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etl.py
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import pandas as pd
import numpy as np
import glob
import re
from datetime import date, timedelta
import time
import io
import requests
import sys
def save_from_web(url):
'''
fetching data from the Johns Hopkins public repo and save it as a csv file
'''
url = r'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/{}'.format(
url)
raw_string = requests.get(url).content
return pd.read_csv(io.StringIO(raw_string.decode('utf-8')))
def load_time_series(source='web', update='manual'):
'''
fetch # of cases as time series then save it in the data/raw folder
to use it for building the indicators components.
'''
if source == 'web':
today = date.today()
prepend = r'csse_covid_19_time_series/time_series_covid19_'
current_data = False
start_time = time.time()
# Continuously re-download files until all have been updated
while not current_data:
try:
confirmed_us = save_from_web(
r'{}confirmed_US.csv'.format(prepend))
print('confirmed_us ', confirmed_us.columns[-1])
confirmed_global = save_from_web(
r'{}confirmed_global.csv'.format(prepend))
print('confirmed_global ', confirmed_global.columns[-1])
deaths_us = save_from_web(r'{}deaths_US.csv'.format(prepend))
print('deaths_us ', deaths_us.columns[-1])
deaths_global = save_from_web(
r'{}deaths_global.csv'.format(prepend))
print('deaths_global ', deaths_global.columns[-1])
recovered_global = save_from_web(
r'{}recovered_global.csv'.format(prepend))
print('recovered_global ', recovered_global.columns[-1])
csv_files = {'1': confirmed_us,
'2': confirmed_global,
'3': deaths_us,
'4': deaths_global,
'5': recovered_global}
responses = {'1': False,
'2': False,
'3': False,
'4': False,
'5': False}
for csv in csv_files:
if pd.to_datetime(csv_files[csv].columns[-1]) == today:
responses[csv] = True
if sum(responses.values()) == 5:
current_data = True
print()
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
print('Date = {}'.format(confirmed_us.columns[-1]))
elif update == 'manual':
current_data = True
print()
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
print('Date = {}'.format(confirmed_us.columns[-1]))
else:
if time.time() - start_time > 2.5 * 3600: # stop checking after 2.5 hours
print()
print('Timed out after 2.5 hours')
print(time.strftime('%H:%M:%S',
time.localtime(time.time())))
return 'end'
print()
print('Waiting for GitHub update...')
time.sleep(600)
print()
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
except requests.exceptions.ConnectionError:
if time.time() - start_time > 4.5 * 3600: # stop checking after 2.5 hours
print()
print('Timed out after 4.5 hours')
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
return 'end'
print()
print('Waiting for connection to internet...')
time.sleep(600)
print()
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
confirmed_us.to_csv(
'data/raw/time_series_covid19_confirmed_US.csv', index=False)
confirmed_global.to_csv(
'data/raw/time_series_covid19_confirmed_global.csv', index=False)
deaths_us.to_csv(
'data/raw/time_series_covid19_deaths_US.csv', index=False)
deaths_global.to_csv(
'data/raw/time_series_covid19_deaths_global.csv', index=False)
recovered_global.to_csv(
'data/raw/time_series_covid19_recovered_global.csv', index=False)
elif source == 'folder':
confirmed_us = pd.read_csv(
'data/raw/time_series_covid19_confirmed_US.csv')
confirmed_global = pd.read_csv(
'data/raw/time_series_covid19_confirmed_global.csv')
deaths_us = pd.read_csv('data/raw/time_series_covid19_deaths_US.csv')
deaths_global = pd.read_csv(
'data/raw/time_series_covid19_deaths_global.csv')
recovered_global = pd.read_csv(
'data/raw/time_series_covid19_recovered_global.csv')
dates = []
for column in confirmed_global.columns:
if re.search(r'([0-9]{1,2}\/[0-9]{1,2}\/(20))', column):
dates.append(column)
print()
print('Transforming data')
# Melt (de-pivot), join, and union the above tables
confirmed_us_melt = pd.melt(
confirmed_us,
id_vars=['Country_Region', 'Province_State', 'Admin2', 'Lat', 'Long_'],
value_vars=dates).rename(columns={'Country_Region': 'Country/Region',
'Province_State': 'Province/State',
'Lat': 'Latitude',
'Long_': 'Longitude',
'variable': 'date',
'value': 'Confirmed'})
deaths_us_melt = pd.melt(
deaths_us,
id_vars=['Country_Region', 'Province_State', 'Admin2', 'Lat', 'Long_'],
value_vars=dates).rename(columns={'Country_Region': 'Country/Region',
'Province_State': 'Province/State',
'Lat': 'Latitude',
'Long_': 'Longitude',
'variable': 'date',
'value': 'Deaths'})
us_confirmed_deaths = pd.merge(
confirmed_us_melt,
deaths_us_melt[['date',
'Country/Region',
'Province/State',
'Admin2',
'Deaths']],
on=['date',
'Country/Region',
