#MULTILINE STRING PYTHON JUPYTER NOTEBOOK CODE#

#MULTILINE STRING PYTHON JUPYTER NOTEBOOK CODE#

Once your visualisation is ready as a standalone, it’s straightforward to copy your code across, just editing a few minor things for the Jupyter environment. Debugging JavaScript in a Jupyter notebook is not great (there’s lots of stuff already going on in the DOM structure and error messaging, code formatting is nicer in a proper code editor, and I also found Jupyter temperamental for forcing a hard refresh when I was updating the code). You’ll likely want to create your visualisation code in a standalone HTML page first and do all your debugging there. Creating a D3 visualisation in a Jupyter cell This approach is also neat if you choose to keep your JavaScript in an external file that you load in, and can thus just reference the data file by filename there. (See the accompanying notebook for a fuller example of this). You can run JavaScript in a Python Jupyter cell simply by writing JavaScript in a Python Jupyter notebook cell

The code is on Github and you can try the notebook live on Binder. There’s an accompanying Jupyter notebook to this blog post which demonstrates the steps below. What I’ve put below repeats some of Stefaan’s post, adds some reflections, and notes where we did things a little differently or for different reasons. I found this blog post by Stefaan Lippens the most helpful for figuring out what to do and I’d recommend giving it a read. We, therefore, looked into extending the design in D3. Creating it in JavaScript meant we could add selection boxes as part of the visualisation. to use the visualisation as a selection interface.more control and customisation in the visual design to communicate the complexity of our data, including interactive tooltips (additional text that appears when elements are hovered over).We initially created a static Matplotlib plot, but found we wanted: You can read more about this tool, Press Picker, in this post. After filtering our dataset, we created a visualisation overviewing the remaining data to support selecting titles for digitisation. The Times or The Blackpool Herald) for digitisation using a range of criteria.

We were using a Jupyter notebook to narrow down a large dataset of historical newspaper titles held by the British Library (here a newspaper title means eg. Before I go on to how, I’ll explain why we ended up looking into this in the first place. Can you put a D3 JavaScript visualisation into a Python Jupyter notebook? Yes! (Though there are some hacky bits). But you may find you need more customisation in your visualisation design than these libraries support.ĭ3 is a low-level JavaScript visualisation library: you’ll write more code, but it has an ‘ expressivity advantage’. the classic Matplotlib and, with chart interactivity nicely built in with Jupyter, Altair, Bokeh, and Plotly. There are a number of Python libraries for doing visualisation eg. In this post, I explain why we needed to use the JavaScript visualisation library D3 in a Python Notebook, and share the steps I took to get it working for our specific goals.

In Living with Machines we’ve found ourselves using Jupyter notebooks widely, largely in Python. Tim Sherratt’s GLAM Workbench, for example, uses Jupyter notebooks to interweave data processing and analysis with visualisations and commentary. They’re becoming increasingly popular in cultural heritage data/digital humanities work. Jupyter notebooks are a great environment for bringing together code with the outputs of processing, including visualisations.

Multiline string python jupyter notebook