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Description:

rkicovid19csvparser 1.2.0

rki-covid19csv-parser
A small python module to work with the RKI_Covid19.csv files issued by the German RKI (Robert Koch Institut) on a daily basis.
Installation:
pip install rki-covid19csv-parser
Usage:
First steps:
Initialize the parser and load data from the RKI_Covid19.csv file. Because of the daily increasing file size this process can take a while.
import rki_covid19csv_parser

covid_cases = rki_covid19csv_parser.covid_cases()

covid_cases.load_rki_csv('path/to/csv')

Speeding up the loading process:
Once you have loaded the csv file it's possible to save the processed data to a file.
This can speed up the process of loading the data significantly if you whish to run your script more than once.
#save file.
covid_cases.save_toFile('desired/path')

#load file.
covid_cases.load_fromFile('path/to/saved/file')

Get the covid19 data:
Supported methods:
A description of the parameters can be found below.

method
description
returns

cumCases(date, region_id, date_type)
cumulated covid19 cases
Filter object

cumDeaths(date, region_id, date_type)
cumulated covid19 deaths
Filter object

newCases(date, region_id, date_type)
new covid19 cases
Filter object

newDeaths(date, region_id, date_type)
new covid19 deaths
Filter object

newCasesTimespan(date, region_id, date_type, timespan)
new covid19 cases in period
Filter object

newDeathsTimespan(date, region_id, date_type, timespan)
new covid19 deaths in period
Filter object

activeCases(date, region_id, date_type, days_infectious)
active covid19 cases
Filter object

sevenDayCaserate(date, region_id, date_type)
new covid19 cases in 7-days
Filter object

sevenDayIncidence(date, region_id, date_type)
new covid19 cases per 100k people in 7-days
Filter object

deathRate(date, region_id, days_infectious)
death rate (activeCases/newDeaths)
Filter object

Parameters:

parameter
type
description
example

date
str in iso-format, datetime.date obj, datetime.datetime obj
The desired date.
'2020-06-01 00:00:00'

region_id
str
A list of region-ids can be found here.
'0'

date_type
str
The date type to use. Meldedatum or Refdatum
'Meldedatum'

timespan
int
Number of last days to be included in calculation.
3

days_infectious
int
Number of days a case is considered as active.
14

Get your covid19 data in shape:
Each of the methods mentioned above returns an objct of the class Filter. You can use the following methods to get the data into your desired shape.

method
description
returns

values()
raw data
ndarray

by_cases(raw, decimals)
absolute number of cases
dict

by_age(frequency, decimals)
cases sorted into agegroups
dict

by_gender(frequency, decimals)
cases sorted by gender
dict

by_ageandgener(frequency, decimals)
cases sorted by age and gender
dict

parameter
input type
description
example

frequency
str
weather you want the absolute or relative number of cases
'absolute'

decimals
int
number of decimals
3

raw
bool
True to get raw values
True

Examples:
cases = covid_cases.cumCases(date='2021-04-29 00:00:00', region_id='01001', date_type='Meldedatum').by_gender(frequency='absolute')
print(cases)
>>> {'M': 1200, 'W': 1400, 'unbekannt': 130}

Example values!