'Province/State',
'Admin2'],
how='outer').assign(Recovered=np.nan)[['date',
'Country/Region',
'Province/State',
'Admin2',
'Latitude',
'Longitude',
'Confirmed',
'Deaths',
'Recovered']]
confirmed_global_melt = pd.melt(
confirmed_global,
id_vars=['Country/Region', 'Province/State', 'Lat', 'Long'],
value_vars=dates).rename(columns={'Lat': 'Latitude',
'Long': 'Longitude',
'variable': 'date',
'value': 'Confirmed'}).assign(Admin2=np.nan)
# aggregate Canada confirmed because recovered is not aggregated
confirmed_canada = confirmed_global_melt[confirmed_global_melt['Country/Region']
== 'Canada'].groupby('date', as_index=False).agg({'Confirmed': 'sum'})
confirmed_canada['Country/Region'] = 'Canada'
confirmed_canada['Province/State'] = np.nan
confirmed_canada['Latitude'] = 56.1304
confirmed_canada['Longitude'] = -106.346800
confirmed_canada['Admin2'] = np.nan
confirmed_canada = confirmed_canada[[
'Country/Region', 'Province/State', 'Latitude', 'Longitude', 'date', 'Confirmed', 'Admin2']]
confirmed_global_melt = pd.concat([
confirmed_global_melt[confirmed_global_melt['Country/Region'] != 'Canada'],
confirmed_canada])
deaths_global_melt = pd.melt(deaths_global,
id_vars=['Country/Region',
'Province/State', 'Lat', 'Long'],
value_vars=dates).rename(columns={'Lat': 'Latitude',
'Long': 'Longitude',
'variable': 'date',
'value': 'Deaths'}).assign(Admin2=np.nan)
# aggregate Canada deaths because recovered is not aggregated
deaths_canada = deaths_global_melt[deaths_global_melt['Country/Region']
== 'Canada'].groupby('date', as_index=False).agg({'Deaths': 'sum'})
deaths_canada['Country/Region'] = 'Canada'
deaths_canada['Province/State'] = np.nan
deaths_canada['Latitude'] = 56.1304
deaths_canada['Longitude'] = -106.346800
deaths_canada['Admin2'] = np.nan
deaths_canada = deaths_canada[[
'Country/Region', 'Province/State', 'Latitude', 'Longitude', 'date', 'Deaths', 'Admin2']]
deaths_global_melt = pd.concat([
deaths_global_melt[deaths_global_melt['Country/Region'] != 'Canada'],
deaths_canada])
confirmed_deaths_global = pd.merge(
confirmed_global_melt,
deaths_global_melt[['date',
'Country/Region',
'Province/State',
'Admin2',
'Deaths']],
on=['date', 'Country/Region', 'Province/State', 'Admin2'],
how='outer')
# fix some mismatched coordinates
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Syria', ['Latitude']] = 34.802075
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Syria', ['Longitude']] = 38.996815
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Mozambique', ['Latitude']] = -18.6657
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Mozambique', ['Longitude']] = 35.5296
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Timor-Leste', ['Latitude']] = -8.8742
confirmed_deaths_global.loc[confirmed_deaths_global['Country/Region']
== 'Timor-Leste', ['Longitude']] = 125.7275
recovered_global_melt = pd.melt(
recovered_global,
id_vars=['Country/Region', 'Province/State', 'Lat', 'Long'],
value_vars=dates).rename(columns={'Lat': 'Latitude',
'Long': 'Longitude',
'variable': 'date',
'value': 'Recovered'}).assign(Admin2=np.nan)
# fix some mismatched coordinates
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Syria', ['Latitude']] = 34.802075
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Syria', ['Longitude']] = 38.996815
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Mozambique', ['Latitude']] = -18.6657
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Mozambique', ['Longitude']] = 35.5296
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Timor-Leste', ['Latitude']] = -8.8742
recovered_global_melt.loc[recovered_global_melt['Country/Region']
== 'Timor-Leste', ['Longitude']] = 125.7275
global_confirmed_deaths_recovered = pd.merge(
confirmed_deaths_global,
recovered_global_melt[['date',
'Country/Region',
'Province/State',
'Admin2',
'Recovered']],
on=['date', 'Country/Region', 'Province/State', 'Admin2'],
how='outer')[['date',
'Country/Region',
'Province/State',
'Admin2',
'Latitude',
'Longitude',
'Confirmed',
'Deaths',
'Recovered']]
df = pd.concat([
us_confirmed_deaths,
global_confirmed_deaths_recovered
])
df['date'] = pd.to_datetime(df['date'])
# Create "Recovered" state for unassigned recoveries
df.loc[(df['Country/Region'] == 'US') &
(df['Recovered'].notna()), 'Province/State'] = 'Recovered'
df.loc[df['Province/State'] == 'Recovered', 'Confirmed'] = 0
df.loc[df['Province/State'] == 'Recovered', 'Deaths'] = 0
df['Recovered'] = df['Recovered'].fillna(0)
# Create "Active" column
df['Active'] = df['Confirmed'] - df['Deaths'] - df['Recovered']
df.loc[df['Province/State'] == 'Recovered', 'Active'] = 0
df['Recovered'] = df['Recovered'].astype(int)
df['Active'] = df['Active'].astype(int)
df = df.sort_values(by=['date', 'Country/Region',
