When plotting huge data sets using Python while keeping interactivity, Datashader is paramount. In this post, I demonstrate the abilities of this powerful and convenient library. We use an unique dataset containing a whole year of shared bike usage in Cologne to plot over a million locations on a map.

Do you want to build map visualizations in Python? Look no further than GeoViews. It is not only super simple to use but also offers several interactive features that make your visualization stand out. Using geo spatial data from our bike rental data set we explore some of the possibilities.

Does the physicians rating platform Jameda discriminate against non-paying users? We investigate whether this claim made in an article by the newspaper "Die Zeit" can be supported. For that, we conduct a thorough exploratory data analysis on an extensive data set of the platform. In the end, we come to a fact based conclusion.

Building on a basic choropleth map, we learn how to add additional interaction for the user. We visualize change in data patterns over time by allowing dynamic control over the plotted data. Moreover, we add animation to our visualization.

Using our bike data, we look at a different method to make a choropleth map. With the help of bokeh it is easy to create nice looking visualizations. They are interactive and can easily be integrated into web applications or websites.