Carol is currently a Research Software Engineer at Cal Poly San Luis Obispo working full-time on Project Jupyter. She is also a a Python Software Foundation Fellow and former Director; a Project Jupyter Steering Council member; a core developer on CPython, Jupyter, AnitaB.org’s open source projects, and PyLadies; and a co-organizer of PyLadies San Diego and San Diego Python User Group.
By pairing JupyterLab and JupyterHub, you can improve your users' data science workflow. In this talk, you will learn the new functionality that JupyterLab offers you to customize your work environment. By adding JupyterHub, you can serve JupyterLab to large groups of users while streamlining installation and dependency management.
JupyterLab, the next generation of Jupyter Notebook, provides a high level of integration between notebooks, documents, and activities. It adds new functionality, including:
- Drag-and-drop cells and between notebooks.
- Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).
- Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.
- Edit popular file formats with live preview, such as Markdown, JSON, CSV, Vega, VegaLite, and more.
JupyterHub serves groups of users, whether data scientists, scientists, or students. Combining JupyterLab with JupyterHub improves the data science workflow and helps you increase productivity.