(row['Total des cas']),html.Td(row['Total décès']),html.Td(row['Mortalité'])],style={
'border-bottom':'1px solid #e8e8e8'
}))
return [
html.Thead([
html.Tr(
[
html.Th(
'COuntry',style={'text-align':'center','width':'180px'}
),
html.Th(
'CASES',style ={
'text-align':'center',
}
),
html.Th(
'DEATHS',style={
'text-align':'center',
}
),
html.Th(
'MORTALITY',style={
'text-align':'center',
}
),
],
style = {
'border-bottom': '2px solid',
'text-transform':'uppercase',
"font-size":"14px"
}
)
]),
html.Tbody(
rows
)
]
def updatelabel3(valued,valuep):
previous=int(valuep)
date=valued
df_recap=GetData.get_recap_by_country(date,previous=previous)
deathsp=df_recap['Deaths (+)'].sum(axis=0)
france_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="France"]):,}'
france_deathsp2="{0}".format(france_deathsp)
return "In the last **{0}** days, **{1}** new Coronavirus deaths have been \
reported worldwide. Of which **{2}** are from France.".format(\
previous, deathsp, france_deathsp2)
def updatelabel2(valued,valuep):
previous=int(valuep)
date=valued
df_recap=GetData.get_recap_by_country(date,previous=previous)
#Récupération des cas confirmés
confirmedp=df_recap['Cases (+)'].sum(axis=0)
france_casesp=f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="France"]):,}'
france_casesp2="{0}".format(france_casesp)
return "In the last **{0}** days, **{1}** new Coronavirus cases have been \
reported worldwide. Of which **{2}** are from France.".format(\
valuep, confirmedp, france_casesp2)
def recap_table(dt, previous):
previous = (pd.to_datetime(dt, dayfirst=True) -
pd.to_datetime(previous, dayfirst=True)).days
df_recap = GetData.get_recap_by_country(dt, previous=previous)
columns = [
'Pays', 'Nouveaux Cas', 'Total des cas', 'Total décès',
'Nouveau décès', 'Mortalité', 'Rétabli'
]
df_H5 = pd.DataFrame(columns=columns)
df_H5["Pays"] = x = ['{0}'.format(i) for i in df_recap["Country/Region"]]
df_H5["Total des cas"] = df_recap["Cases"]
df_H5["Total décès"] = df_recap["Deaths"]
df_H5["Mortalité"] = df_H5["Total décès"] / df_H5["Total des cas"]
df_H5["Total décès"] = df_recap["Deaths"]
df_H5["Rétabli"] = df_recap["Recovered"]
df_H5["Nouveaux cas"] = [f'{i:,}' for i in df_recap["Cases (+)"]]
df_H5["Nouveaux cas"] = [
"(+{0})".format(str(i)) for i in df_H5["Nouveaux cas"]
]
df_H5["Nouveau décès"] = [f'{i:,}' for i in df_recap['Deaths (+)']]
df_H5["Nouveau décès"] = [
"(+{0})".format(str(i)) for i in df_H5["Nouveau décès"]
]
df_H5["Mortalité"] = ["{:.2%}".format(i) for i in df_H5["Mortalité"]]
df_H5["Rétabli"] = [f'{i:,}' for i in df_H5["Rétabli"]]
df_H5["Total des cas"] = [f'{i:,}' for i in df_H5["Total des cas"]]
df_H5["Total décès"] = [f'{i:,}' for i in df_H5["Total décès"]]
rows = []
for index, row in df_H5.iterrows():
rows.append(
html.Tr([
html.Td(row.Pays,
style={
'font-weight': 'bold',
'text-align': 'right'
}),
html.Td(make_bars(row.Pays),
style={'vertical-align': 'middle'}),
html.Td(row['Total des cas'], style={'font-weight': 'bolder'}),
html.Td(row['Nouveaux cas'], style=CSS['danger']),
html.Td(row['Total décès']),
html.Td(row['Nouveau décès'], style=CSS['danger']),
html.Td(row['Mortalité']),
html.Td(row['Rétabli'])
],
