Export 86 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 86 rows into a DataFrame called df
:
df = pandas.read_json( "https://covid-19.datasettes.com/la-times.json?p0=No&sql=select+rowid%2C+county%2C+district%2C+enr_total%2C+elem_enr%2C+middle_enr%2C+high_enr%2C+elem_in_school%2C+middle_in_school%2C+high_in_school%2C+total_in_school%2C+reopening_plan%2C+operational_status_elem%2C+operational_status_middle%2C+operational_status_high%2C+is_reopening%2C+is_reopening_elem%2C+elem_reopening_date%2C+is_reopening_middle%2C+middle_reopening_date%2C+is_reopening_high%2C+high_reopening_date%2C+near_term_opening_date%2C+update_date+from+%5Bschool-districts-reopening%5D+where+%22is_reopening_elem%22+%3D+%3Ap0+order+by+rowid+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=No&sql=select+rowid%2C+county%2C+district%2C+enr_total%2C+elem_enr%2C+middle_enr%2C+high_enr%2C+elem_in_school%2C+middle_in_school%2C+high_in_school%2C+total_in_school%2C+reopening_plan%2C+operational_status_elem%2C+operational_status_middle%2C+operational_status_high%2C+is_reopening%2C+is_reopening_elem%2C+elem_reopening_date%2C+is_reopening_middle%2C+middle_reopening_date%2C+is_reopening_high%2C+high_reopening_date%2C+near_term_opening_date%2C+update_date+from+%5Bschool-districts-reopening%5D+where+%22is_reopening_elem%22+%3D+%3Ap0+order+by+rowid+limit+101&_shape=array" )