Elaborate Covid-19 Italian Data
Very simple notebook example that shows how to load data from a CSV, leverage panda’s Dataframes tocalculate few extra columns with Covid Pandemic KPIs and then display it using Plotly
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| # Import Global Libraries
import pandas as pd
import numpy as np
import io
import requests
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Load Data
The data are downloaded from the Italian “Protezione Civile” Covid Data repository on github https://github.com/pcm-dpc/COVID-19
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| url='https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv'
s = requests.get(url).text
df = pd.read_csv(io.StringIO(s))
df.tail()
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|
data |
stato |
ricoverati_con_sintomi |
terapia_intensiva |
totale_ospedalizzati |
isolamento_domiciliare |
totale_positivi |
variazione_totale_positivi |
nuovi_positivi |
dimessi_guariti |
... |
tamponi |
casi_testati |
note |
ingressi_terapia_intensiva |
note_test |
note_casi |
totale_positivi_test_molecolare |
totale_positivi_test_antigenico_rapido |
tamponi_test_molecolare |
tamponi_test_antigenico_rapido |
| 330 |
2021-01-19T17:00:00 |
ITA |
22699 |
2487 |
25186 |
510338 |
535524 |
-11535 |
10497 |
1781917 |
... |
29619436 |
16009790.0 |
NaN |
176.0 |
NaN |
NaN |
2397121.0 |
3477.0 |
29132944.0 |
486492.0 |
| 331 |
2021-01-20T17:00:00 |
ITA |
22469 |
2461 |
24930 |
498623 |
523553 |
-11971 |
13571 |
1806932 |
... |
29899198 |
16102034.0 |
NaN |
152.0 |
NaN |
NaN |
2409616.0 |
4550.0 |
29296422.0 |
602776.0 |
| 332 |
2021-01-21T17:00:00 |
ITA |
22045 |
2418 |
24463 |
492105 |
516568 |
-6985 |
14078 |
1827451 |
... |
30166765 |
16197394.0 |
NaN |
155.0 |
NaN |
NaN |
2422728.0 |
5493.0 |
29458875.0 |
707890.0 |
| 333 |
2021-01-22T17:00:00 |
ITA |
21691 |
2390 |
24081 |
477972 |
502053 |
-14515 |
13633 |
1855127 |
... |
30431493 |
16279588.0 |
NaN |
144.0 |
NaN |
NaN |
2435519.0 |
6335.0 |
29608567.0 |
822926.0 |
| 334 |
2021-01-23T17:00:00 |
ITA |
21403 |
2386 |
23789 |
475045 |
498834 |
-3219 |
13331 |
1871189 |
... |
30717824 |
16367107.0 |
NaN |
174.0 |
NaN |
NaN |
2447861.0 |
7324.0 |
29759716.0 |
958108.0 |
5 rows × 24 columns
Build additional columns to calculate pandemics KPIs
Calculating and wrangling a bit the data to fit them in a comparable scale.
Additional columns:
\[infection.rate = nuovi.positivi/nuovi.tamponi\]
\[infectedX1000=nuovi.positivi/1000\]
\[deadX100=nuovi.deceduti/100\]
\[criticalX100=terapia.intensiva/100\]
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| df.fillna(0)
df['nuovi_tamponi']=df['tamponi'].diff()
df['nuovi_deceduti']=df['deceduti'].diff()
df['infection_rate']=100*df['nuovi_positivi']/df['nuovi_tamponi']
df['infectedX1000']=df['nuovi_positivi']/1000
df['deadX100']=df['nuovi_deceduti']/100
df['criticalX100']=df['terapia_intensiva']/100
#df[df['data']=='2020-12-17T17:00:00','infection_rate']=0
df.loc[df['data']=='2020-12-17T17:00:00',['infection_rate']]=0
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| # Display the dataframe types with the added columns at the end
df.dtypes
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| data object
stato object
ricoverati_con_sintomi int64
terapia_intensiva int64
totale_ospedalizzati int64
isolamento_domiciliare int64
totale_positivi int64
variazione_totale_positivi int64
nuovi_positivi int64
dimessi_guariti int64
deceduti int64
casi_da_sospetto_diagnostico float64
casi_da_screening float64
totale_casi int64
tamponi int64
casi_testati float64
note object
ingressi_terapia_intensiva float64
note_test float64
note_casi float64
totale_positivi_test_molecolare float64
totale_positivi_test_antigenico_rapido float64
tamponi_test_molecolare float64
tamponi_test_antigenico_rapido float64
nuovi_tamponi float64
nuovi_deceduti float64
infection_rate float64
infectedX1000 float64
deadX100 float64
criticalX100 float64
dtype: object
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Display Results
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| # import graphic libraries
import plotly.express as px
import plotly.io as pio
# Draw Graph
fig = px.line(df, x='data', y=['infection_rate','deadX100','infectedX1000', 'criticalX100'], title='Italy [Rate of infection, deaths/100, infected/1000, critical/100] ')
# write image
pio.write_image(fig, file='covid_graph.png',format='png')
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OPTIONAL: save html Graph
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| fig.write_html('Infection_Ratio_Chart.html', include_plotlyjs="cdn",auto_open=False)
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