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.
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