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covid.py
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covid.py
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import pandas as pd
import numpy as np
import os
from datetime import datetime, timedelta
import time
import plotly.graph_objects as go
from urllib.request import urlopen
import json
INPUT_URL = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/"
df_lookup = pd.read_csv(INPUT_URL+"UID_ISO_FIPS_LookUp_Table.csv")
def transform_and_standardize(df, var_name):
df = df.drop(columns=['Lat', 'Long']).merge(
df_lookup.rename(columns={'Country_Region': 'Country/Region', 'Province_State': 'Province/State'})[['Country/Region', 'Province/State', 'iso3','Population']],
how='outer',
on=['Country/Region', 'Province/State']
).dropna(subset=["iso3"])
df = df.groupby(['iso3','Country/Region']).sum().reset_index()
df = df.melt(id_vars=[df.columns[0],df.columns[1],df.columns[-1]],
value_vars=df.columns[2:-1],
var_name='date',
value_name=var_name
).dropna()
df['date']=pd.to_datetime(df['date'])
return df.sort_values(by=['iso3', 'date'])
def transform_and_standardize_us(df, var_name):
if var_name is 'deaths':
df=df.drop(columns=['Population'])
df = df.drop(columns=['UID','iso2','iso3','Country_Region','code3']).groupby(['FIPS','Admin2','Province_State','Lat','Long_']).sum().reset_index()
df = df.melt(id_vars=[df.columns[0],df.columns[1],df.columns[2],df.columns[3],df.columns[4]],
value_vars=df.columns[5:],
var_name='date',
value_name=var_name
).dropna()
df['date']=pd.to_datetime(df['date'])
return df.sort_values(by=['FIPS', 'date'])
df_confirmed = transform_and_standardize(pd.read_csv(INPUT_URL+"csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"), 'confirmed')
df_deaths = transform_and_standardize(pd.read_csv(INPUT_URL+"csse_covid_19_time_series/time_series_covid19_deaths_global.csv"), 'deaths')
df_recovered = transform_and_standardize(pd.read_csv(INPUT_URL+"csse_covid_19_time_series/time_series_covid19_recovered_global.csv"), 'recovered')
df = df_confirmed.merge(df_deaths,how='outer',on=['date', 'iso3', 'Population','Country/Region']).merge(df_recovered,how='outer',on=['date', 'iso3', 'Population','Country/Region'])
for col in ['confirmed', 'deaths', 'recovered']:
df[f'{col}_rate'] = (df[col]/df['Population']*100000000).astype('int64')
df_confirmed_us = transform_and_standardize_us(pd.read_csv(INPUT_URL+"csse_covid_19_time_series/time_series_covid19_confirmed_US.csv"), 'confirmed')
df_deaths_us = transform_and_standardize_us(pd.read_csv(INPUT_URL+"csse_covid_19_time_series/time_series_covid19_deaths_US.csv"), 'deaths').drop(columns=['Lat','Long_'])
df_us=df_confirmed_us.merge(df_deaths_us,how='outer',on=['date', 'FIPS', 'Admin2','Province_State'])
df_us=df_us.merge(df_lookup[['FIPS','Population']],
how='outer',
on=['FIPS']).dropna()
df_us = df_us.astype({'FIPS':'int','confirmed':'int','deaths':'int','Population':'int'})
df_states=df_us.drop(columns=['FIPS']).groupby(['Province_State','date',]).sum().reset_index()
for col in ['confirmed', 'deaths']:
df_states[f'{col}_rate'] = (df_states[col]/df_states['Population']*100000000).astype('int64')
df_us[f'{col}_rate'] = (df_us[col]/df_us['Population']*100000000).astype('int64')
abbreviations={
"Alabama": ["01", "AL"],
"Alaska": ["02", "AK"],
"Arizona": ["04", "AZ"],
"Arkansas": ["05", "AR"],
"California": ["06", "CA"],
"Colorado": ["08", "CO"],
"Connecticut": ["09", "CT"],
"Delaware": ["10", "DE"],
"District of Columbia": ["11", "DC"],
"Florida": ["12", "FL"],
"Georgia": ["13", "GA"],
"Hawaii": ["15", "HI"],
"Idaho": ["16", "ID"],
"Illinois": ["17", "IL"],
