Export 101 rows to a notebook
You can export this data to a Jupyter or Observable notebook by copying and pasting the following:
Jupyter
Make sure you have Pandas. Import it in a cell like this:
import pandasIf this shows an error you can run
%pip install pandas
in a notebook cell to install it.
Now paste the following into a cell to load the 101 rows into a DataFrame called df
:
df = pandas.read_json( "https://covid-19.datasettes.com/covid.json?p0=monthly&p1=6&sql=select+rowid%2C+country%2C+region%2C+region_code%2C+start_date%2C+end_date%2C+days%2C+year%2C+week%2C+population%2C+total_deaths%2C+covid_deaths%2C+expected_deaths%2C+excess_deaths%2C+non_covid_deaths%2C+covid_deaths_per_100k%2C+excess_deaths_per_100k%2C+excess_deaths_pct_change%2C+cadence%2C+month%2C+total_deaths_per_7_days%2C+covid_deaths_per_7_days%2C+expected_deaths_per_7_days%2C+excess_deaths_per_7_days%2C+non_covid_deaths_per_7_days%2C+covid_deaths_per_100k_per_7_days%2C+excess_deaths_per_100k_per_7_days%2C+quarter+from+economist_excess_deaths+where+%22cadence%22+%3D+%3Ap0+and+%22month%22+%3D+%3Ap1+order+by+covid_deaths_per_7_days+limit+101&_shape=array" )
Run df
in a new cell to see the table.
Observable
Import the data into a variable called rows
like this:
rows = d3.json( "https://covid-19.datasettes.com/covid.json?p0=monthly&p1=6&sql=select+rowid%2C+country%2C+region%2C+region_code%2C+start_date%2C+end_date%2C+days%2C+year%2C+week%2C+population%2C+total_deaths%2C+covid_deaths%2C+expected_deaths%2C+excess_deaths%2C+non_covid_deaths%2C+covid_deaths_per_100k%2C+excess_deaths_per_100k%2C+excess_deaths_pct_change%2C+cadence%2C+month%2C+total_deaths_per_7_days%2C+covid_deaths_per_7_days%2C+expected_deaths_per_7_days%2C+excess_deaths_per_7_days%2C+non_covid_deaths_per_7_days%2C+covid_deaths_per_100k_per_7_days%2C+excess_deaths_per_100k_per_7_days%2C+quarter+from+economist_excess_deaths+where+%22cadence%22+%3D+%3Ap0+and+%22month%22+%3D+%3Ap1+order+by+covid_deaths_per_7_days+limit+101&_shape=array" )