JupyterLab Now Available

Project Jupyter announces the general availability of JupyterLab:

JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area.

JupyterLab provides a high level of integration between notebooks, documents, and activities:

  • Drag-and-drop to reorder notebook cells and copy them 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.

I like this, as I’m a big fan of notebooks but sometimes you just want to write some diagnostic queries and an IDE is way better for that. H/T Giovanni Lanzani

Related Posts


John Mount explains the vtreat package that he and Nina Zumel have put together: When attempting predictive modeling with real-world data you quicklyrun into difficulties beyond what is typically emphasized in machine learning coursework: Missing, invalid, or out of range values. Categorical variables with large sets of possible levels. Novel categorical levels discovered during test, cross-validation, or […]

Read More

Wrapping Up A Data Science Project

I have finished my series on launching a data science project.  First, I have a post on deploying models as microservices: The other big shift is a shift away from single, large services which try to solve all of the problems.  Instead, we’ve entered the era of the microservice:  a small service dedicated to providing […]

Read More


February 2018
« Jan Mar »