style={'border-bottom': '1px solid #e8e8e8'}))
return [
html.Thead([
html.Tr([
html.Th(children='', style={'width': '150px'}),
html.Th(
[
html.Div(children='10',
style={
"width": "27px",
"height": "15px",
"font-size": "8px"
}),
html.Div(children='100',
style={
"width": "27px",
"height": "15px",
"font-size": "8px"
}),
html.Div(children='1000',
style={
"width": "27px",
"height": "15px",
"font-size": "8px"
}),
],
style={
'width': '170px',
"height": "15px",
'vertical-align': 'middle',
"display": "flex",
"margin-left": "85px"
}),
html.Th()
]),
html.Tr([
html.Th(children='', style={'width': '150px'}),
html.Th(
[
html.Div(children='',
style={
"background": "rgba(255, 152, 0,0.1)",
"width": "27px",
"height": "15px"
}),
html.Div(children='',
style={
"background": "rgba(255, 152, 0,0.4)",
"width": "27px",
"height": "15px"
}),
html.Div(children='',
style={
"background": "rgba(255, 152, 0,0.7)",
"width": "27px",
"height": "15px"
}),
html.Div(children='',
style={
"background": "rgba(255, 152, 1)",
"width": "27px",
"height": "15px"
}),
],
style={
'width': '170px',
"height": "15px",
'vertical-align': 'middle',
"display": "flex",
"margin-left": "50px"
}),
html.Th()
]),
html.Tr(
[
html.Th('Pays',
style={
'text-align': 'right',
'width': '180px'
}),
html.Th('Evolution des cas',
style={
'text-align': 'center',
'width': '170px'
}),
html.Th('Total des cas',
style={
'text-align': 'center',
'width': '250px'
}),
html.Th('Nouveaux cas',
style={
'text-align': 'center',
'width': '250px'
}),
html.Th('Total décès',
style={
'text-align': 'center',
'width': '250px'
}),
html.Th('Nouveaux décès',
style={
'text-align': 'center',
'width': '300px'
}),
html.Th('Mortalité',
style={
'text-align': 'center',
'width': '200px'
}),
html.Th('Rétablis',
style={
'text-align': 'center',
'width': '200px'
})
],
style={
'border-bottom': '2px solid',
'text-transform': 'uppercase',
"font-size": "14px"
})
]),
html.Tbody([
html.Tr([
html.Td(),
html.Td(
[html.Div('22 Janvier'),
html.Div("Aujourd'hui")],
style={
'display': 'flex',
'justify-content': 'space-between',
'font-size': '9px'
}),
html.Td(),
html.Td('( + NOUVEAU ) depuis le {}'.format(
(pd.to_datetime(dt, dayfirst=True) -
timedelta(previous)).strftime('%d/%m/%Y')),
style={
"font-size": "9px",
'color': '#999',
'text-align': 'left'
}),
html.Td(),
html.Td()
])
] + rows)
]
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from apps import GetData
from apps import graph
import dash_bootstrap_components as dbc
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import time
from app import app
from dash.dependencies import Input, Output
from datetime import datetime, timedelta
app.title = 'CoronaRecap'
df = GetData.get_world('confirmed')
def make_bars(country, df=df):
t = df[(df['Country/Region'] == country)].groupby('Country/Region').sum()
lst = [
list(t[col].values)[0] for col in t.columns if col not in
['Continent', 'Country/Region', 'Province/State', 'Lat', 'Long']
]
variation = [lst[i] - lst[i - 1] for i in range(1, len(lst))]
color = {
'0': {
'border-right': '1px solid rgba(255,255,255,0.5)',
'width': '3px',
},
).iloc[:, 2:].T.iloc[-1].sort_values(ascending=False).index[0:3].values
df_confirmed_world = GetData.get_world('confirmed').groupby(
