COVID19 Data scraped from global sources

by

A very conprehensive collection of automatically scraped data.

coronadatascraper.com-covid19-1.2.24. Modified 2020-07-08T15:54:25

Resources | Packages | Documentation| Contacts| Data Dictionary

Resources

  • source_tidy. Scraped coronavirus data, with one row for each type of observation.
  • source_jhu. Scraped coronavirus data, in format similar to JHU
  • source. Scraped coronavirus data, with one row per region per day and one type of observation per column.

Documentation

The Corona Data Scraper scrapes data from a wide variety of sources to produce a dataset with both global scope and regional granularity. See the project page for more details.

Contacts

Data Dictionary

source_tidy | source | source_jhu

source_tidy

Column NameData TypeDescription
nametext
levelstring
citystringCity Name
countystringCounty Name
statetextState Name
countrystringCountry code
populationintegerTotal population of the region
latnumberLatitude
longnumberLongitude
aggregatestring
tzstring
datedateObservation Date
typestringType of observation: cases, deaths, recovered or active
valuenumberValue of the observatoin.

source

Column NameData TypeDescription
nametext
levelstring
citystringCity Name
countystringCounty Name
statetextState Name
countrystringCountry code
populationnumberTotal population of the region
latnumberLatitude
longnumberLongitude
urlstringSource Url
aggregatestring
tzstring
casesintegerSum of deaths, recoveries and active.
deathsintegerCumulative number of deaths
recoveredintegerCumulative number of recoveries
activeintegerCumulative number of active confirmed infections.
testedintegerNumber tested
growthfactornumberGrowth factor, compared to previous observation. 1+ growth rate
datedateObservation Date

source_jhu

Column NameData TypeDescription
nametext
levelstring
citystring
countytext
statetext
countrytext
latnumber
longnumber
populationinteger
urlstring
aggregatestring
tzstring
2020_01_22integer
2020_01_23integer
2020_01_24integer
2020_01_25integer
2020_01_26integer
2020_01_27integer
2020_01_28integer
2020_01_29integer
2020_01_30integer
2020_01_31integer
2020_02_01integer
2020_02_02integer
2020_02_03integer
2020_02_04integer
2020_02_05integer
2020_02_06integer
2020_02_07integer
2020_02_08integer
2020_02_09integer
2020_02_10integer
2020_02_11integer
2020_02_12integer
2020_02_13integer
2020_02_14integer
2020_02_15integer
2020_02_16integer
2020_02_17integer
2020_02_18integer
2020_02_19integer
2020_02_20integer
2020_02_21integer
2020_02_22integer
2020_02_23integer
2020_02_24integer
2020_02_25integer
2020_02_26integer
2020_02_27integer
2020_02_28integer
2020_02_29integer
2020_03_01integer
2020_03_02integer
2020_03_03integer
2020_03_04integer
2020_03_05integer
2020_03_06integer
2020_03_07integer
2020_03_08integer
2020_03_09integer
2020_03_10integer
2020_03_11integer
2020_03_12integer
2020_03_13integer
2020_03_14integer
2020_03_15integer
2020_03_16integer
2020_03_17integer
2020_03_18integer
2020_03_19integer
2020_03_20integer
2020_03_21integer
2020_03_22integer
2020_03_23integer
2020_03_24integer
2020_03_25integer
2020_03_26integer
2020_03_27integer
2020_03_28integer
2020_03_29integer
2020_03_30integer
2020_03_31integer
2020_04_01integer
2020_04_02integer
2020_04_03integer
2020_04_04integer
2020_04_05integer
2020_04_06integer
2020_04_07integer
2020_04_08integer
2020_04_09integer
2020_04_10integer
2020_04_11integer
2020_04_12integer
2020_04_13integer
2020_04_14integer
2020_04_15integer
2020_04_16integer
2020_04_17integer
2020_04_18integer
2020_04_19integer
2020_04_20integer
2020_04_21integer
2020_04_22integer
2020_04_23integer
2020_04_24integer

Packages

Accessing Data in Vanilla Pandas

import pandas as pd


source_tidy_df =  pd.read_csv('http://library.metatab.org/coronadatascraper.com-covid19-1.2.24/data/source_tidy.csv')
source_jhu_df =  pd.read_csv('http://library.metatab.org/coronadatascraper.com-covid19-1.2.24/data/source_jhu.csv')
source_df =  pd.read_csv('http://library.metatab.org/coronadatascraper.com-covid19-1.2.24/data/source.csv')

Accessing Package in Metapack

import metapack as mp
pkg = mp.open_package('http://library.metatab.org/coronadatascraper.com-covid19-1.2.24.csv')

# Create Dataframes
source_tidy_df = pkg.resource('source_tidy').dataframe()
source_jhu_df = pkg.resource('source_jhu').dataframe()
source_df = pkg.resource('source').dataframe()