Databricks is a platform that provides various tools and services for working with big data and machine learning using Python. Here’s how Python is used in Databricks and some resources to get started:

Ways to use Python in Databricks:

1. Databricks Notebooks: You can write and execute Python code in interactive notebooks, similar to Jupyter Notebooks. These notebooks allow you to easily mix code, visualizations, and text for data analysis and exploration.
2. PySpark: Databricks provides PySpark, a Python API for Apache Spark, a popular distributed computing framework. PySpark lets you process and analyze large scalable datasets across a cluster of machines.
3. Pandas API on Spark: If you’re familiar with the Pandas library for data manipulation and analysis, Databricks offers a Pandas API on Spark that allows you to use Pandas-like syntax with the power of Spark’s distributed processing capabilities.
4. Machine Learning Libraries: Databricks integrates with popular Python machine learning libraries, such as sci-kit, TensorFlow, and PyTorch. This makes it easier to develop and deploy machine learning models on Databricks.
5. Databricks SDK for Python: Databricks provides an SDK that lets you programmatically manage and interact with Databricks resources (clusters, jobs, notebooks, etc.) using Python code.