'Country/Region').sum().iloc[:, 2:].T
df_deaths_world = GetData.get_world('deaths').groupby(
'Country/Region').sum().iloc[:, 2:].T
df_recovered_world = GetData.get_world('recovered').groupby(
'Country/Region').sum().iloc[:, 2:].T
df = pd.DataFrame()
df['Confirmed'] = df_confirmed_world[index_top3].iloc[-1, :]
df['Recovered'] = df_recovered_world[index_top3].iloc[-1, :]
df['Deaths'] = df_deaths_world[index_top3].iloc[-1, :]
return df
top3 = get_top3_country('confirmed')
df = GetData.get_world('confirmed')
def make_bars(country, df=df):
t = df[(df['Country/Region'] == country)].groupby('Country/Region').sum()
lst = [
list(t[col].values)[0] for col in t.columns if col not in
['Continent', 'Country/Region', 'Province/State', 'Lat', 'Long']
]
variation = [lst[i] - lst[i - 1] for i in range(1, len(lst))]
color = {
'0': {
'border-right': '1px solid rgba(255,255,255,0.5)',
'width': '3px',
},
from dash.dependencies import Input, Output, State
import numpy as np
import pandas as pd
from datetime import datetime
from pytrends.request import TrendReq
import plotly.graph_objects as go
from plotly.offline import plot
from app import app
from apps import GetData
from apps import sidebar
import yfinance as yf
import plotly.express as px
df_confirmed_world = GetData.get_world('confirmed')
df_deaths_world = GetData.get_world('deaths')
df = pd.DataFrame()
df['Date'] = pd.to_datetime(df_confirmed_world.iloc[:,5:].columns)
df['Confirmed'] = df_confirmed_world.iloc[:,5:].sum().values
df['Deaths'] = df_deaths_world.iloc[:,5:].sum().values
df.set_index('Date', inplace = True)
list_tickers = ['^FCHI', '^GSPC', '^DJI', '^GDAXI', '^IXIC', '^N225', '^HSI', '^IBEX', 'BTC-USD', 'ETHUSD=X', 'EURUSD=X', 'EURGBP=X', 'EURJPY=X', 'EURCNY=X', 'EURCHF=X',
'HG=F', 'EH=F', 'GC=F', 'NG=F', 'CL=F', 'PL=F', 'SI=F']
title = ['CAC40', 'SP500', 'Dow Jones', 'Dax', 'Nasdaq', 'Nikkei', 'Hangseng', 'Ibex', 'BTC/USD', 'ETH/USD', 'EUR/USD', 'EUR/GBP', 'EUR/JPY', 'EUR/CNY', 'EUR/CHF', 'Copper', 'Ethanol',
'Gold', 'Natural Gas', 'Oil', 'Platinum', 'Silver']
title = np.array(title)
def updatefigure3(valued,valuep):
previous=int(valuep)
date=valued
df_recap=GetData.get_recap_by_country(date,previous=previous)
fill_color_H2='lightgray'
line_color_H2='lightgray'
font_color_H2=[['black','red']]
font_size_H2=[12,11,11,11,11,11,11]
columns_=["Country", "New Cases", "Total Cases","New Deaths","Total Deaths","Fatality", "Recovered"]
columns_=['<b>{0}</b>'.format(i) for i in columns_]
df_H5=pd.DataFrame(columns=["Country", "New Cases", "Total Cases","New Deaths","Total Deaths","Fatality", "Recovered"])
df_H5["Country"]=x = ['<b>{0}</b>'.format(i) for i in df_recap["Country/Region"]]
df_H5["Total Cases"]=df_recap["Cases"]
df_H5["Total Deaths"]=df_recap["Deaths"]
df_H5["Fatality"]=df_H5["Total Deaths"]/df_H5["Total Cases"]
df_H5["Total Deaths"]=df_recap["Deaths"]
df_H5["Recovered"]=df_recap["Recovered"]
df_H5["New Cases"]=[f'{i:,}'for i in df_recap["Cases (+)"]]
df_H5["New Cases"]=["(+{0})".format(str(i)) for i in df_H5["New Cases"]]
df_H5["New Deaths"]=[f'{i:,}'for i in df_recap['Deaths (+)']]
df_H5["New Deaths"]=["(+{0})".format(str(i)) for i in df_H5["New Deaths"]]
