#!/usr/bin/env python
import pandas
import datetime
from covid_io import read_argv
from utils import dataframe_output
# Read CSV file from covidtracking's GitHub project
data = read_argv()
# Rename the appropriate columns
data = data.rename(
columns={
'date': 'Date',
'prname': '_RegionLabel',
'numconf': 'Confirmed',
'numdeaths': 'Deaths',
'numtested': 'Tested'
})
# Convert date to datetime object
data['Date'] = data['Date'].apply(lambda date: datetime.datetime.strptime(
date, '%d-%m-%Y').date().isoformat())
# Output the results
dataframe_output(data, 'CA')
#!/usr/bin/env python
import sys
from datetime import datetime, timedelta
from pandas import isna
from covid_io import read_argv
from utils import dataframe_output, merge_previous
# Confirmed and deaths come from different CSV files, parse them separately first
confirmed, deaths, prev_data = read_argv()
confirmed = confirmed.rename(columns={
'fecha': 'Date',
'CCAA': '_RegionLabel',
'total': 'Confirmed'
})
deaths = deaths.rename(columns={
'fecha': 'Date',
'CCAA': '_RegionLabel',
'total': 'Deaths'
})
# Now we can simply join them into the same table
df = confirmed.merge(deaths)
# Parse date into a datetime object
df['Date'] = df['Date'].apply(lambda date: datetime.fromisoformat(date).date())
# Add the country code to all records
df['CountryCode'] = 'ES'
# Convert dates to ISO format
#!/usr/bin/env python
'''
This script loads the latest JSON from covidtracking.com website and extracts
the confirmed cases, deaths and total tests for each state.
Credit to the covidtracking.com team for scraping the data from each state.
'''
import sys
import datetime
from covid_io import read_argv
from utils import dataframe_output
# Read CSV file from covidtracking's GitHub project
df = read_argv()
# Rename the appropriate columns
df = df.rename(
columns={
'date': 'Date',
'state': 'RegionCode',
'positive': 'Confirmed',
'death': 'Deaths',
'total': 'Tested'
})
# Convert date to ISO format
df['Date'] = df['Date'].apply(lambda date: datetime.datetime.strptime(
str(date), '%Y%m%d').date().isoformat())
# Output the results
#!/usr/bin/env python
from datetime import datetime
from pandas import DataFrame
from covid_io import read_argv
from utils import dataframe_output
# Read data from GitHub repo
confirmed, deaths = read_argv()
for df in (confirmed, deaths):
df.rename(columns={'Unnamed: 1': 'RegionCode'}, inplace=True)
df.set_index('RegionCode', inplace=True)
# Transform the data from non-tabulated format to record format
records = []
for region_code in confirmed.index.unique():
for col in confirmed.columns[1:]:
date = col + '/' + str(datetime.now().year)
date = datetime.strptime(date, '%d/%m/%Y').date().isoformat()
records.append({
'Date': date,
'RegionCode': region_code,
'Confirmed': confirmed.loc[region_code, col],
'Deaths': deaths.loc[region_code, col]})
df = DataFrame.from_records(records)
# Output the results
dataframe_output(df, 'BR')
#!/usr/bin/env python
import sys
from covid_io import read_argv
from utils import dataframe_output, datetime_isoformat
# Retrieve the CSV files from https://covid19.isciii.es
df = read_argv(encoding='ISO-8859-1').rename(columns={
'FECHA': 'Date',
'CCAA': 'RegionCode',
'Fallecidos': 'Deaths'
}).dropna(subset=['Date'])
# Add the country code to all records
df['CountryCode'] = 'ES'
# Confirmed cases are split across 3 columns
confirmed_columns = ['CASOS', 'PCR+', 'TestAc+']
for col in confirmed_columns:
df[col] = df[col].fillna(0)
df['Confirmed'] = df.apply(lambda x: sum([x[col]
for col in confirmed_columns]),
axis=1)
# Convert dates to ISO format
df['Date'] = df['Date'].apply(
lambda date: datetime_isoformat(date, '%d/%m/%Y'))
# Country-wide is the sum of all regions
region_level = df
country_level = df.groupby(['Date', 'CountryCode']).sum().reset_index()
#!/usr/bin/env python
import sys
from datetime import datetime, timedelta
from pandas import isna
from covid_io import read_argv
from utils import dataframe_output, merge_previous
# Retrieve the CSV files from GitHub
df, prev_data = read_argv()
df = df.rename(columns={
'fecha': 'Date',
'casos_total': 'Confirmed',
'fallecimientos': 'Deaths'
})
# Parse date into a datetime object
df['Date'] = df['Date'].apply(lambda date: datetime.fromisoformat(date).date())
# Add the country code to all records
df['CountryCode'] = 'ES'
# Convert dates to ISO format
df['Date'] = df['Date'].apply(lambda date: date.isoformat())
def filter_function(row):
return row['CountryCode'] == 'ES' and isna(row['RegionCode'])
# Merge with the prior data
#!/usr/bin/env python
import sys
from pathlib import Path
from datetime import datetime, timedelta
import pandas
from covid_io import read_argv
from utils import dataframe_output, merge_previous
df = read_argv().rename(
columns={
'data': 'Date',
'totale_casi': 'Confirmed',
'deceduti': 'Deaths',
'tamponi': 'Tested'
})
df['_RegionLabel'] = df['denominazione_regione']
# Parse date into a datetime object
df['Date'] = df['Date'].apply(lambda date: datetime.fromisoformat(date).date())
# Convert dates to ISO format
df['Date'] = df['Date'].apply(lambda date: date.isoformat())
# Add the country code to all records
df['CountryCode'] = 'IT'
# Output the results
dataframe_output(df, 'IT')
#!/usr/bin/env python
from datetime import datetime
from pandas import DataFrame
from covid_io import read_argv
from utils import dataframe_output, merge_previous
data = read_argv().transpose()
# Transform the data from non-tabulated format to record format
records = []
for idx, row in data.iterrows():
for code in data.columns:
subset = row[code]
record = {
'Date': idx.date().isoformat(),
'RegionCode': code,
'Confirmed': subset[0]
}
if len(subset) > 1:
record['Deaths'] = subset[1]
if len(subset) > 2:
record['Recovered'] = subset[2]
if len(subset) > 3:
record['Tested'] = subset[3]
records.append(record)
data = DataFrame.from_records(records)
# Output the results
dataframe_output(data, 'AU')
