plotting COVID advance

covid_folium.py

import folium, pandas as pd
from folium.plugins import MarkerCluster

pdf = pd.read_json('https://tinyurl.com/covid19-github')
pdf = pdf[pdf.data==pdf.data.max()]
pdf = pdf[pdf.totale_casi>0]

location = pdf.describe()[['lat','long']].loc['50%'].values
fm = folium.Map(location=location, zoom_start=6, tile='stamentoner',
               width=800, height=600)
mc = MarkerCluster()
for row in pdf.itertuples():
    mc.add_child(folium.Marker(location=[row.lat,  row.long],
                 popup=row.denominazione_provincia))
mc.add_to(fm)
fm

covid_folium_HMWT.py

import folium, pandas as pd, numpy as np
from folium.plugins import HeatMapWithTime

pdf = pd.read_json('https://tinyurl.com/covid19-github')
pdf = pdf[pdf.totale_casi>0]
pdf = pdf[(pdf.lat>0) & (pdf.long > 0)]

# Calculate weight relative to max number of cases per location
max_prov = pdf[['lat','long','totale_casi']].groupby(['lat','long']).max().rename(columns={'totale_casi': 'Max_Provincia'})
pdf = pd.merge(pdf, max_prov, on=['lat','long'], how='left')
pdf['weight'] =  round(pdf['totale_casi'] / pdf['Max_Provincia'], 5) #Weight based on highest value (should decrease as people recover)

# format data for heatmapwithtime
final_df = []
for dt in np.unique(pdf['data']):
    final_df.append(pdf[pdf['data']==dt][['lat','long','weight']].values.tolist())
    
#location = pdf.describe()[['lat','long']].loc['50%'].values
location = [41.54, 12.29]
fm = folium.Map(location=location, zoom_start=5, width=640, height=480)

HeatMapWithTime(data=final_df, index=np.unique(pdf['data']).tolist()).add_to(fm)
fm