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These two software packages, created at the Interactive Data Lab at the University of Washington, specify what is called a data visualization grammar. Altair is also part of a larger eco-system of libraries, and based on two lower-level libraries called Vega and Vega-Lite. In this version of the walkthrough, we’re going to use a library called Altair. Many of those communities of developers are friendly and very interested in your feedback! Altair, Vega-Lite and Vega When there’s a feature missing you really need, contact them (also via an issue or mailing list or whatever means of communication they use). It’s also fair to say that most of them are open-source projects, that is, they thrive around a community of volunteers that help improve them. If you’re willing to take that risk, however, you can do pretty amazing things. So using any of these packages carries a bit of a risk: they might not be super well documented, or they might be missing features, or their interface might change over the course of a year or so. DataShader), others don’t handle large data sets well at the moment.
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Some are focused specifically on dealing with very large data sets (e.g. One important note is that many of the packages involved are still fairly young, and so the library and the syntax might change quite frequently. For an overview of the different options in Python, PyViz is a great resource to explore!
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Because D3.js is pretty labour-intensive (and requires you to know some JavaScript), groups have started developing alternatives and extensions, some based on D3, some not, to make interactive visualization design more accessible to non-experts. In recent years, however, there have been new developments both in terms of computation and in the data visualization world, and alternatives have emerged.Īs we’ve mentioned previously, d3.js is a very powerful library to develop interactive visualizations using JavaScript. Matplotlib has been enormously successful at making Python viable as a standard language for scientific computing and data analysis. Instead of matplotlib and seaborn, we are This notebook is a follow-up to the visualization walkthrough. In particular, Altair has a small library of aggregators that can be really helpful for data exploration.Apply the visualization principles learned during the first half to a practical problem. But with that said, in data exploration it’s often really nice to have some quick convenience transformations, and Altair does not disappoint. Aggregators ¶Īs we noted in the last reading, it will often be the case that it’s easier to just do any transformations of your data before passing the data to Altair (in part because of the Altair data size issues noted above). OK, enough of the nuts and bolts of Altair! Let’s dive into the little quirks and features of Altair that will make your life easier. Just put the URL you want to use in Chart() when you make your chart! Altair Quirks & Convenience Features ¶ So if your data is too big to put into the Vega-Lite spec for some reason, you can also host it elsewhere and provide a URL to the data. Is actually represented by this JSON file: Thankfully, most of that isn’t your problem, but it is helpful to know that when you create an Altair chart, what you’re actually generating is a JSON-formatted Vega-Lite specification for your chart. Altair itself is actually just a Python wrapper for a visualization library called Vega-Lite, which is itself a simplified interface for Vega, which in turn is built on top of D3, a low level JavaScript visualization library. This may sound like a bit of an odd question to even ask – it’s it just an image?! – but in the case of Altair, it turns out the answer is a little more complicated than you might think.Īltair actually sits on top of a rather large stack of software libraries.
#Altair mark text how to#
In this reading, we’ll learn more about how Altair works, some of its quirks and hidden features (and how they can make your life easier), and how to generate and share interactive graphics. In our last reading, we introduced Altair, and explored how to make basic charts, layer and facet them, and more. If you want to make a chart from a large dataset….Plotting, Advanced Plotting, Advanced Contents.