Given a table with many columns, an understanding of the range and simple statistics of the feature values in every column often results in an individual becoming curious about how different features affect one another. Relationships between features are modeled as correlation measures. Formulating and computing correlations between features in a dataset is a complex problem. Sometimes, joint distribution plots are able to encapsulate and visualize these relationships very well.
We can visualize multiple features for every row at once as points on a chart. The bubble chart in Superset can be used to visualize a feature type on the y axis perpendicular to the x axis timeline. A second feature is color-coded, and a third feature value is reflected as bubble size in a group of one or more rows in a dataset. In this chapter, we will make the following charts:
- A multiple line chart against a time x axis differentiated by color-coding
- An area chart against a time x axis differentiated by color-coding
- A bubble chart
- A dual axis line chart
- A time series percentage change
There are other charts that reflect the relative percent change for each time step. A rose chart displays the feature value distributed over arcs on concentric circles, so that you can visually compare across multiple features. There are effective charts for feature comparison available in Superset that are not used in this chapter. Do explore other charts with your own datasets so that you can develop intuition for selecting charts.
In this chapter, we will specifically cover the following, using a time series dataset:
- Comparing multiple time series
- Comparing two time series
- Identifying differences in trends for two feature values