Covid-19 in Italy / Covid-19 in Italia

All data is provided by the official GitHub repository of the Protezione Civile.

La fonte dei dati di queste visualizzazioni e' la Protezione Civile, tramite il loro repository su GitHub.

curl https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-andamento-nazionale.json > data.json
curl https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv > data.csv
1.5s
Bash in Python
Python 3

Raw Data / Dati grezzi

import pandas as pd
data = pd.read_csv('data.csv')
data.sort_values(by=['data'], ascending=False)
0.6s
Python
Python 3

Contagion and outcome / Contagio e prognosi

Linear scale / Scala lineare

import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
fig = go.Figure(data=[
    go.Scatter(mode='lines',name='Recovered / Guariti', x=data['data'], y=data['dimessi_guariti'], marker_color='limegreen'),
    go.Scatter(mode='lines',name='Positive / Positivi', x=data['data'], y=data['totale_positivi'], marker_color='purple'),
  go.Scatter(mode='lines',name='Dead / Deceduti', x=data['data'], y=data['deceduti'], marker_color='tomato')
])
fig
0.7s
Python
Python 3

Logarithmic scale / Scala logaritmica

import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
fig = go.Figure(data=[
    go.Scatter(mode='lines', name='Recovered / Guariti', x=data['data'], y=data['dimessi_guariti'], marker_color='limegreen'),
    go.Scatter(mode='lines', name='Positive / Positivi', x=data['data'], y=data['totale_positivi'], marker_color='purple'),
  go.Scatter(mode='lines', name='Dead / Deceduti', x=data['data'], y=data['deceduti'], marker_color='tomato')
])
fig.update_yaxes(type="log")
fig
0.6s
Python
Python 3

Situation in Hospitals / La situazione negli ospedali

Hospitalisation vs house isolation / Ricovero vs Isolamento domiciliare

import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
fig = go.Figure(data=[
   go.Scatter(mode='lines',name='House isolation / Isolamento domiciliare', x=data['data'], y=data['isolamento_domiciliare'], marker_color='royalblue'),
    go.Scatter(mode='lines',name='Hospitalised / Ospedalizzati', x=data['data'], y=data['totale_ospedalizzati'], marker_color='peru')
])
fig
0.4s
Python
Python 3

ICU vs Normal Hospital Care / Terapia intensiva vs normale ricovero

import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
fig = go.Figure(data=[
   go.Scatter(mode='lines',name='ICU / Terapia intensiva', x=data['data'], y=data['terapia_intensiva'], marker_color='orange'),
    go.Scatter(mode='lines',name='Normal hospital care / Normale ricovero', x=data['data'], y=data['ricoverati_con_sintomi'], marker_color='turquoise')
])
fig
0.5s
Python
Python 3

Daily new cases / Nuovi casi giornalieri

import pandas as pd
import numpy as numpy
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
yy = data['nuovi_positivi']
dead = data['deceduti']
a=dead.shift()
a[0]=0
new_dead = dead-a
recovered = data['dimessi_guariti']
b = recovered.shift()
b[0] = 0
new_recovered = recovered - b
fig = go.Figure(data=[
   go.Scatter(mode='lines', name='New positive / Nuovi positivi', x=data['data'], y=yy, marker_color='orchid'),
    go.Scatter(mode='lines',name='Deaths / Decessi', x=data['data'], y=new_dead, marker_color='tomato'),
 go.Scatter(mode='lines',name='Recoveries / Guarigioni', x=data['data'], y=new_recovered, marker_color='limegreen')
])
fig
0.4s
Python
Python 3

Daily tests and new infected / Tamponi giornalieri e nuovi positivi

import pandas as pd
import numpy as numpy
import plotly.graph_objs as go
data = pd.read_csv('data.csv')
yy = data['nuovi_positivi']
tests = data['tamponi']
a=tests.shift()
a[0]=0
new_tests = tests-a
fig = go.Figure(data=[
   go.Scatter(mode='lines', name='New positive / Nuovi positivi', x=data['data'], y=yy, marker_color='orchid'),
    go.Scatter(mode='lines', name='Tests / Tamponi', x=data['data'], y=new_tests, marker_color='darkcyan')
])
fig
0.6s
Python
Python 3

Lombardia

curl https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv > data_region.csv
0.9s
Bash in Python
Python 3
import pandas as pd
data = pd.read_csv('data_region.csv')
data = data[data['denominazione_regione'] == "Lombardia"]
data.sort_values(by=['data'], ascending=False)
0.6s
Python
Python 3
import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data_region.csv')
data = data[data['denominazione_regione'] == "Lombardia"]
fig = go.Figure(data=[
    go.Scatter(mode='lines',name='Recovered / Guariti', x=data['data'], y=data['dimessi_guariti'], marker_color='limegreen'),
    go.Scatter(mode='lines',name='Positive / Positivi', x=data['data'], y=data['totale_positivi'], marker_color='purple'),
  go.Scatter(mode='lines',name='Dead / Deceduti', x=data['data'], y=data['deceduti'], marker_color='tomato')
])
fig
0.5s
Python
Python 3
import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('data_region.csv')
data = data[data['denominazione_regione'] == "Lombardia"]
fig = go.Figure(data=[
   go.Scatter(mode='lines',name='House isolation / Isolamento domiciliare', x=data['data'], y=data['isolamento_domiciliare'], marker_color='royalblue'),
    go.Scatter(mode='lines',name='Hospitalised / Ospedalizzati', x=data['data'], y=data['totale_ospedalizzati'], marker_color='peru')
])
fig
0.4s
Python
Python 3
import pandas as pd
import numpy as numpy
import plotly.graph_objs as go
data = pd.read_csv('data_region.csv')
data = data[data['denominazione_regione'] == "Lombardia"]
yy = data['nuovi_positivi']
tests = data['tamponi']
a=tests.shift()
a[0]=0
new_tests = tests-a
fig = go.Figure(data=[
   go.Scatter(mode='lines', name='New positive / Nuovi positivi', x=data['data'], y=yy, marker_color='orchid'),
    go.Scatter(mode='lines', name='Tests / Tamponi', x=data['data'], y=new_tests, marker_color='darkcyan')
])
fig
0.5s
Python
Python 3

Other resources / Altre risorse (in inglese)

Runtimes (1)