JupyterHub is the best way to serve Jupyter notebook for multiple users. A notebook is useful to share interactive algorithms with your audience by focusing on teaching or demonstrating a technique. The Jupyter project’s JupyterHub is the most widely used multi-user server for Jupyter. But for someone like me, who is comfortable with the python interface to bokeh and wants to learn more about bokehjs — especially considering that, while the python API is extensively and meticulously documented, the bokehjs API is basically a black box — Observable offers a fun opportunity. It works a bit differently from Jupyter, as there are no real magic commands; To work with other languages, you need to add separate Bash, Stan, Python, SQL or Rcpp chunks to the notebook. The root jupyter command.. There is also a next version - Jupyter Lab[1] which looks fantastic! Pros: * Fast prototyping * Visual results * Shareable insights Cons: * Collaboration is tricky * Versioning and code reviews are hard * Prone to producing complexity That being said, a lot of effort is being put in order to reduce the cons. For example, the year by year counts are stored in the variable data which is declared directly: Notice that the braces used in javascript explicit object creation need parentheses to help the observable parser out. In the world of computer programming, notebooks typically … I am trying to convince the Jupyter community to pay attention to their design. JupyterLab on JupyterHub¶. Jupyter has been a good exemplar of this conundrum. You can look directly at the observable notebook where I draw this plot. I was using an extension in Jupyter but it was behaving unpredictably, and not being a frontend engineer by trade I found the process of sublime + browser foreign and cumbersome. Interactive. The Jupyter Blog. Published on May 26, 2018. I do all my data wrangling with Pandas, generate a JSON/CSV file, use scp to upload it to my server and then access it in Observable. It also takes an object with properties.If we’re adding an element it’s a content object, and if we’re styling an element it’s a style object (usual CSS styles). The crucial require statements in this code act via side effects, rather than by returning a value. What happens next is that they dump a whole jupiter script into a class method and call it a day. I’m not sure if this is a common use case, but it might be useful sometimes. The Jupyter Notebook is a web-based interactive computing platform. JupyterHub¶. As I experimented with adding more interactivity to my plots, it gradually became clear to me that knowing some javascript — which I didn’t — and having a clearer understanding of bokehjs would let me do a lot more with Bokeh. Bokeh is powerful, easy to use, has accessible interactive features, and produces beautiful graphs. Morever, you can have Jupyter Notebook run on one machine (like a VM that you have provisioned in the cloud) and access the web page / do your editing from a different machine (like a Chromebook). Make learning your daily ritual. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. ObservableHQ is a platform being built by Mike Bostock (creator of the D3 visualisation library), Jeremy Ashkenas (“Made CoffeeScript, Backbone.js, Underscore and other ragbag” from his Twitter bio) and Tom MacWright (creator of the big presentation framework, simple-statistics and documentation.js as well as D3 contributor amongst other things). As a big fan of jupyter and zeppelin I am stoked to see notebooks entering the js data viz domain. But JupyterLab helps transcend the limitations, while retaining the innovation and convenience. 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