'Province/State', 'Admin2']).reset_index(drop=True)
# Rename countries
df['Country/Region'].replace('Korea, South', 'South Korea', inplace=True)
df['Country/Region'].replace('Taiwan*', 'Taiwan', inplace=True)
return df
def load_daily_reports(source='web'):
if source == 'web':
# Load files from web
file_date = date(2020, 1, 22)
dates = []
while file_date <= date.today():
dates.append(file_date)
file_date += timedelta(days=1)
files = []
for file in dates:
file = file.strftime("%m-%d-%Y")
url = r'csse_covid_19_daily_reports/{}.csv'.format(file)
raw_string = requests.get(url).content
df = pd.read_csv(io.StringIO(raw_string.decode('utf-8')))
if b'404: Not Found' not in raw_string:
df.to_csv('data/raw/{}.csv'.format(file), index=False)
print(file)
df['date'] = pd.to_datetime(file)
df.rename(columns={'Province_State': 'Province/State',
'Country_Region': 'Country/Region',
'Lat': 'Latitude',
'Long_': 'Longitude'}, inplace=True)
files.append(df)
elif source == 'folder':
# Load files from folder
path = 'data/raw'
all_files = glob.glob(path + "/*.csv")
files = []
for filename in all_files:
file = re.search(r'([0-9]{2}\-[0-9]{2}\-[0-9]{4})', filename)[0]
print(file)
df = pd.read_csv(filename, index_col=None, header=0)
df['date'] = pd.to_datetime(file)
df.rename(columns={'Province_State': 'Province/State',
'Country_Region': 'Country/Region',
'Lat': 'Latitude',
'Long_': 'Longitude'}, inplace=True)
files.append(df)
# Rename countries with duplicate naming conventions
df['Country/Region'].replace('Mainland China', 'China', inplace=True)
df['Country/Region'].replace('Hong Kong SAR', 'Hong Kong', inplace=True)
df['Country/Region'].replace(' Azerbaijan', 'Azerbaijan', inplace=True)
df['Country/Region'].replace('Holy See', 'Vatican City', inplace=True)
df['Country/Region'].replace('Iran (Islamic Republic of)',
'Iran', inplace=True)
df['Country/Region'].replace('Taiwan*', 'Taiwan', inplace=True)
df['Country/Region'].replace('Korea, South', 'South Korea', inplace=True)
df['Country/Region'].replace('Viet Nam', 'Vietnam', inplace=True)
df['Country/Region'].replace('Macao SAR', 'Macau', inplace=True)
df['Country/Region'].replace('Russian Federation', 'Russia', inplace=True)
df['Country/Region'].replace('Republic of Moldova',
'Moldova', inplace=True)
df['Country/Region'].replace('Czechia', 'Czech Republic', inplace=True)
df['Country/Region'].replace('Congo (Kinshasa)', 'Congo', inplace=True)
df['Country/Region'].replace('Northern Ireland',
'United Kingdom', inplace=True)
df['Country/Region'].replace('Republic of Korea',
'South Korea', inplace=True)
df['Country/Region'].replace('Congo (Brazzaville)', 'Congo', inplace=True)
df['Country/Region'].replace('Taipei and environs', 'Taiwan', inplace=True)
df['Country/Region'].replace('Others', 'Cruise Ship', inplace=True)
df['Province/State'].replace('Cruise Ship',
'Diamond Princess cruise ship', inplace=True)
df['Province/State'].replace('From Diamond Princess',
'Diamond Princess cruise ship', inplace=True)
# Replace old reporting standards
df['Province/State'].replace('Chicago', 'Illinois', inplace=True)
df['Province/State'].replace('Chicago, IL', 'Illinois', inplace=True)
df['Province/State'].replace('Cook County, IL', 'Illinois', inplace=True)
df['Province/State'].replace('Boston, MA', 'Massachusetts', inplace=True)
df['Province/State'].replace(' Norfolk County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Suffolk County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Middlesex County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Norwell County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Plymouth County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Norfolk County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Berkshire County, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Unknown Location, MA',
'Massachusetts', inplace=True)
df['Province/State'].replace('Los Angeles, CA', 'California', inplace=True)
df['Province/State'].replace('Orange, CA', 'California', inplace=True)
df['Province/State'].replace('Santa Clara, CA', 'California', inplace=True)
df['Province/State'].replace('San Benito, CA', 'California', inplace=True)
df['Province/State'].replace('Humboldt County, CA',
'California', inplace=True)
df['Province/State'].replace('Sacramento County, CA',
'California', inplace=True)
df['Province/State'].replace('Travis, CA (From Diamond Princess)',
'California', inplace=True)
df['Province/State'].replace('Placer County, CA',
'California', inplace=True)
df['Province/State'].replace('San Mateo, CA', 'California', inplace=True)
df['Province/State'].replace('Sonoma County, CA',
'California', inplace=True)
df['Province/State'].replace('Berkeley, CA', 'California', inplace=True)
