Databricks for Python developers

This section provides a guide to developing notebooks and jobs in Databricks using the Python language.

Python APIs

PySpark API

PySpark is the Python API for Apache Spark. These links provide an introduction to and reference for PySpark.

pandas API (Koalas)

Koalas is an open source project that provides a drop-in replacement for pandas. pandas is a Python package commonly used by data scientists. However, pandas does not scale out to big data. Koalas fills this gap by providing pandas equivalent APIs that work on Apache Spark.

Koalas

Visualizations

Databricks Python notebooks support various types of visualizations using the display function.

Visualizations in Python

You can also use the following third-party libraries to create visualizations in Databricks Python notebooks.

Interoperability

These articles describe features that support interoperability between PySpark and pandas.

This article describes features that support interoperability between Python and SQL.

User-defined functions

Notebooks

For information about working with Python in Databricks notebooks, see Use notebooks. For instance:

You can override a notebook’s default language by specifying the language magic command %<language> at the beginning of a cell. For example, you can run Python code in a cell within a notebook that has a default language of R, Scala, or SQL. For Python, the language magic command is %python.
In Databricks Runtime 7.4 and above, you can display Python docstring hints by pressing Shift+Tab after entering a completable Python object.
Python notebooks support error highlighting. The line of code that throws the error is highlighted in the cell.

Tools

In addition to Databricks notebooks, you can use the following Python developer tools:

For information about additional tools for working with Databricks, see Developer tools.

Libraries

Databricks runtimes include many popular libraries. You can also install additional third-party or custom Python libraries to use with notebooks and jobs running on Databricks clusters.

Cluster-based libraries

Cluster-based libraries are available to all notebooks and jobs running on the cluster.

For information about viewing the list of installed libraries, see View the libraries installed on a cluster.
For information about installing cluster-based libraries, see Install a library on a cluster.
See also the Libraries API, Libraries CLI, and Library utilities.

Notebook-scoped libraries

Notebook-scoped libraries are available only to the notebook on which they are installed and must be reinstalled for each session.

For an overview of different options you can use to install Python libraries within Databricks, see Python environment management.
For information about notebook-scoped libraries in Databricks Runtime 6.4 ML and above and Databricks Runtime 7.1 and above, see Notebook-scoped Python libraries.
For information about notebook-scoped libraries in Databricks Runtime 7.0 and below, see Library utilities.

Machine learning

For general information about machine learning on Databricks, see Machine learning and deep learning guide.

To get started with machine learning using the scikit-learn library, use the following notebook. It covers data loading and preparation; model training, tuning, and inference; and model deployment and management with MLflow.

10-minute tutorial: machine learning on Databricks with scikit-learn

To get started with GraphFrames, a package for Apache Spark that provides DataFrame-based graphs, use the following notebook. It covers creating GraphFrames from vertex and edge DataFrames, peforming simple and complex graph queries, building subgraphs, and using standard graph algorithms such as breadth-first search and shortest paths.

GraphFrames Python notebook

Jobs

You can run a Python script by calling the create job API, specifying the spark_python_task field in the request body.