df_H5["Fatality"]=["{:.2%}".format(i) for i in df_H5["Fatality"]]
df_H5["Recovered"]=[f'{i:,}'for i in df_H5["Recovered"]]
df_H5["Total Cases"]=[f'{i:,}'for i in df_H5["Total Cases"]]
df_H5["Total Deaths"]=[f'{i:,}'for i in df_H5["Total Deaths"]]
font_color_H5=['black','red','black','red','black','red','black']
fig_H5 = go.Figure(go.Table(
header=dict(values=list(columns_),
align ='center', font=dict(color='black',size=12),
line_color=line_color_H2,
fill_color=fill_color_H2),
cells=dict(values=[df_H5.Country, df_H5["New Cases"],df_H5["Total Cases"]\
, df_H5["New Deaths"], df_H5["Total Deaths"],\
df_H5.Fatality, df_H5.Recovered],
font_size=font_size_H2,font_color=font_color_H5,
line_color=line_color_H2,fill_color=fill_color_H2)))
fig_H5.update_layout(
autosize=False,
width=1000,
height=400,
margin=dict(
l=300,
r=20,
b=100,
t=50,
pad=400
),
title_text="<b>BY COUNTRY</b>",title_x=0.63,
title_font_color='black'
)
return fig_H5
def updatefigure3(valued,valuep):
previous=int(valuep)
date=valued
df_recap=GetData.get_recap_by_country(date,previous=previous)
fill_color_H2='lightgray'
line_color_H2='lightgray'
font_color_H2=[['black','red']]
#USA
us_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="US"]):,}'
us_deaths="<b>{0}</b>".format(us_deaths)
us_casesp=f'{int(df_recap["Cases (+)"][df_recap["Country/Region"]=="US"]):,}'
us_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="US"]):,}'
us_casesp="<b>(+{0})</b>".format(us_casesp)
us_deathsp="<b>(+{0})</b>".format(us_deathsp)
#France
france_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="France"]):,}'
france_deaths="<b>{0}</b>".format(france_deaths)
france_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="France"]):,}'
france_deathsp="<b>(+{0})</b>".format(france_deathsp)
#Italy
italy_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="Italy"]):,}'
italy_deaths="<b>{0}</b>".format(italy_deaths)
italy_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="Italy"]):,}'
italy_deathsp="<b>(+{0})</b>".format(italy_deathsp)
#Germany
germany_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="Germany"]):,}'
germany_deaths="<b>{0}</b>".format(germany_deaths)
germany_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="Germany"]):,}'
germany_deathsp="<b>(+{0})</b>".format(germany_deathsp)
#China
china_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="China"]):,}'
china_deaths="<b>{0}</b>".format(china_deaths)
china_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="China"]):,}'
china_deathsp="<b>(+{0})</b>".format(china_deathsp)
#Spain
spain_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="Spain"]):,}'
spain_deaths="<b>{0}</b>".format(spain_deaths)
spain_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="Spain"]):,}'
spain_deathsp="<b>(+{0})</b>".format(spain_deathsp)
#Iran
iran_deaths=f'{int(df_recap["Deaths"][df_recap["Country/Region"]=="Iran"]):,}'
iran_deaths="<b>{0}</b>".format(iran_deaths)
iran_deathsp=f'{int(df_recap["Deaths (+)"][df_recap["Country/Region"]=="Iran"]):,}'
iran_deathsp="<b>(+{0})</b>".format(iran_deathsp)
values_H4=[[china_deaths,china_deathsp],[france_deaths,france_deathsp],\
