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 are sharing the notebook and do not want to include any results.

Clear state

Clears the notebook state, including function and variable definitions, data, and imported libraries.

Clear state and outputs

Clears both cell outputs and the notebook state.

Clear state and run all

Clears the notebook state and starts a new run.

Show results

When a cell is run, table results return a maximum of 10,000 rows or 2 MB, whichever is less.

By default, text results return a maximum of 50,000 characters. With Databricks Runtime 12.1 and above, you can increase this limit by setting the Spark configuration property spark.databricks.driver.maxReplOutputLength.

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 made available as a Python DataFrame. For details, see Explore SQL cell results in Python notebooks.

New cell result table

Preview

This feature is in Public Preview.

You can now select a new cell result table rendering. With the new result table, you can do the following:

  • Copy a column or other subset of tabular results to the clipboard.

  • Do a text search over the results table.

  • 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 enable the new result table, click New result table in the upper-right corner of the cell results, and change the toggle selector from OFF to ON.

new result display selector

When the feature is on, you can click column or row headers to select entire columns or rows, and you can click in the upper-left cell of the table to select the entire table. You can drag your cursor across any rectangular set of cells to select them.

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.

To search for text in the results table, enter the text in the Search box. Matching cells are highlighted.

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

location of panel icon

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

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 on the Databricks Runtime version. Downloaded results are saved on your local machine as a CSV file named export.csv.

Download cell results

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 Repos

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

  • The notebook must be an .ipynb file

  • Workspace admin settings must allow notebook outputs to be committed.