![]() If you want to know more about this project, you can refer to their FAQ. So I'm now using holoviz as a main entry point to visualization. The holoviz team is packaging the tools I need in the pyviz conda repo, and seems to share my views of how data visualization in python should evolve. In june 2019, the holoviz project was launched. Again, bokeh and holoviews are the way to go here. When I need to display big data, I use datashader, a library that compresses big data into an image dynamically before sending it to a bokeh plot. Holoviews, which can be used as a high-level interface to bokeh or matplotlib, makes it easy to create complex plots in just two lines of code, and is thus addressing point 1. But making a single plot in bokeh can require a dozen lines of code or more. The quantile method is usually applied for choropleth maps. To define the density classes, this document describes common methodologies. So, option 2 is certainly the most appropriate one. Displaying population makes the result too dependent on the subdivision. Many times, I use bokeh directly, like in Show your Data in a Google Map with Python or Interactive Visualization with Bokeh in a Jupyter Notebook. Usually, choropleth maps display densities and not populations. So that's exactly what I need to address point 2. ![]() I did spend at least a week on it already! I will probably prefer to hire an expert when I really need a D3-based display rather than investing time into learning it.īokeh, on the other hand, drives JavaScript from python code (without relying on D3). But D3 for example has quite a steep learning curve. I still believe that these libraries are the way to go for professional and large scale display in the browser. ![]() rules out pure JavaScript libraries such as D3.js. python: so JavaScript should remain mostly hidden from me.įormalizing these four points helped me a lot in choosing my tools.big data: display lots of information without killing the client browser.interactive plots in the browser: this basically calls for JavaScript under the hood.a concise syntax: I want to do plots without wasting time writing code, to get fast insight on my data.This might seem a bit complicated, and indeed it is!Ĭurrently, at the end of 2019, the landscape of python visualization is transforming rapidly, and it can be quite difficult to choose and learn the right tools. numpy : efficient manipulation of multidimensional data arrays, and fundamental package for scientific computing in python.geopandas : describe and analyze geographical data.bokeh : low-level visualization backend, based on JavaScript.holoviews : high-level visualization of multidimensional data.Here is a simplified description of the dependencies between some of these packages: The python visualization landscape : orientationīy installing geoviews, we have actually installed a large number of python packages, that are (or might be) needed for geographical data analysis and visualization.
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