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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 io
import requests
import json
def etl(source='web'):
if source == 'folder':
# Load files from folder
path = 'data'
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)
files.append(df)
elif 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")
print(file)
url = r'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/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\n' not in raw_string:
df.to_csv('data/{}.csv'.format(file), index=False)
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)
df = pd.concat(files, axis=0, ignore_index=True, sort=False)
# 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',
'North 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)
# South Korea data on March 10 seems to be mislabled as North Korea
df.loc[(df['Country/Region'] == 'North Korea') & (df['date']
== '03-10-2020'), 'Country/Region'] = 'South Korea'
# Re-order the columns for readability
df = df[['date',
'Country/Region',
'Province/State',
'Confirmed',
'Deaths',
'Recovered',
'Latitude',
'Longitude']]
# Fill missing values as 0; create Active cases column
df['Confirmed'] = df['Confirmed'].fillna(0).astype(int)
df['Deaths'] = df['Deaths'].fillna(0).astype(int)
df['Recovered'] = df['Recovered'].fillna(0).astype(int)
df['Active'] = df['Confirmed'] - df['Deaths'] - df['Recovered']
# 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 df
def views(df, states, eu, africa):
df_us = df[df['Province/State'].isin(states)]
df_eu = df[df['Country/Region'].isin(eu)]
df_af = df[df['Country/Region'].isin(africa)]
df_eu = df_eu.append(pd.DataFrame({'date': [pd.to_datetime('2020-01-22'), pd.to_datetime('2020-01-23')],
'Country/Region': ['France', 'France'],
'Province/State': [np.nan, np.nan],
'Confirmed': [0, 0],
'Deaths': [0, 0],
'Recovered': [0, 0],
'Latitude': [np.nan, np.nan],
'Longitude': [np.nan, np.nan],
'Active': [0, 0]})).sort_index()
df_us.drop('Country/Region', axis=1, inplace=True)
df_us.rename(columns={'Province/State': 'Country/Region'}, inplace=True)
return df_us, df_eu, df_af
def indicators(df, column):
value = df[df['date'] == df['date'].iloc[-1]][column].sum()
delta = df[df['date'] == df['date'].unique()[-2]][column].sum()
return value, delta
def infections(data):
df = pd.DataFrame()
df['x'] = data.groupby('date')['date'].first()
df['Confirmed'] = data.groupby('date')['Confirmed'].sum()
df['Active'] = data.groupby('date')['Active'].sum()
df['Recovered'] = data.groupby('date')['Recovered'].sum()
df['Deaths'] = data.groupby('date')['Deaths'].sum()
return df
def active_countries(data, regions):
df = pd.DataFrame()
for region in regions:
df['x_{}'.format(region)] = data[data['Country/Region']
== region].groupby('date')['date'].first()
df['y_{}'.format(region)] = data[data['Country/Region']
== region].groupby('date')['Active'].sum()
return df
def stacked(data, regions, scope):
df = pd.DataFrame()
for region in regions:
if data[(data['date'] == data['date'].iloc[-1]) & (data['Country/Region'] == region)]['Confirmed'].sum() > scope:
df['x_{}'.format(region)] = data[data['Country/Region']
== region].groupby('date')['date'].first()
for column in ['Confirmed', 'Active', 'Recovered', 'Deaths']:
df['y_{}_{}'.format(region, column)] = data[data['Country/Region']
== region].groupby('date')[column].sum()
return df
def map_data(data):
df_world_map = pd.DataFrame()
for date_index in range(len(df['date'].unique())):
date = df['date'].unique()[date_index]
df_world_map = data[data['date'] == date].groupby('Country/Region').agg({'Active': 'sum',
'Longitude': 'mean',
'Latitude': 'mean',
'Country/Region': 'first'})
# Manually change some country centroids which are mislocated due to far off colonies
df_world_map.loc[df_world_map['Country/Region']
== 'US', 'Latitude'] = 39.810489
df_world_map.loc[df_world_map['Country/Region']
== 'US', 'Longitude'] = -98.555759
df_world_map.loc[df_world_map['Country/Region']
== 'France', 'Latitude'] = 46.2276
df_world_map.loc[df_world_map['Country/Region']
== 'France', 'Longitude'] = -3.4360
df_world_map.loc[df_world_map['Country/Region']
== 'United Kingdom', 'Latitude'] = 55.3781
df_world_map.loc[df_world_map['Country/Region']
== 'United Kingdom', 'Longitude'] = 2.2137
df_world_map.loc[df_world_map['Country/Region']
== 'Denmark', 'Latitude'] = 56.2639
df_world_map.loc[df_world_map['Country/Region']
== 'Denmark', 'Longitude'] = 9.5018
df_world_map.loc[df_world_map['Country/Region']
== 'Netherlands', 'Latitude'] = 52.1326
df_world_map.loc[df_world_map['Country/Region']
== 'Netherlands', 'Longitude'] = 5.2913
return df_world_map
if __name__ == '__main__':
df = etl()
df.to_csv('dashboard_data.csv', index=False)
# available_countries = sorted(df['Country/Region'].unique())
# states = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',
# 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia', 'Florida',
# 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky',
# 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi',
# 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico',
# 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania',
# 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont',
# 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']
# 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_us, df_eu = views(df, states, eu)
# value_ww_confirmed, delta_ww_confirmed = indicators(df, 'Confirmed')
# value_ww_active, delta_ww_active = indicators(df, 'Active')
# value_ww_recovered, delta_ww_recovered = indicators(df, 'Recovered')
# value_ww_deaths, delta_ww_deaths = indicators(df, 'Deaths')
# df_worldwide_infections = infections(df)
# df_us_infections = infections(df_us)
# df_eu_infections = infections(df_eu)
# df_ww_active = active_countries(df, available_countries)
# df_us_active = active_countries(df_us, states)
# df_eu_active = active_countries(df_eu, eu)
# df_ww_stacked = stacked(df, available_countries, 1000)
# df_us_stacked = stacked(df_us, states, 20)
# df_eu_stacked = stacked(df_eu, states, 1000)
# df_world_map_ww =