"Indiana": ["18", "IN"],
"Iowa": ["19", "IA"],
"Kansas": ["20", "KS"],
"Kentucky": ["21", "KY"],
"Louisiana": ["22", "LA"],
"Maine": ["23", "ME"],
"Maryland": ["24", "MD"],
"Massachusetts": ["25", "MA"],
"Michigan": ["26", "MI"],
"Minnesota": ["27", "MN"],
"Mississippi": ["28", "MS"],
"Missouri": ["29", "MO"],
"Montana": ["30", "MT"],
"Nebraska": ["31", "NE"],
"Nevada": ["32", "NV"],
"New Hampshire": ["33", "NH"],
"New Jersey": ["34", "NJ"],
"New Mexico": ["35", "NM"],
"New York": ["36", "NY"],
"North Carolina": ["37", "NC"],
"North Dakota": ["38", "ND"],
"Ohio": ["39", "OH"],
"Oklahoma": ["40", "OK"],
"Oregon": ["41", "OR"],
"Pennsylvania": ["42", "PA"],
"Rhode Island": ["44", "RI"],
"South Carolina": ["45", "SC"],
"South Dakota": ["46", "SD"],
"Tennessee": ["47", "TN"],
"Texas": ["48", "TX"],
"Utah": ["49", "UT"],
"Vermont": ["50", "VT"],
"Virginia": ["51", "VA"],
"Washington": ["53", "WA"],
"West Virginia": ["54", "WV"],
"Wisconsin": ["55", "WI"],
"Wyoming": ["56", "WY"]
}
df_states['number']=df_states['Province_State'].map(lambda x: abbreviations[x][0])
df_states['abbreviation']=df_states['Province_State'].map(lambda x: abbreviations[x][1])
df_us['number']=df_us['Province_State'].map(lambda x: abbreviations[x][0])
df_us['FIPS']=df_us['FIPS'].astype(str).str.zfill(5)
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
df_geo=dict()
for county in counties['features']:
area=county['properties']['STATE']
df_geo.setdefault(area,{'type':'FeatureCollection', 'features':[]})
df_geo[area]['features'].append(county)
data='confirmed'
scope='usa'
dates=df_us['date'].unique()
area='06'
df_geo_area=df_geo[area]
fig=go.Figure(layout={
'paper_bgcolor':'rgba(0,0,0,0)',
'plot_bgcolor':'rgba(0,0,0,0)',
'margin':{'l':0,'r':0,'t':0,'b':0,'pad':0},
'uirevision':True,
'geo':{'uirevision':True,
'bgcolor':'rgba(0,0,0,0)',
'showcountries':True, 'countrycolor': 'white', 'countrywidth': 15,
'showsubunits':True, 'subunitcolor': 'white', 'subunitwidth': 1,
'showcoastlines': True, 'coastlinecolor': 'white', 'coastlinewidth': 15,
'showland':False,
'showlakes':False,
'scope':scope,
'showframe': False,
},
'hovermode':'closest'
})
for date_parsed in dates:
df_by_date=df_us.query('number==@area').query('date==@date_parsed').to_dict('list')
df_geo_area=df_geo[area]
#fig.update_layout(title={
# 'text':str(date_parsed)[:10],
# 'x':0.02,
# 'y':0.98,
# 'yanchor':'top',
# 'xanchor':'left',
# 'font_size':70,
# 'font_color':'white',
#})
fig.update(data=({
'type':'scattergeo',
'lat':df_by_date['Lat'],
'lon':df_by_date['Long_'],
'text':df_by_date['Admin2'],
'customdata':["USA"]*len(df_by_date[data]),
'marker':{
'size':[x/100 for x in df_by_date[data]],
'opacity':0.6,
'sizemode':'area',
'color':'rgb(0,0,0)',
'line_width':0
}
},{
'type':'choropleth',
'geojson':df_geo_area,
'locations':df_by_date['FIPS'],
'z':df_by_date[f'{data}_rate'],
'zmin':0,
'zmax':1000000,
'text':df_by_date['Admin2'],
'customdata':["USA"]*len(df_by_date[data]),
'autocolorscale':False,
'marker_line_color': 'white',
'marker_line_width': 4,
'colorscale':[[0.0, 'rgb(255,255,255)'],
[1e-06, 'rgb(255,245,240)'],
[1e-05, 'rgb(254,224,210)'],
[3.2e-05, 'rgb(252,187,161)'],
[0.0001, 'rgb(252,146,114)'],
[0.00032, 'rgb(251,106,74)'],
[0.001, 'rgb(239,59,44)'],
[0.01, 'rgb(203,24,29)'],
[0.1, 'rgb(165,15,21)'],
[1.0, 'rgb(103,0,13)']],
'showscale':False
}))
fig.add_annotation({
'text':'Map: Johan Vonk; Data: JHU',
'x':0.98,
'y':0.02,
'yanchor':'bottom',
'xanchor':'right',
'font_size':30,
'font_color':'white',
})
fig.update_geos(fitbounds="geojson")
name=f"img/{str(date_parsed)[:10]}.png"
fig.write_image(name, width=3840, height=2160)
print(name)