Working with Jupyter Notebooks in Positron: From Exploration to Production
Jupyter notebooks are the standard for data exploration, but the traditional notebook experience often creates friction. You likely spend too much time printing variables just to inspect your current state, wrestling with coding assistants that lack the context of your memory, and hitting a wall when it is time to move your work out of the notebook and into production.
In this session led by Cindy Tong & Wasim Lorgat at Posit, you will learn how to: 00:46 - Open existing.ipynb files (See how Positron handles your current notebooks with no conversion required) 06:40 - Interact with data visually (Move beyond df.head() by using the Data explorer to interrogate multi-column datasets visually) 07:24 - Automate with AI (See how Positron Assistant reads your active memory and dataframes to generate accurate code, rather than generic text suggestions) 15:35 - Deliver to stakeholders (Learn the workflow for turning your exploration into an application published on Posit Connect)
Explore the Demo Assets:
Notebooks Blog Post: https://posit.co/blog/announcing-the-positron-notebook-editor-for-jupyter-notebooks/ Demo Notebook Repository: https://posit.co/blog/announcing-the-positron-notebook-editor-for-jupyter-notebooks/ Explore Positron: https://positron.posit.co/ Positron Notebook Editor: https://positron.posit.co/positron-notebook-editor.html
Positron