[germany_deaths,germany_deathsp],[us_deaths,us_deathsp],\
[spain_deaths,spain_deathsp], [iran_deaths,iran_deathsp],\
[italy_deaths,italy_deathsp]]
fig_H4= go.Figure(data=[go.Table(
header=dict(values=["China","France", "Germany"\
,"US","Spain","Iran"\
,"Italy"]
,
line_color=line_color_H2,fill_color=fill_color_H2,
align='center',font=dict(color='black', size=10)
),
cells=dict(
line_color=line_color_H2,fill_color=fill_color_H2,font_color=font_color_H2,
values=values_H4, font_size=13
))
])
fig_H4.update_layout(
autosize=False,
width=1000,
height=170,
margin=dict(
l=300,
r=20,
b=0,
t=40,
pad=20
),
title_text="<b>DEATHS</b>",title_x=0.63,
title_font_color='black'
)
return fig_H4
def updatefigure1(valued,valuep):
previous=int(valuep)
date=valued
df_recap=GetData.get_recap_by_country(date,previous=previous)
#Récupération des cas confirmés
df_confirmed=GetData.get_world('confirmed')
confirmed=df_confirmed.iloc[:,5:].sum(axis=1).sum(axis=0)
confirmedp=df_recap['Cases (+)'].sum(axis=0)
#Récupération des cas morts
df_deaths=GetData.get_world('deaths')
deaths=df_deaths.iloc[:,5:].sum(axis=1).sum(axis=0)
deathsp=df_recap['Deaths (+)'].sum(axis=0)
#Récupération des cas guéris
df_recovered=GetData.get_world('recovered')
recovered=df_recovered.iloc[:,5:].sum(axis=1).sum(axis=0)
recoveredp=df_recap['Recovered (+)'].sum(axis=0)
confirmed=f'{confirmed:,}'
deaths=f'{deaths:,}'
recovered=f'{recovered:,}'
confirmedp=f'{confirmedp:,}'
deathsp=f'{deathsp:,}'
recoveredp=f'{recoveredp:,}'
a="<b>{0}</b>".format(confirmed)
b="<b>{0}</b>".format(deaths)
c="<b>{0}</b>".format(recovered)
e="<b>(+{0})</b>".format(confirmedp)
f="<b>(+{0})</b>".format(deathsp)
g="<b>(+{0})</b>".format(recoveredp)
#CSS
fill_color_H2='lightgray'
line_color_H2='lightgray'
font_color_H2=[['black','red']]
font_size_H2=[15]
#layout = go.Layout( autosize=True, **margin={'l': 0, 'r': 0, 't': 20, 'b': 0}**)
fig_H2= go.Figure(data=[go.Table(
header=dict(values=["Confirmed Cases","Deaths", "Recovered"]
,
line_color=line_color_H2,fill_color=fill_color_H2,
align='center',font=dict(color='black', size=10)
),
cells=dict(
values=[[a,e],[b,f], [c,g]],align='center',
line_color=line_color_H2,
fill_color=fill_color_H2, font_color=font_color_H2,
font_size=font_size_H2
))
])
fig_H2.update_layout(
autosize=False,
width=500,
height=170,
margin=dict(
l=20,
r=20,
b=20,
t=50,
pad=10
),
title_text="<b>WORLD</b>",title_x=0.5,
title_font_color='black'
)
return fig_H2
href="/"),
dbc.NavbarToggler(id="navbar-toggler2"),
dbc.Nav(
[
dbc.NavItem(dbc.NavLink("Récapitulatif",href="/")),
dbc.NavItem(dbc.NavLink("Simulateur",href="/simulation")),
],className="ml-auto",navbar=True
)
],
color="dark",
dark=True,
)
df=GetData.get_recap_by_country("20/03/2020",10)
style_H1 ={"display": "block",
"margin-left": "auto",
"margin-right": "auto",'textAlign': 'center'}
style_label = {"display": "block",
"margin-left": "auto",
"margin-right": "auto",'textAlign': 'center', 'color':'black'}
style_phraseH5 = {'color': 'black', 'textAlign': 'center',\
'margin-bottom':'0px','margin-top': '0px' }
style_fig_H2={
'height': 152,
import pandas as pd
from apps import GetData
from apps import graph
import dash_bootstrap_components as dbc
