Summary
In this chapter, we focused on temporal data visualizations. Firstly, we learned the theory behind temporal data. Then, we covered the real-world applications of temporal data.
We used the pandas time function to learn about transforming date columns, such as setting time as an index value in line plots and analyzing data at different frequency levels. Time is sequential in nature, so we covered the shift and tshift functions, which can be used to compare current observations with past observations and to find out if there are any correlations.
We also looked at the Bokeh plotting interface. We plotted graphs using increasing levels of complexity and also explained how to add interactive annotations to play around with the time axis.
Finally, we covered the most important plots that will interact with users without running a server using the ipywidgets.interact and push_notebook() functions.
In the next chapter, we will see how to create interactive visualizations...