df['Province/State'].replace('Orange County, CA',
'California', inplace=True)
df['Province/State'].replace('Contra Costa County, CA',
'California', inplace=True)
df['Province/State'].replace('San Francisco County, CA',
'California', inplace=True)
df['Province/State'].replace('Yolo County, CA', 'California', inplace=True)
df['Province/State'].replace('Santa Clara County, CA',
'California', inplace=True)
df['Province/State'].replace('San Diego County, CA',
'California', inplace=True)
df['Province/State'].replace('Travis, CA', 'California', inplace=True)
df['Province/State'].replace('Alameda County, CA',
'California', inplace=True)
df['Province/State'].replace('Madera County, CA',
'California', inplace=True)
df['Province/State'].replace('Santa Cruz County, CA',
'California', inplace=True)
df['Province/State'].replace('Fresno County, CA',
'California', inplace=True)
df['Province/State'].replace('Riverside County, CA',
'California', inplace=True)
df['Province/State'].replace('Shasta County, CA',
'California', inplace=True)
df['Province/State'].replace('Seattle, WA', 'Washington', inplace=True)
df['Province/State'].replace('Snohomish County, WA',
'Washington', inplace=True)
df['Province/State'].replace('King County, WA', 'Washington', inplace=True)
df['Province/State'].replace('Unassigned Location, WA',
'Washington', inplace=True)
df['Province/State'].replace('Clark County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Jefferson County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Pierce County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Kittitas County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Grant County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Spokane County, WA',
'Washington', inplace=True)
df['Province/State'].replace('Tempe, AZ', 'Arizona', inplace=True)
df['Province/State'].replace('Maricopa County, AZ',
'Arizona', inplace=True)
df['Province/State'].replace('Pinal County, AZ', 'Arizona', inplace=True)
df['Province/State'].replace('Madison, WI', 'Wisconsin', inplace=True)
df['Province/State'].replace('San Antonio, TX', 'Texas', inplace=True)
df['Province/State'].replace('Lackland, TX', 'Texas', inplace=True)
df['Province/State'].replace('Lackland, TX (From Diamond Princess)',
'Texas', inplace=True)
df['Province/State'].replace('Harris County, TX', 'Texas', inplace=True)
df['Province/State'].replace('Fort Bend County, TX', 'Texas', inplace=True)
df['Province/State'].replace('Montgomery County, TX',
'Texas', inplace=True)
df['Province/State'].replace('Collin County, TX', 'Texas', inplace=True)
df['Province/State'].replace('Ashland, NE', 'Nebraska', inplace=True)
df['Province/State'].replace('Omaha, NE (From Diamond Princess)',
'Nebraska', inplace=True)
df['Province/State'].replace('Douglas County, NE',
'Nebraska', inplace=True)
df['Province/State'].replace('Portland, OR', 'Oregon', inplace=True)
df['Province/State'].replace('Umatilla, OR', 'Oregon', inplace=True)
df['Province/State'].replace('Klamath County, OR', 'Oregon', inplace=True)
df['Province/State'].replace('Douglas County, OR', 'Oregon', inplace=True)
df['Province/State'].replace('Marion County, OR', 'Oregon', inplace=True)
df['Province/State'].replace('Jackson County, OR ', 'Oregon', inplace=True)
df['Province/State'].replace('Washington County, OR',
'Oregon', inplace=True)
df['Province/State'].replace('Providence, RI',
'Rhode Island', inplace=True)
df['Province/State'].replace('Providence County, RI',
'Rhode Island', inplace=True)
df['Province/State'].replace('Grafton County, NH',
'New Hampshire', inplace=True)
df['Province/State'].replace('Rockingham County, NH',
'New Hampshire', inplace=True)
df['Province/State'].replace('Hillsborough, FL', 'Florida', inplace=True)
df['Province/State'].replace('Sarasota, FL', 'Florida', inplace=True)
df['Province/State'].replace('Santa Rosa County, FL',
'Florida', inplace=True)
df['Province/State'].replace('Broward County, FL', 'Florida', inplace=True)
df['Province/State'].replace('Lee County, FL', 'Florida', inplace=True)
df['Province/State'].replace('Volusia County, FL', 'Florida', inplace=True)
df['Province/State'].replace('Manatee County, FL', 'Florida', inplace=True)
df['Province/State'].replace('Okaloosa County, FL',
'Florida', inplace=True)
df['Province/State'].replace('Charlotte County, FL',
'Florida', inplace=True)
df['Province/State'].replace('New York City, NY', 'New York', inplace=True)
df['Province/State'].replace('Westchester County, NY',
'New York', inplace=True)
df['Province/State'].replace('Queens County, NY', 'New York', inplace=True)
df['Province/State'].replace('New York County, NY',
'New York', inplace=True)
df['Province/State'].replace('Nassau, NY', 'New York', inplace=True)
