{% extends "base.html" %} {% block title %}Export {{ count }} rows to a notebook{% endblock %} {% block extra_head %} {% endblock %} {% block content %}

Export {{ count }} 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 {{ count }} row{% if count != 1 %}s{% endif %} into a DataFrame called df:

df = pandas.read_json(
    {{ json.dumps(json_url) }}
)

Run df in a new cell to see the table.

{% if allow_csv_stream and csv_stream_url and has_next %}

You can export all {% if total_count %}{{ "{:,}".format(total_count) }} {% endif %}rows using a single streaming CSV export like this:

{{ pandas_stream_code }}
{% endif %}

Observable

{% if cors_enabled %}

Import the data into a variable called rows like this:

rows = d3.json(
  {{ json.dumps(json_url) }}
)
{% if allow_csv_stream and csv_stream_url and has_next %}

You can export all {% if total_count %}{{ "{:,}".format(total_count) }} {% endif %}rows using a single streaming CSV export like this:

rows = d3.csv(
  {{ json.dumps(csv_stream_url) }},
  d3.autoType
)
{% endif %} {% else %}

Export to Observable is only available if Datasette is running with the --cors option.

{% endif %} {% endblock %}