Announcing Python Notebook in Preview
The highly anticipated Python Notebook is now in preview! This new feature is designed to enhance the experience of BI Developers and Data Scientists working with smaller datasets using Python as their primary language.
Key Features
- Native Python Support: Enjoy the full power of Python with native features and libraries right out of the box, like ipywidget, magic commands.
- Version Flexibility: Easily switch between different Python versions (initially supporting Python 3.11 and 3.10).
- Optimized Resource Utilization: Benefit from better resource utilization with a smaller 2vCore/16G memory compute, real-time resource utilization monitor is available.
- Lakehouse & Resources natively available: Leveraging the Fabric Lakehouse capabilities seamlessly, with built-in Resource folder to store your modules, libs, and files.
- Mix programming with T-SQL: Data Warehouses natively supported, using notebookutils to easily read/write Data Warehouse with T-SQL statements.
- Superior Python intellisense: Powerful Pylance are natively integrated to provide smoother coding experience.
- Popular libraries are pre-installed: Using duckdb, polars and other popular 3rd party libraries on Python notebook conveniently. The Fabric utilities like Semantic Link (SemPy) and NotebookUtils are also natively supported.
- Custom Session configuration: Live pool experience with 5s spin up a session by default enabled, you can also use %%configure to customize your own session with specific hardware settings, mountpoints and default lakehouse.
- Seamless Integration with Fabric eco-system: All the advantages of Fabric notebook like sharing, CI/CD, schedule run, data pipeline integration, OrgAPP integration, are available for Python experience.
Getting Started
Access the Notebook: You can access the Python Notebook from the Notebook language dropdown menu.
Comprehensive Guide: A detailed guide is available to help you get started. Please refer to the public document to find more details.
The preview of the Python experience in notebooks is a significant milestone. Watch the 5-minute video to learn the end-to-end machine learning workflow using Python notebooks.
Your feedback is crucial in shaping the future of our product. We look forward to your active participation and valuable insights, and greatly appreciate your support!