df['Province/State'].replace('Nassau County, NY', 'New York', inplace=True)
df['Province/State'].replace('Rockland County, NY',
'New York', inplace=True)
df['Province/State'].replace('Saratoga County, NY',
'New York', inplace=True)
df['Province/State'].replace('Suffolk County, NY',
'New York', inplace=True)
df['Province/State'].replace('Ulster County, NY', 'New York', inplace=True)
df['Province/State'].replace('Fulton County, GA', 'Georgia', inplace=True)
df['Province/State'].replace('Floyd County, GA', 'Georgia', inplace=True)
df['Province/State'].replace('Polk County, GA', 'Georgia', inplace=True)
df['Province/State'].replace('Cherokee County, GA',
'Georgia', inplace=True)
df['Province/State'].replace('Cobb County, GA', 'Georgia', inplace=True)
df['Province/State'].replace('Wake County, NC',
'North Carolina', inplace=True)
df['Province/State'].replace('Chatham County, NC',
'North Carolina', inplace=True)
df['Province/State'].replace('Bergen County, NJ',
'New Jersey', inplace=True)
df['Province/State'].replace('Hudson County, NJ',
'New Jersey', inplace=True)
df['Province/State'].replace('Clark County, NV', 'Nevada', inplace=True)
df['Province/State'].replace('Washoe County, NV', 'Nevada', inplace=True)
df['Province/State'].replace('Williamson County, TN',
'Tennessee', inplace=True)
df['Province/State'].replace('Davidson County, TN',
'Tennessee', inplace=True)
df['Province/State'].replace('Shelby County, TN',
'Tennessee', inplace=True)
df['Province/State'].replace('Montgomery County, MD',
'Maryland', inplace=True)
df['Province/State'].replace('Harford County, MD',
'Maryland', inplace=True)
df['Province/State'].replace('Denver County, CO', 'Colorado', inplace=True)
df['Province/State'].replace('Summit County, CO', 'Colorado', inplace=True)
df['Province/State'].replace('Douglas County, CO',
'Colorado', inplace=True)
df['Province/State'].replace('El Paso County, CO',
'Colorado', inplace=True)
df['Province/State'].replace('Delaware County, PA',
'Pennsylvania', inplace=True)
df['Province/State'].replace('Wayne County, PA',
'Pennsylvania', inplace=True)
df['Province/State'].replace('Montgomery County, PA',
'Pennsylvania', inplace=True)
df['Province/State'].replace('Fayette County, KY',
'Kentucky', inplace=True)
df['Province/State'].replace('Jefferson County, KY',
'Kentucky', inplace=True)
df['Province/State'].replace('Harrison County, KY',
'Kentucky', inplace=True)
df['Province/State'].replace('Marion County, IN', 'Indiana', inplace=True)
df['Province/State'].replace('Hendricks County, IN',
'Indiana', inplace=True)
df['Province/State'].replace('Ramsey County, MN',
'Minnesota', inplace=True)
df['Province/State'].replace('Carver County, MN',
'Minnesota', inplace=True)
df['Province/State'].replace('Fairfield County, CT',
'Connecticut', inplace=True)
df['Province/State'].replace('Charleston County, SC',
'South Carolina', inplace=True)
df['Province/State'].replace('Spartanburg County, SC',
'South Carolina', inplace=True)
df['Province/State'].replace('Kershaw County, SC',
'South Carolina', inplace=True)
df['Province/State'].replace('Davis County, UT', 'Utah', inplace=True)
df['Province/State'].replace('Honolulu County, HI', 'Hawaii', inplace=True)
df['Province/State'].replace('Tulsa County, OK', 'Oklahoma', inplace=True)
df['Province/State'].replace('Fairfax County, VA',
'Virginia', inplace=True)
df['Province/State'].replace('St. Louis County, MO',
'Missouri', inplace=True)
df['Province/State'].replace('Unassigned Location, VT',
'Vermont', inplace=True)
df['Province/State'].replace('Bennington County, VT',
'Vermont', inplace=True)
df['Province/State'].replace('Johnson County, IA', 'Iowa', inplace=True)
df['Province/State'].replace('Jefferson Parish, LA',
'Louisiana', inplace=True)
df['Province/State'].replace('Johnson County, KS', 'Kansas', inplace=True)
df['Province/State'].replace('Washington, D.C.',
'District of Columbia', inplace=True)
# Fill missing values as 0
df['Confirmed'] = df['Confirmed'].fillna(0).astype(int)
df['Deaths'] = df['Deaths'].fillna(0).astype(int)
df['Recovered'] = df['Recovered'].fillna(0).astype(int)
# Replace missing values for latitude and longitude
df['Latitude'] = df['Latitude'].fillna(df.groupby(
'Province/State')['Latitude'].transform('mean'))
df['Longitude'] = df['Longitude'].fillna(df.groupby(
'Province/State')['Longitude'].transform('mean'))
return pd.concat(files, axis=0, ignore_index=True, sort=False)
def etl(layout='time_series', source='web', update='manual'):
if layout == 'time_series':
df = load_time_series(source=source, update=update)
if isinstance(df, str):
return df
elif layout == 'daily_reports':
df = load_daily_reports(source=source)
