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data.py
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import pandas as pd
import numpy as np
from pathlib import Path
import itertools
import requests
from covid19.utils import state2initial
DATA_DIR = Path(__file__).resolve().parents[1] / 'data'
COVID_19_BY_CITY_URL=('https://raw.githubusercontent.com/wcota/covid19br/'
'master/cases-brazil-cities-time.csv')
IBGE_POPULATION_PATH=DATA_DIR / 'ibge_population.csv'
WORLD_POPULATION_PATH=DATA_DIR / 'country_population.csv'
SRAG_SUBNOTIFICATION_PATH= DATA_DIR / 'srag_death_subnotification.csv'
VULNERABLE_POPULATION_PATH=DATA_DIR / 'populao-em-risco.csv'
COVID_SAUDE_URL = ('https://raw.githubusercontent.com/3778/COVID-19/'
'master/data/latest_cases_ms.csv')
FIOCRUZ_URL = 'https://bigdata-covid19.icict.fiocruz.br/sd/dados_casos.csv'
LETHALITY_PATH=DATA_DIR / 'lethality_rates.csv'
def _prepare_fiocruz_data(df, by):
if by == 'country':
return (df.assign(country=np.where((df['name'].str.contains('^[\wA-z\wÀ-ú]')),
df['name'],
None)))
if by == 'state':
return (df.assign(state=np.where(df['name'].str.startswith('#BR'),
df['name'].str[5:],
None))
.replace({'state': state2initial}))
if by == 'city':
return (df.assign(city=np.where(df['name'].str.startswith('#Mun BR'),
df['name'].str[9:],
None))
.assign(city=lambda df: df['city'].str.rsplit(' ', 1)
.str.join('/')))
def _make_total_deaths(df, by):
df = df.assign(deaths=lambda df: df.groupby([by])['new_deaths'].cumsum())
return df
def load_cases(by, source='fiocruz'):
'''Load cases from wcota/covid19br or covid.saude.gov.br or fiocruz
Args:
by (string): either 'state' or 'city'.
Returns:
pandas.DataFrame
Examples:
>>> cases_city = load_cases('city')
>>> cases_city['São Paulo/SP']['newCases']['2020-03-20']
47
>>> cases_state = load_cases('state')
>>> cases_state['SP']['newCases']['2020-03-20']
110
>>> cases_ms = load_cases('state', source='ms')
>>> cases_ms['SP']['newCases']['2020-03-20']
110
'''
assert source in ['ms', 'wcota', 'fiocruz']
assert by in ['country', 'state', 'city']
if source == 'monitora':
assert by == 'state'
df = (pd.read_csv(COVID_MONITORA_URL,
sep=';',
parse_dates=['date'],
dayfirst=True)
.rename(columns={'casosNovos': 'newCases',
'casosAcumulados': 'totalCases',
'estado': 'state'}))
if source == 'ms':
assert by == 'state'
df = (pd.read_csv(COVID_SAUDE_URL,
sep=';',
parse_dates=['date'],
dayfirst=True)
.rename(columns={'casosNovos': 'newCases',
'casosAcumulados': 'totalCases',
'estado': 'state'}))
elif source == 'wcota':
df = (pd.read_csv(COVID_19_BY_CITY_URL, parse_dates=['date'])
.query("state != 'TOTAL'"))
elif source == 'fiocruz':
df = (pd.read_csv(FIOCRUZ_URL, parse_dates=['date'])
.rename(columns={'new_cases': 'newCases'})
.pipe(_prepare_fiocruz_data, by=by)
.assign(totalCases=lambda df: df.groupby([by])['newCases'].cumsum()))
df = _make_total_deaths(df, by)
return (df.groupby(['date', by])
[['newCases', 'totalCases', 'deaths']]
.sum()
.unstack(by)
.sort_index()
.swaplevel(axis=1)
.fillna(0)
.astype(int))
def load_population(by):
''''Load population from IBGE.
Args:
by (string): either 'state' or 'city'.
