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Showing content from https://docs.databricks.com/aws/en/notebooks/notebooks-code below:

Develop code in Databricks notebooks

This page describes how to develop code in Databricks notebooks, including autocomplete, automatic formatting for Python and SQL, combining Python and SQL in a notebook, and tracking the notebook version history.

For more details about advanced functionality available with the editor, such as autocomplete, variable selection, multi-cursor support, and side-by-side diffs, see Navigate the Databricks notebook and file editor.

When you use the notebook or the file editor, Databricks Assistant is available to help you generate, explain, and debug code. See Use Databricks Assistant for more information.

Databricks notebooks also include a built-in interactive debugger for Python notebooks. See Debug notebooks.

With Databricks Runtime 11.3 LTS and above, you can create and manage source code files in the Databricks workspace, and then import these files into your notebooks as needed.

For more information on working with source code files, see Share code between Databricks notebooks and Work with Python and R modules.

Databricks provides tools that allow you to format Python and SQL code in notebook cells quickly and easily. These tools reduce the effort to keep your code formatted and help to enforce the same coding standards across your notebooks.

Databricks supports Python code formatting using black within the notebook. The notebook must be attached to a cluster with black and tokenize-rt Python packages installed.

On Databricks Runtime 11.3 LTS and above, Databricks preinstalls black and tokenize-rt. You can use the formatter directly without needing to install these libraries.

On Databricks Runtime 10.4 LTS and below, you must install black==22.3.0 and tokenize-rt==4.2.1 from PyPI on your notebook or cluster to use the Python formatter. You can run the following command in your notebook:

%pip install black==22.3.0 tokenize-rt==4.2.1

or install the library on your cluster.

For more details about installing libraries, see Python environment management.

For files and notebooks in Databricks Git folders, you can configure the Python formatter based on the pyproject.toml file. To use this feature, create a pyproject.toml file in the Git folder root directory and configure it according to the Black configuration format. Edit the [tool.black] section in the file. The configuration is applied when you format any file and notebook in that Git folder.

You must have CAN EDIT permission on the notebook to format code.

Databricks uses the Gethue/sql-formatter library to format SQL and the black code formatter for Python.

You can trigger the formatter in the following ways:

• Format a single cell

  • Keyboard shortcut: Press Cmd+Shift+F.
  • Command context menu:
    • Format SQL cell: Select Format SQL in the command context dropdown menu of a SQL cell. This menu item is visible only in SQL notebook cells or those with a %sql language magic.
    • Format Python cell: Select Format Python in the command context dropdown menu of a Python cell. This menu item is visible only in Python notebook cells or those with a %python language magic.
  • Notebook Edit menu: Select a Python or SQL cell, and then select Edit > Format Cell(s).

• Format multiple cells

  Select multiple cells and then select Edit > Format Cell(s). If you select cells of more than one language, only SQL and Python cells are formatted. This includes those that use %sql and %python.

• Format all Python and SQL cells in the notebook

  Select Edit > Format Notebook. If your notebook contains more than one language, only SQL and Python cells are formatted. This includes those that use %sql and %python.

To customize how your SQL queries are formatted, see Custom format SQL statements.

• Black enforces PEP 8 standards for 4-space indentation. Indentation is not configurable.
• Formatting embedded Python strings inside a SQL UDF is not supported. Similarly, formatting SQL strings inside a Python UDF is not supported.

The default language for the notebook appears below the notebook name.

To change the default language, click the language button and select the new language from the dropdown menu. To ensure that existing commands continue to work, commands of the previous default language are automatically prefixed with a language magic command.

By default, cells use the default language of the notebook. You can override the default language in a cell by clicking the language button and selecting a language from the dropdown menu.

Alternately, you can use the language magic command %<language> at the beginning of a cell. The supported magic commands are: %python, %r, %scala, and %sql.

note

When you invoke a language magic command, the command is dispatched to the REPL in the execution context for the notebook. Variables defined in one language (and hence in the REPL for that language) are not available in the REPL of another language. REPLs can share state only through external resources such as files in DBFS or objects in object storage.

Notebooks also support a few auxiliary magic commands:

• %sh: Allows you to run shell code in your notebook. To fail the cell if the shell command has a non-zero exit status, add the -e option. This command runs only on the Apache Spark driver, and not the workers. To run a shell command on all nodes, use an init script.
• %fs: Allows you to use dbutils filesystem commands. For example, to run the dbutils.fs.ls command to list files, you can specify %fs ls instead. For more information, see Work with files on Databricks.
• %md: Allows you to include various types of documentation, including text, images, and mathematical formulas and equations. See the next section.

Syntax highlighting and SQL autocomplete are available when you use SQL inside a Python command, such as in a spark.sql command.

In a Databricks notebook, results from a SQL language cell are automatically made available as an implicit DataFrame assigned to the variable _sqldf. You can then use this variable in any Python and SQL cells you run afterward, regardless of their position in the notebook.

note

This feature has the following limitations:

• The _sqldf variable is not available in notebooks that use a SQL warehouse for compute.
• Using _sqldf in subsequent Python cells is supported in Databricks Runtime 13.3 and above.
• Using _sqldf in subsequent SQL cells is only supported on Databricks Runtime 14.3 and above.
• If the query uses the keywords CACHE TABLE or UNCACHE TABLE, the _sqldf variable is not available.

The screenshot below shows how _sqldf can be used in subsequent Python and SQL cells:

important

The variable _sqldf is reassigned each time a SQL cell is run. To avoid losing reference to a specific DataFrame result, assign it to a new variable name before you run the next SQL cell:

• Python
• SQL

Python

new_dataframe_name = _sqldf

SQL

ALTER VIEW _sqldf RENAME TO new_dataframe_name

While a command is running and your notebook is attached to an interactive cluster, you can run a SQL cell simultaneously with the current command. The SQL cell is executed in a new, parallel session.

To execute a cell in parallel:

1. Run the cell.

2. Click Run now. The cell is immediately executed.

Because the cell is run in a new session, temporary views, UDFs, and the implicit Python DataFrame (_sqldf) are not supported for cells that are executed in parallel. In addition, the default catalog and database names are used during parallel execution. If your code refers to a table in a different catalog or database, you must specify the table name using three-level namespace (catalog.schema.table).

You can run SQL commands in a Databricks notebook on a SQL warehouse, a type of compute that is optimized for SQL analytics. See Use a notebook with a SQL warehouse.

You can run code in the Assistant on Databricks on serverless compute, which is the default compute on Databricks. For pages where there is already a compute selected (for example, notebooks or SQL editor), Databricks defaults to using it. For information about compute types, see Types of compute.

If you don't have access to serverless compute, you must have a compute instance available to run code in the Assistant panel.

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