# create Active cases column
df['Active'] = df['Confirmed'] - df['Deaths'] - df['Recovered']
# Re-order the columns for readability
df = df[['date',
'Country/Region',
'Province/State',
'Admin2',
'Latitude',
'Longitude',
'Confirmed',
'Active',
'Deaths',
'Recovered']]
return df
def worldwide(data):
print('processing worldwide')
df = data.groupby(['date', 'Country/Region'], as_index=False).agg({'Latitude': 'mean',
'Longitude': 'mean',
'Confirmed': 'sum',
'Deaths': 'sum',
'Recovered': 'sum',
'Active': 'sum'})
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
# Manually change some country centroids which are mislocated due to far off colonies
df.loc[df['Country/Region'] == 'US', 'Latitude'] = 39.810489
df.loc[df['Country/Region'] == 'US', 'Longitude'] = -98.555759
df.loc[df['Country/Region'] == 'France', 'Latitude'] = 46.2276
df.loc[df['Country/Region'] == 'France', 'Longitude'] = 2.2137
df.loc[df['Country/Region'] == 'United Kingdom', 'Latitude'] = 55.3781
df.loc[df['Country/Region'] == 'United Kingdom', 'Longitude'] = -3.4360
df.loc[df['Country/Region'] == 'Denmark', 'Latitude'] = 56.2639
df.loc[df['Country/Region'] == 'Denmark', 'Longitude'] = 9.5018
df.loc[df['Country/Region'] == 'Netherlands', 'Latitude'] = 52.1326
df.loc[df['Country/Region'] == 'Netherlands', 'Longitude'] = 5.2913
df.loc[df['Country/Region'] == 'Canada', 'Latitude'] = 56.1304
df.loc[df['Country/Region'] == 'Canada', 'Longitude'] = -106.346800
return df
def us(data):
print('processing US')
df = data[data['Country/Region'] == 'US']
df = df.groupby(['date', 'Province/State'], as_index=False).agg({'Confirmed': 'sum',
'Deaths': 'sum',
'Recovered': 'sum',
'Active': 'sum'})
df = df.merge(pd.read_csv('data/geo_us.csv'),
left_on='Province/State', right_on='Province/State', how='left')
df = df.rename(columns={'Province/State': 'Country/Region'})
df = df[['date',
'Country/Region',
'Latitude',
'Longitude',
'Confirmed',
'Active',
'Deaths',
'Recovered']].sort_values(['date', 'Country/Region'])
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
return df
def eu(data):
print('processing Europe')
eu = ['Albania', 'Andorra', 'Austria', 'Belarus', 'Belgium',
'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Cyprus',
'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France',
'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy',
'Kosovo', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg',
'Malta', 'Moldova', 'Monaco', 'Montenegro', 'Netherlands',
'North Macedonia', 'Norway', 'Poland', 'Portugal', 'Romania',
'San Marino', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden',
'Switzerland', 'Turkey', 'Ukraine', 'United Kingdom',
'Vatican City']
df = data[data['Country/Region'].isin(eu)]
df = df.groupby(['date', 'Country/Region'], as_index=False).agg({'Latitude': 'mean',
'Longitude': 'mean',
'Confirmed': 'sum',
'Deaths': 'sum',
'Recovered': 'sum',
'Active': 'sum'})
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
# Manually change some country centroids which are mislocated due to far off colonies
df.loc[df['Country/Region'] == 'US', 'Latitude'] = 39.810489
df.loc[df['Country/Region'] == 'US', 'Longitude'] = -98.555759
df.loc[df['Country/Region'] == 'France', 'Latitude'] = 46.2276
df.loc[df['Country/Region'] == 'France', 'Longitude'] = 2.2137
df.loc[df['Country/Region'] == 'United Kingdom', 'Latitude'] = 55.3781
df.loc[df['Country/Region'] == 'United Kingdom', 'Longitude'] = -3.4360
df.loc[df['Country/Region'] == 'Denmark', 'Latitude'] = 56.2639
df.loc[df['Country/Region'] == 'Denmark', 'Longitude'] = 9.5018
df.loc[df['Country/Region'] == 'Netherlands', 'Latitude'] = 52.1326
df.loc[df['Country/Region'] == 'Netherlands', 'Longitude'] = 5.2913
df.loc[df['Country/Region'] == 'Canada', 'Latitude'] = 59.050000
df.loc[df['Country/Region'] == 'Canada', 'Longitude'] = -112.833333
return df
def china(data):
print('processing China')
df = data[data['Country/Region'] == 'China']
df = df.drop(['Country/Region', 'Admin2'], axis=1)
df = df.rename(columns={'Province/State': 'Country/Region'})
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
return df
def us_county(data):
print('processing US counties')
df = data[data['Country/Region'] == 'US']
df = df.assign(key=df['Admin2'] + ' County, ' + df['Province/State'])
df = df.drop('Country/Region', axis=1)
df = df.rename(columns={'key': 'Country/Region'})
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
return df
def us_county_compressed(data, df_us):
'''
Same as function us_county(data) except that this compresses the data by
including only county-level data for the most recent date and then for all
other dates state-level data is used. Fixes the issue where the US map
causes Heroku to time-out while loading the web page.
