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stats.py

import pandas as pd
import datetime
from iso3166 import countries
import gettext
import pycountry
french = gettext.translation('iso3166', pycountry.LOCALES_DIR, languages=['fr'])
french.install()

df = pd.read_csv('OxCGRT_Download_130420_173359_Full.csv')
df['Date'] = pd.to_datetime(df['Date'], format = '%Y%m%d')

days_since = [
    {'label': '1death', 'column': 'ConfirmedDeaths', 'threshold': 1},
    {'label': '10deaths', 'column': 'ConfirmedDeaths', 'threshold': 10},
    {'label': '100deaths', 'column': 'ConfirmedDeaths', 'threshold': 100},
    {'label': '1case', 'column': 'ConfirmedCases', 'threshold': 1},
    {'label': '10cases', 'column': 'ConfirmedCases', 'threshold': 10},
    {'label': '100cases', 'column': 'ConfirmedCases', 'threshold': 100},
]

policies = [
    {'label': 'School closing', 'column': 'S1', 'requireValue': 2, 'requireGeneral': True},
    {'label': 'Workplace closing', 'column': 'S2', 'requireValue': 2, 'requireGeneral': True},
    {'label': 'Cancel public events', 'column': 'S3', 'requireValue': 2, 'requireGeneral': False},
    {'label': 'Close public transport', 'column': 'S4', 'requireValue': 2, 'requireGeneral': True},
    {'label': 'Restrictions on internal movement', 'column': 'S6', 'requireValue': 2, 'requireGeneral': True},
    {'label': 'International travel controls', 'column': 'S7', 'requireValue': 3, 'requireGeneral': False},
    {'label': 'Testing framework', 'column': 'S12', 'requireValue': 2, 'requireGeneral': False},
    {'label': 'Contact tracing', 'column': 'S13', 'requireValue': 1, 'requireGeneral': False},  
]

OECD = [
    'AUS', 'AUT', 'BEL', 'CAN', 'CHL', 'CZE',
    'DNK', 'EST', 'FIN', 'FRA', 'DEU', 'GRC',
    'HUN', 'ISL', 'IRL', 'ISR', 'ITA', 'JPN',
    'KOR', 'LUX', 'MEX', 'NLD', 'NZL', 'NOR',
    'POL', 'PRT', 'SVK', 'SVN', 'ESP', 'SWE',
    'CHE', 'TUR', 'GBR', 'USA',
    'ARG', 'BRA', 'CHN', 'IND', 'IDN', 'RUS',
    'SAU', 'ZAF'
]

df = df[df['CountryCode'].isin(OECD)]
df['CountryCode2L'] = df['CountryCode'].map(lambda c: countries.get(c).alpha2)
df['CountryName'] = df['CountryName'].map(_)

references = pd.DataFrame()
for d in days_since:
    reference = df[df[d['column']] >= d['threshold']] \
        .groupby('CountryCode') \
        .agg({'Date': 'min'}) \
        .rename(columns={'Date': d['label']})

    references = references.merge(reference,
        how = 'right',
        left_index = True,
        right_index = True)

df = df.join(references, how = 'outer', on = 'CountryCode')

for d in days_since:
    df.loc[:, d['label']].fillna(datetime.datetime.now(), inplace = True)

for d in days_since:
    df['days_since_' + d['label']] = (df['Date'] - df[d['label']]).dt.days


# https://www.bsg.ox.ac.uk/sites/default/files/2020-04/BSG-WP-2020-031-v4.0_0.pdf

for policy in policies:
    col = policy['column']
    label = policy['label']
    if policy['requireGeneral']:
        df[label] = (df[col + '_' + label] >= policy['requireValue']) & (df[col + '_IsGeneral'] == 1)
    else:
        df[label] = (df[col + '_' + label] >= policy['requireValue'])

compact = pd.DataFrame()
for policy in policies:
    aggregate = {
        'days_since_' + d['label']: 'min' for d in days_since
    }
    aggregate['ConfirmedCases'] = 'min'
    aggregate['ConfirmedDeaths'] = 'min'
    
    pivot = df[df[policy['label']] == True] \
        .groupby('CountryCode') \
        .agg(aggregate)

    compact = compact.join(pivot.add_suffix('_' + policy['label']), how = 'outer')

compact = compact.join(
    df[['Date', 'CountryCode', 'CountryCode2L', 'CountryName', 'S12_Testing framework']] \
        .sort_values('Date', ascending = True) \
        .groupby('CountryCode') \
        .max(),
    how = 'outer'
)

compact.to_csv('compact.csv')
compact.to_json('compact.json')

describe = compact.describe()
describe.to_csv('describe.csv')
describe.to_json('describe.json')
#print(compact)