Notebook outputs and results

After you attach a notebook to a cluster and run one or more cells, your notebook has state and displays outputs. This section describes how to manage notebook state and outputs.

Clear notebooks state and outputs

To clear the notebook state and outputs, select one of the Clear options at the bottom of the Run menu.

Menu option	Description
Clear all cell outputs	Clears the cell outputs. This is useful if you share the notebook and want to avoid including any results.
Clear state	Clears the notebook state, including function and variable definitions, data, and imported libraries.
Clear state and outputs	Clears cell outputs and the notebook state.
Clear state and run all	Clears the notebook state and starts a new run.

Results table

When a cell is run, results are shown in a results table. With the results table, you can do the following:

• Copy a column or other subset of tabular results data to the clipboard.
• Do a text search over the results table.
• Sort and filter data.
• Navigate between table cells using the keyboard arrow keys.
• Select part of a column name or cell value by double-clicking and dragging to select the desired text.

To view limits on the results table, see Notebook results table limits.

Select data

To select data in the results table, do any of the following.

• Click a column or row header.
• Click in the upper-left cell of the table to select the entire table.
• Drag your cursor across any set of cells to select them.

To open a side panel displaying selection information, click the panel icon icon in the upper-right corner, next to the Search box.

Copy data to clipboard

To copy the selected data to the clipboard, press Cmd + c on MacOS or Ctrl + c on Windows, or right-click and select Copy from the drop-down menu.

Sort results

To sort the results table by the values in a column, hover your cursor over the column name. An icon containing the column name appears at the right of the cell. Click the arrow to sort the column.

To sort by multiple columns, hold down the Shift key as you click the sort arrow for the columns.

Filter results

To create a filter, click at the upper-right of the cell results. In the dialog that appears, select the column to filter on and the filter rule and value to apply. For example:

To add another filter, click .

To temporarily enable or disable a filter, toggle the Enabled/Disabled button in the dialog. To delete a filter, click the X next to the filter name .

To filter by a specific value, right-click on a cell with that value and select Filter by this value from the drop-down menu.

You can also create a filter from the kebab menu in the column name:

Filters are applied only to the results shown in the results table. If the data returned is truncated (for example, when a query returns more than 64,000 rows), the filter is applied only to the returned rows.

Column headers indicate the data type of the column. For example, indicates integer data type. Hover over the indicator to see the data type.

Format columns

You can format columns in results tables as types like Currency, Percentage, URL and more, with control over decimal places for clearer tables.

Format columns from the kebab menu in the column name.

Download results

By default, downloading results is enabled. To toggle this setting, see Manage the ability to download results from notebooks.

You can download a cell result that contains tabular output to your local machine. Click the downward pointing arrow next to the tab title. The menu options depend on the number of rows in the result and the Databricks Runtime version. Downloaded results are saved on your local machine as a CSV file named export.csv.

Explore SQL cell results in Python notebooks natively using Python

You can load data using SQL and explore it using Python. In a Databricks Python notebook, table results from a SQL language cell are automatically available as a Python DataFrame. For details, see Explore SQL cell results in Python notebooks.

View multiple outputs per cell

Python notebooks and %python cells in non-Python notebooks support multiple outputs per cell. For example, the output of the following code includes both the plot and the table:

import pandas as pd
from sklearn.datasets import load_iris

data = load_iris()
iris = pd.DataFrame(data=data.data, columns=data.feature_names)
ax = iris.plot()
print("plot")
display(ax)
print("data")
display(iris)

Commit notebook outputs in Databricks Git folders

To learn about committing .ipynb notebook outputs, see Allow committing .ipynb notebook output.

• The notebook must be a .ipynb file
• Workspace admin settings must allow notebook outputs to be committed.