Export 100 rows to a notebook

Back to the rows

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 pandas
If 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 100 rows into a DataFrame called df:

df = pandas.read_json("https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations.json?_shape=array")

Run df in a new cell to see the table.

You can export all 3,131 rows using a single streaming CSV export like this:

df = pandas.read_csv("https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations.csv?_stream=on", dtype={
    "fips": int,
    "cases": int,
    "deaths": int,
    "population": int,
    "deaths_per_million": int,
    "cases_per_million": int,
})

Observable

Import d3 by running this in a cell:

d3 = require("d3@5")

Now import the data into a variable called rows like this:

rows = d3.json("https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations.json?_shape=array")

You can export all 3,131 rows using a single streaming CSV export like this:

rows = d3.csv("https://covid-19.datasettes.com/covid/latest_ny_times_counties_with_populations.csv?_stream=on")

This could lose type information, since every column in a CSV import will be treated as text.