Cloud Datalab is Google’s Jupyter fork, which is provided as a part of Google Cloud.

Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base.

While that is a fun product to play with, I had a few hiccups.


Cloud Datalab includes a set of well-known Python libraries. One of them is ggplot, which is R’s ggplot2 alternative.

However that’s not the only alternative you can use. I think plotnine is much better and I’m not the only one.

Luckily, you can install plotnine on Datalab easily by running

! pip install plotnine

from Jupyter.


Cloud Datalab doesn’t include Bokeh. Long story short, Bokeh’s latest version’s Notebook integration doesn’t work on Datalab.

According to this GitHub issue,

As of 0.12.9 the minimum supportable notebook version is 5.0. There is no technical path that will allow Bokeh to support JupyterLab, classic Notebook 5+ and Classic Notebook 4.x and earlier at the same time, with identical code in each. Supporting JupyterLab is imperative for the project, so earlier classic notebook versions below 5.0 cannot be supported. You can downgrade Bokeh, or upgrade your notebook (or use JupyterLab).

And Datalab is based on Jupyter 4.2.3 apparently (I checked Jupyter.version from my browser’s JavaScript console).

You can downgrade Bokeh, or install datalab package on your latest, local Jupyter notebook. Hope Google updates Datalab’s Jupyter soon.