When it comes to data visualization there are many possible tools Matplotlib, Plotly, Bokeh… Which one is fitting my short term goals, within a notebook, and is a good choice for longer-term, in production? What does production mean?

Now that you have a nice machine learning model, or you have completed some data mining or analysis, you need to present and promote this amazing work. You may initially reuse some notebooks to produce a few charts… but soon colleagues or clients are requesting access to the data or are asking for other views or parameters. What should you do? Which tools and libraries should you use? Is there a one fits all solution for all stages of my work?

Data-visualization has a very wide scope, ranging from presenting data with simple charts to be included in a report, to complex interactive dashboards. The first is reachable to anybody that knows about Excel whereas the later is more a software product that may require the full software development cycle and methodology.

In between these two extreme cases, data scientists face many choices that are not trivial. This post is providing some questions that will come along this process, and some tips and answers to these. The chosen starting point is Python within a Jupiter notebook, the target is a Web dashboard in production.

#plotly #bokeh #data-visualization #matplotlib #data-science #big data