PyCoronaData

Python wrapper for Johns Hopkins data.

Dependancies

• Python 3.7>
• Pandas
• GeoPandas

Features

• Fetch and construct a pandas dataframe based on Johns Hopkins data
• Estimate the number of recovery cases for each day
• Add countries informations (geometry, population size) for each country and clean country information from raw data
• Add some statistics such as lethaly rate, number of cases per 100.000 habitants for each country
• Afford a persistent mode for interactive application, allowing dynamic update

Install

Using pip

pip install git+https://github.com/jsgounot/pycoronadata.git

Or download / clone the github

git clone https://github.com/jsgounot/pycoronadata.git
cd pycoronadata
python setup.py install --user

Quick manual

Produce a simple dataframe from John Hopkins raw data with longitude and latitude as pivot points

from pycoronadata import CoronaData
cd = CoronaData(["Lat", "Long"])
print (cd.cdf)

Combine both raw data and geographical data

from pycoronadata import GeoCoronaData

# Default pivot is Country
cd = GeoCoronaData()

# Extract data from report 58 with values for missing countries
cd.data_from_day(58, report=True, fill=True)

# Same but with continents instead of country
cd.data_from_day(58, report=True, fill=True, geocolumn="Continent"))

# Grab data from a specific location 
cd.data_from_geocol(select="Africa", geocolumn="Continent", fill=True)

Persistant mode : Load and save data into a file

from pycoronadata import PersistantGeoCoronaData

cd = PersistantGeoCoronaData(file_path)
cd.update()
cd.save()

About data columns

CoronaData and following

ID	Description
RepDays	Days passed since first report (2020-03-02)
Recovered	Number of recovered (see below for how it is calculated)
Active	Number of active cases
CODay	New confirmed cases of the day
REDay	New recovered cases of the day
DEDay	New deaths cases of the day
LRate	Lethality rate

GeoCoronaData and persistant mode

ID	Description
Country	Country related to each entry, confirmed using longitude and latitude data
ADMO_3	Country code
SubRegion	Entry sub region
REGION_WB	Entry world regions (i.e South Asia)
Continent	Entry continent
PopSize	Population size (2018) for either a country or a region
PrcCont	Percent of the population
CO10K	Number of confirmed per 100,000 habitants
DE10K	Number of deaths per 100,000 habitants
RE10K	Number of recovered per 100,000 habitants
AC10K	Number of actives per 100,000 habitants

How is calculated the number of recovery ?

Edit : Recovered cases are back ! Previous option is still usable but by default (rtime = None) the CSSEGI file will be used.

Since this report, recovered cases are no longer provided. To get an estimation of recovered cases for each day / country, one can define a mean value of the disease period until recovery which by default is set to 14 days. With this, the number of recovered cases is then linked to both confirmed cases from X previous day and the number of deaths at a given time. Note that the value provided here is therefore only an estimation and does not reflect reality. To change the communicability period, modify the rtime parameter during instance construction.

See also

CoronaTools : Dashboard of corona data using bokeh