'''
print('processing US counties')
df = data[data['Country/Region'] == 'US']
# Only keep county-level data for previous 8 days for share_of_last_week
most_recent = df['date'].max()
df = df[df['date'] > most_recent - pd.Timedelta(days=8)]
df = df.assign(key=df['Admin2'] + ' County, ' + df['Province/State'])
df = df.drop('Country/Region', axis=1)
df = df.rename(columns={'key': 'Country/Region'})
df['share_of_last_week'] = 100 * (df['Confirmed'] - df.groupby(
'Country/Region')['Confirmed'].shift(7, fill_value=0)) / df['Confirmed']
df['share_of_last_week'] = df['share_of_last_week'].replace(
[np.inf, -np.inf], np.nan).fillna(0)
df.loc[df['share_of_last_week'] < 0, 'share_of_last_week'] = 0
df['percentage'] = df['share_of_last_week'].apply(
lambda x: '{:.1f}'.format(x))
# Only keep county-level data for most recent date
# then merge with state-level data for previous dates
df = df[df['date'] == most_recent]
# df_us['date'] = pd.to_datetime(df_us['date'])
df_us = df_us[df_us['date'] < most_recent]
df = pd.concat([df, df_us]).sort_values('date')
df = df[['date', 'Country/Region', 'Latitude', 'Longitude', 'Confirmed',
'Deaths', 'Recovered', 'Active', 'share_of_last_week', 'percentage']]
return df
def global_population():
# source: United Nations, https://population.un.org/wpp/Download/Standard/CSV/
# 2019 data
print()
print('loading population')
pop_global = pd.read_csv('data/WPP2019_TotalPopulationBySex.csv')
pop_global = pop_global[(pop_global['Variant'] == 'Medium') & (
pop_global['Time'] == 2020)][['Location', 'PopTotal']].reset_index(drop=True)
pop_global['population'] = (pop_global['PopTotal'] * 1000).astype(int)
pop_global['region'] = pop_global['Location']
pop_global = pop_global[['region', 'population']]
return pop_global
def us_population():
# source: US Census, https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html
# 2019 data projected from 2010 Census
pop_us = pd.read_csv('data/nst-est2019-alldata.csv')
pop_us = pop_us[['NAME', 'POPESTIMATE2019']]
pop_us['region'] = pop_us['NAME']
pop_us['population'] = pop_us['POPESTIMATE2019']
pop_us = pop_us[['region', 'population']]
return pop_us
def china_population(pop_global):
# source: National Bureau of Statistics of China, http://data.stats.gov.cn/english/easyquery.htm?cn=E0103
# Hong Kong source: World Bank, https://data.worldbank.org/indicator/SP.POP.TOTL?locations=HK
# 2018 data
pop_china = pd.read_csv('data/AnnualbyProvince.csv',
header=3, encoding='ISO-8859-1')
pop_china = pop_china[['Region', '2018']].iloc[:31]
pop_china['region'] = pop_china['Region']
pop_china['population'] = (pop_china['2018'] * 10000).astype(int)
pop_china = pop_china[['region', 'population']]
pop_china.loc[31] = ['Hong Kong', pop_global[pop_global['region']
== 'China, Hong Kong SAR'].squeeze()['population']]
pop_china.loc[32] = ['Macau', pop_global[pop_global['region']
== 'China, Macao SAR'].squeeze()['population']]
return pop_china
def population_to_worldwide(df, pop_global):
df = pd.merge(df, pop_global, left_on='Country/Region',
right_on='region', how='left').drop(columns='region')
# correct mismatched country names
df.loc[df['Country/Region'] == 'Bolivia', ['population']] = pop_global.loc[pop_global['region']
== 'Bolivia (Plurinational State of)'].squeeze()['population']
df.loc[df['Country/Region'] == 'Brunei', ['population']
] = pop_global.loc[pop_global['region'] == 'Brunei Darussalam'].squeeze()['population']
df.loc[df['Country/Region'] == 'Burma', ['population']
] = pop_global.loc[pop_global['region'] == 'Myanmar'].squeeze()['population']
df.loc[df['Country/Region'] == 'Congo (Brazzaville)', ['population']
] = pop_global.loc[pop_global['region'] == 'Congo'].squeeze()['population']
df.loc[df['Country/Region'] == 'Congo (Kinshasa)', ['population']] = pop_global.loc[pop_global['region']
== 'Democratic Republic of the Congo'].squeeze()['population']
df.loc[df['Country/Region'] == "Cote d'Ivoire", ['population']
] = pop_global.loc[pop_global['region'] == "Côte d'Ivoire"].squeeze()['population']