Returns:
pandas.DataFrame
Examples:
>>> load_population('state').head()
state
AC 881935
AL 3337357
AM 4144597
AP 845731
BA 14873064
Name: estimated_population, dtype: int64
>>> load_population('city').head()
city
Abadia de Goiás/GO 8773
Abadia dos Dourados/MG 6989
Abadiânia/GO 20042
Abaetetuba/PA 157698
Abaeté/MG 23237
Name: estimated_population, dtype: int64
'''
assert by in ['country', 'state', 'city']
if by == 'country':
return (pd.read_csv(WORLD_POPULATION_PATH)
.groupby('country')
['population']
.first())
else:
return (pd.read_csv(IBGE_POPULATION_PATH)
.rename(columns={'uf': 'state'})
.assign(city=lambda df: df.city + '/' + df.state)
.groupby(by)
['estimated_population']
.sum()
.sort_index())
def prepare_age_data(level, old_col, new_col):
BASE_URL = "http://api.sidra.ibge.gov.br/values/t/5918/p/201904/v/606/C58/all/f/n"
url = f"{BASE_URL}{level}"
r = requests.get(url)
df = pd.read_json(r.text)
df = (
df.rename(columns=df.iloc[0])
.drop(df.index[0])
.drop(columns=["Trimestre", "Variável", "Unidade de Medida"])
.rename(columns={"Grupo de idade": "g_idade", old_col: new_col})
)
df = df.pivot(new_col, columns="g_idade")["Valor"].reset_index()
df.columns.name = None
return df
def load_age_group_rate(granularity):
assert granularity in ["state", "country"]
if granularity == "state":
df = (
prepare_age_data("/N3/all", "Unidade da Federação", "state")
.replace({"state": state2initial})
.set_index("state")
.astype(int)
)
else:
df = (
prepare_age_data("/N1/all", "Brasil", "country")
.assign(country=lambda df: df["country"].str.replace(" - ", "/"))
.set_index(granularity)
.astype(int)
)
return (df.assign(Jovem= lambda df: (df['0 a 13 anos'] + df['14 a 17 anos'])/df['Total'])
.assign(Adulto= lambda df: (df['18 a 24 anos'] + df['25 a 39 anos'] + df['40 a 59 anos'])/df['Total'])
.assign(Idoso= lambda df: df['60 anos ou mais']/df['Total'])
.drop(df.columns[0:7], axis=1))
def load_lethality_rate():
return (pd.read_csv(LETHALITY_PATH)
.set_index('state')
.rename(columns={'adult_lethality': 'Adulto',
'elder_lethality': 'Idoso',
'young_lethality': 'Jovem'}))
def load_srag_death_subnotification():
return (
pd.read_csv(SRAG_SUBNOTIFICATION_PATH)
.set_index('state')
.to_dict()
['death_subnotification']
)
def load_vulnerable_population():
state_initials = {
'Acre': 'AC',
'Alagoas': 'AL',
'Amapá': 'AP',
'Amazonas': 'AM',
'Bahia': 'BA',
'Ceará': 'CE',
'Distrito Federal': 'DF',
'Espírito Santo': 'ES',
'Goiás': 'GO',
'Maranhão': 'MA',
'Mato Grosso': 'MT',
'Mato Grosso do Sul': 'MS',
'Minas Gerais': 'MG',
'Pará': 'PA',
'Paraíba': 'PB',
'Paraná': 'PR',
'Pernambuco': 'PE',
'Piauí': 'PI',
'Rio de Janeiro': 'RJ',
'Rio Grande do Norte': 'RN',
'Rio Grande do Sul': 'RS',
'Rondônia': 'RO',
'Roraima': 'RR',
'Santa Catarina': 'SC',
'São Paulo': 'SP',
'Sergipe': 'SE',
'Tocantins': 'TO'
}
return (
pd.read_csv(VULNERABLE_POPULATION_PATH)
.rename(columns={
'População adulta com pelo menos uma doença crônica não transmissível': 'chronic_disease_ratio',
'População idosa': 'elderly_ratio',
'UF': 'state',
})
.assign(state = lambda df: df['state'].apply(lambda k: state_initials.get(k, k)))
.set_index('state')
.assign(elderly_risk = lambda df: df['elderly_ratio'] / df['elderly_ratio']['Brasil'])
.assign(chronic_disease_risk = lambda df: df['chronic_disease_ratio'] / df['chronic_disease_ratio']['Brasil'])
)