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

Now paste the following into a cell to load the 100 rows into a DataFrame called df:

df = pandas.read_json(
    "https://covid-19-j7hipcg4aq-uc.a.run.app/la-times/cdph-press-releases.json?_shape=array"
)

Run df in a new cell to see the table.

You can export all 559 rows using a single streaming CSV export like this:

df = pandas.read_csv(
    "https://covid-19-j7hipcg4aq-uc.a.run.app/la-times/cdph-press-releases.csv?_stream=on", dtype={
    "rowid": int,
    "confirmed_cases": int,
    "deaths": int,
    "age_0_to_17": int,
    "age_18_to_49": int,
    "age_50_to_64": int,
    "age_65_and_up": int,
    "age_unknown": int,
    "gender_male": int,
    "gender_female": int,
    "gender_unknown": int,
    "total_tests": int,
    "confirmed_hospitalizations": int,
    "confirmed_icu": int,
    "suspected_hospitalizations": int,
    "suspected_icu": int,
    "healthcare_worker_infections": int,
    "healthcare_worker_deaths": int,
    "vaccine_doses_administered": int,
})

Observable

Import the data into a variable called rows like this:

rows = d3.json(
  "https://covid-19-j7hipcg4aq-uc.a.run.app/la-times/cdph-press-releases.json?_shape=array"
)

You can export all 559 rows using a single streaming CSV export like this:

rows = d3.csv(
  "https://covid-19-j7hipcg4aq-uc.a.run.app/la-times/cdph-press-releases.csv?_stream=on",
  d3.autoType
)