df.loc[df['Country/Region'] == 'Iran', ['population']] = pop_global.loc[pop_global['region']
== 'Iran (Islamic Republic of)'].squeeze()['population']
# source: https://data.worldbank.org/country/kosovo
df.loc[df['Country/Region'] == 'Kosovo', ['population']] = 1845000
df.loc[df['Country/Region'] == 'South Korea', ['population']
] = pop_global.loc[pop_global['region'] == 'Republic of Korea'].squeeze()['population']
df.loc[df['Country/Region'] == 'Laos', ['population']] = pop_global.loc[pop_global['region']
== "Lao People's Democratic Republic"].squeeze()['population']
df.loc[df['Country/Region'] == 'Moldova', ['population']
] = pop_global.loc[pop_global['region'] == 'Republic of Moldova'].squeeze()['population']
df.loc[df['Country/Region'] == 'Russia', ['population']
] = pop_global.loc[pop_global['region'] == 'Russian Federation'].squeeze()['population']
df.loc[df['Country/Region'] == 'Taiwan', ['population']] = pop_global.loc[pop_global['region']
== 'China, Taiwan Province of China'].squeeze()['population']
df.loc[df['Country/Region'] == 'Tanzania', ['population']] = pop_global.loc[pop_global['region']
== 'United Republic of Tanzania'].squeeze()['population']
df.loc[df['Country/Region'] == 'Venezuela', ['population']] = pop_global.loc[pop_global['region']
== 'Venezuela (Bolivarian Republic of)'].squeeze()['population']
df.loc[df['Country/Region'] == 'Vietnam', ['population']
] = pop_global.loc[pop_global['region'] == 'Viet Nam'].squeeze()['population']
df.loc[df['Country/Region'] == 'Syria', ['population']
] = pop_global.loc[pop_global['region'] == 'Syrian Arab Republic'].squeeze()['population']
df.loc[(df['Country/Region'] == 'US'), ['population']] = pop_global.loc[pop_global['region']
== 'United States of America'].squeeze()['population']
df['population'] = df['population'] / 100000
return df
def population_to_us(df, pop_us):
df = pd.merge(df, pop_us, left_on='Country/Region',
right_on='region', how='left').drop('region', axis=1)
df.loc[df['Country/Region'] == 'Recovered', ['population']
] = pop_us[pop_us['region'] == 'United States'].squeeze()['population']
df['population'] = df['population'] / 100000
return df
def population_to_eu(df, pop_global):
df = pd.merge(df, pop_global, left_on='Country/Region',
right_on='region', how='left').drop('region', axis=1)
# source: https://data.worldbank.org/country/kosovo
df.loc[df['Country/Region'] == 'Kosovo', ['population']] = 1845000
df.loc[df['Country/Region'] == 'Moldova', ['population']
] = pop_global.loc[pop_global['region'] == 'Republic of Moldova'].squeeze()['population']
df['population'] = df['population'] / 100000
return df
def population_to_china(df, pop_china):
df = pd.merge(df, pop_china, left_on='Country/Region',
right_on='region', how='left').drop('region', axis=1)
df['population'] = df['population'] / 100000
return df
def main(update):
data = etl('time_series', 'web', update)
if isinstance(data, str):
return
# dashboard_data.csv is now unused in app.py, no longer need to save
# data.to_csv('data/dashboard_data.csv', index=False)
pop_global = global_population()
pop_us = us_population()
pop_china = china_population(pop_global)
df_worldwide = worldwide(data)
df_worldwide = population_to_worldwide(df_worldwide, pop_global)
df_worldwide.to_csv('data/df_worldwide.csv', index=False)
df_us = us(data)
df_us = population_to_us(df_us, pop_us)
df_us.to_csv('data/df_us.csv', index=False)
df_eu = eu(data)
df_eu = population_to_eu(df_eu, pop_global)
df_eu.to_csv('data/df_eu.csv', index=False)
df_china = china(data)
df_china = population_to_china(df_china, pop_china)
df_china.to_csv('data/df_china.csv', index=False)
df_us_county = us_county(data) # full historical US county-level data
# df_us_county = us_county_compressed(data, df_us) # historical US state-level data
df_us_county.to_csv('data/df_us_county.csv', index=False)
if __name__ == '__main__':
if len(sys.argv) == 2:
main(update=str(sys.argv[1]))
else:
main(update='manual')