Introducing Data Profiles in the Databricks Notebook
Before a data scientist can write a report on analytics or train a machine learning (ML) model, they need to understand the shape and content of their data. This exploratory data analysis is iterative, with each stage of the cycle often involving the same basic techniques: visualizing data distributions and computing summary statistics like row...
Databricks Notebook Dark Theme
This became the most-requested feature in Databricks’ history, and now it’s here: a dark theme for the Databricks notebook! We’re excited for you to try it out. To turn it on, open a notebook and navigate to the View menu > Notebook Theme > Dark Theme. Enjoy!
Python Autocomplete Improvements for Databricks Notebooks
At Databricks, we strive to provide a world-class development experience for data scientists and engineers, and new features are constantly getting added to our notebooks to improve our users’ productivity. We are especially excited about the latest of these features, a new autocomplete experience for Python notebooks (powered by the Jedi library ) and new...