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import time
from app import app
from dash.dependencies import Input, Output
from datetime import datetime, timedelta
import yfinance as yf
### PLOT
df_confirmed_world = GetData.get_world('confirmed')
df = pd.DataFrame()
df['Date'] = pd.to_datetime(df_confirmed_world.iloc[:, 5:].columns)
df['Confirmed_World'] = df_confirmed_world.iloc[:, 5:].sum().values
df.set_index('Date', inplace=True)
list_tickers = [
'^FCHI', '^GSPC', '^DJI', '^GDAXI', '^IXIC', '^N225', '^HSI', '^IBEX',
'BTC-USD', 'ETHUSD=X', 'EURUSD=X', 'EURGBP=X', 'EURJPY=X', 'EURCNY=X',
'EURCHF=X', 'HG=F', 'EH=F', 'GC=F', 'NG=F', 'CL=F', 'PL=F', 'SI=F'
]
title = [
'CAC40', 'SP500', 'Dow Jones', 'Dax', 'Nasdaq', 'Nikkei', 'Hangseng',
'Ibex', 'BTC/USD', 'ETH/USD', 'EUR/USD', 'EUR/GBP', 'EUR/JPY', 'EUR/CNY',
'EUR/CHF', 'Copper', 'Ethanol', 'Gold', 'Natural Gas', 'Oil', 'Platinum',
'Silver'
}
}
#FCT
def get_top3_country(name):
index_top3 = GetData.get_world(name).groupby('Country/Region').sum().iloc[:,2:].T.iloc[-1].sort_values(ascending = False).index[0:3].values
df_confirmed_world = GetData.get_world('confirmed').groupby('Country/Region').sum().iloc[:,2:].T
df_deaths_world = GetData.get_world('deaths').groupby('Country/Region').sum().iloc[:,2:].T
df_recovered_world = GetData.get_world('recovered').groupby('Country/Region').sum().iloc[:,2:].T
df = pd.DataFrame()
df['Confirmed'] = df_confirmed_world[index_top3].iloc[-1,:]
df['Recovered'] = df_recovered_world[index_top3].iloc[-1,:]
df['Deaths'] = df_deaths_world[index_top3].iloc[-1,:]
return df
top3 = get_top3_country('confirmed')
df =GetData.get_world('confirmed')
def make_bars(country,df = df):
t =df[(df['Country/Region'] == country)].groupby('Country/Region').sum()
lst= [list(t[col].values)[0] for col in t.columns if col not in ['Continent','Country/Region','Province/State','Lat','Long']]
variation = [lst[i] - lst[i-1] for i in range(1,len(lst))]
color = {
'0':{
'border-right':'1px solid rgba(255,255,255,0.5)',
'width':'3px',
},
'1-10':{
'background':'rgba(255, 152, 0, 0.1)',
'border-right':'1px solid rgba(255,255,255,0.5)',
'width':'3px',
},
import plotly.graph_objects as go
from apps import GetData
import pandas as pd
import plotly.graph_objects as go
from plotly.colors import n_colors
import numpy as np
import chart_studio.plotly as py
#Récupération du récap
date = "20/03/2020"
previous = 5
df_recap = GetData.get_recap_by_country(date, previous=previous)
#Récupération des cas confirmés
df_confirmed = GetData.get_world('confirmed')
confirmed = df_confirmed.iloc[:, 5:].sum(axis=1).sum(axis=0)
confirmedp = df_recap['Cases (+)'].sum(axis=0)
#Récupération des cas morts
df_deaths = GetData.get_world('deaths')
deaths = df_deaths.iloc[:, 5:].sum(axis=1).sum(axis=0)
deathsp = df_recap['Deaths (+)'].sum(axis=0)
#Récupération des cas guéris
df_recovered = GetData.get_world('recovered')
recovered = df_recovered.iloc[:, 5:].sum(axis=1).sum(axis=0)
recoveredp = df_recap['Recovered (+)'].sum(axis=0)
df_H2 = pd.DataFrame([[confirmed, deaths, recovered]],
columns=["Confirmed Cases", "Deaths", "Recovered"])
confirmed = f'{confirmed:,}'
deaths = f'{deaths:,}'
