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/la-times.json?p0=2022-05-05&sql=select+rowid%2C+%5BUnnamed%3A+0%5D%2C+school%2C+status%2C+color%2C+staff_student%2C+transmission%2C+testing_period%2C+tests%2C+pos%2C+pos_rate%2C+change%2C+cos%2C+cos_tests%2C+cos_pos%2C+cos_pos_rate%2C+cos_change%2C+lausd_tests%2C+lausd_pos%2C+lausd_pos_rate%2C+lausd_change%2C+date%2C+time%2C+staff_student_rate%2C+community_rate%2C+lac_rate%2C+community_schools%2C+community+from+%5Blausd-cases%5D+where+date(%22date%22)+%3D+%3Ap0+order+by+cos_pos+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/la-times.json?p0=2022-05-05&sql=select+rowid%2C+%5BUnnamed%3A+0%5D%2C+school%2C+status%2C+color%2C+staff_student%2C+transmission%2C+testing_period%2C+tests%2C+pos%2C+pos_rate%2C+change%2C+cos%2C+cos_tests%2C+cos_pos%2C+cos_pos_rate%2C+cos_change%2C+lausd_tests%2C+lausd_pos%2C+lausd_pos_rate%2C+lausd_change%2C+date%2C+time%2C+staff_student_rate%2C+community_rate%2C+lac_rate%2C+community_schools%2C+community+from+%5Blausd-cases%5D+where+date(%22date%22)+%3D+%3Ap0+order+by+cos_pos+limit+101&_shape=array" )