Editing and running Notebooks in Workspaces¶

Set the execution context¶

Notebooks in Workspaces do not automatically set a database or schema. To query data, you must define the execution context in a cell using the following SQL commands:

USE DATABASE <database>;
USE SCHEMA <schema>;

To ensure notebooks run consistently across environments and clients, use fully qualified names for tables and other objects. For example:

-- Query data objects using a fully qualified name
SELECT * FROM TABLE <database_name.schema_name.table_name>;

-- Create a table using a fully qualified name
WITH filtered_events AS (
    SELECT
        user_id,
        event_type,
        event_timestamp
    FROM raw_events
    WHERE event_timestamp >= '2025-01-01'
)
CREATE OR REPLACE TABLE <database_name.schema_name.table_name> AS
SELECT *
FROM filtered_events;

Use the role and warehouse picker¶

You can set the active role and warehouse for your notebook.

SnowsightSQL

Use the picker at the top left of the Notebooks editor:

The query warehouse is used to run SQL queries and Snowpark pushdown compute invoked by the notebook. It is also used to render the interactive datagrid, but there is no credit charge for this operation.

To learn more about credit usage, see Setting up compute.

Create a Snowpark session¶

Snowpark is a Snowflake developer framework that lets you build data pipelines, transformations, and machine learning logic directly inside Snowflake without moving data out of the platform. It provides APIs that operate on Snowflake data as DataFrames, pushing computation down to Snowflake’s engine for scalability, performance, and security.

To use Snowpark Python APIs in Notebooks, first create a Snowpark session in a Python cell:

from snowflake.snowpark.context import get_active_session
session = get_active_session()

Run cells¶

There are four supported execution options:

• Run all cells

• Run one single cell

• Run current cell and all above cells (via the cell’s ellipsis menu)

• Run current cell and all below cells (via the cell’s ellipsis menu)

Cell names¶

You can assign names to cells to make navigation easier and provide contextual labels.

If an imported .ipynb file already contains name or title metadata, those values are used automatically.

Cell referencing¶

Bidirectional SQL to Python cell referencing allows you to reuse results and variables across cells in either language, enabling seamless transitions between SQL and Python workflows.

You can hover over the result tooltip to see the DataFrame name you can use to reference the result in Python and SQL.

SELECT * FROM {{uploaded_df}} WHERE "price" > 326;

import pandas as pd

uploaded_df = pd.read_csv("../data/diamonds.csv")
uploaded_df

SELECT * FROM {{uploaded_df}} WHERE "price" > 326;

SELECT * FROM {{dataframe_1}} WHERE "carat" < 1.0
UNION ALL
SELECT * FROM {{dataframe_2}} WHERE "carat" >= 1.0;

Interactive datagrid¶

The datagrid supports:

• Scrolling

• Search

• Filtering

• Sorting

• Chart creation without code

Minimap and cell status¶

The minimap generates a table of contents from Markdown headers and displays a comprehensive in-session status for each cell (running, succeeded, failed, and modified).

Global search and replace¶

You can search for keywords across all cells in the current notebook. If you’re editing a particular cell, press esc to exit the edit mode for that cell first.

To search keywords across all cells in the current notebook, do the following:

• To search for keywords, select Search in the minimap, or use the keyboard shortcut CTRL + F.

  Matching keywords in all cells are shown. Optionally, you can replace the search term with the desired value using Replace next or Replace all.

Notebook kernel¶

The notebook kernel remains active as long as the notebook service is in the RUNNING state, allowing uninterrupted execution of critical, long-running processes such as ML training and data engineering jobs.

Actions that do not affect kernel execution:

• Navigating to other pages

• Working elsewhere in Snowsight

• Closing your browser

• Shutting down your computer

You can shut down or restart the kernel using the Connected dropdown.

If the notebook service is suspended, the notebook kernel is also shut down. For more information, see Setting up compute.

Cell output¶

• Cell outputs are not saved to the .ipynb file and cannot be exported.

• Cell output in a notebook in Workspaces (both private and shared workspaces) is accessible to the user who executed the notebook.

• Cell output in a notebook project object (NPO) is accessible to the OWNER role of the notebook project object. Learn more about Notebook Project Objects (NPOs).

Jupyter magics¶

Notebooks in Workspaces run the IPython (Interactive Python) kernel and provide standard Jupyter cell and line magics. Run %lsmagic to view available magics.

For example, you can use the %run magic command to invoke another notebook:

• In a Python cell of notebook_a, call %run path/to/notebook_b.ipynb. This executes notebook_b in the same Python process as notebook_a.

• For variables and pandas DataFrames in notebook_b to render in notebook_a cell results, make sure to explicitly print them. For example: print(var) or display(df).

Developer tools¶

Developer tools include the Terminal, the Scratchpad, and the Variables Explorer. These tools allow you to explore and interact with your data and the notebook environment.

To access the developer tools, in the control bar at the top of the notebook, select <icon>:ui:Tools.

You must be connected to a notebook service to use the developer tools. Switching to a different service will restart the tools.

# If installation fails, run `apt update` first
# Install `htop`
apt install htop

# Run `htop`
htop