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Here’s a short video guide on how to connect Jupyter to Cube.

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Connect from Cube Cloud

Navigate to the Integrations page, click Connect to Cube, and choose Jupyter to get detailed instructions.

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Connect from Cube Core

You can connect a Cube deployment to Jupyter using the SQL API. In Cube Core, the SQL API is disabled by default. Enable it and configure the credentials to connect to Jupyter.

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Connecting from Jupyter

Jupyter connects to Cube as to a Postgres database.

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Creating a connection

Make sure to install the sqlalchemy and pandas modules. 
pip install sqlalchemy
pip install pandas
Then you can use sqlalchemy.create_engine to connect to Cube’s SQL API. 
import sqlalchemy
import pandas

engine = sqlalchemy.create_engine(
    sqlalchemy.engine.url.URL(
        drivername="postgresql",
        username="cube",
        password="9943f670fd019692f58d66b64e375213",
        host="thirsty-raccoon.sql.aws-eu-central-1.cubecloudapp.dev",
        port="5432",
        database="db@thirsty-raccoon",
    ),
    echo_pool=True,
)
print("connecting with engine " + str(engine))
connection = engine.connect()

# ...

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Querying data

Your cubes will be exposed as tables, where both your measures and dimensions are columns. You can write SQL in Jupyter that will be executed in Cube. Learn more about Cube SQL syntax on the reference page. 
# ...

query = "SELECT SUM(count), status FROM orders GROUP BY status;"
df = pandas.read_sql_query(query, connection)
In your Jupyter notebook it’ll look like this.
You can also create a visualization of the executed SQL query.