In the previous recipe, we learned how we can limit the scaling of our charts. In this recipe, we will go one step further by dynamically adjusting the scaling by setting both a limit and analyzing our data before we represent it.
We will enhance the code from the previous recipe, Scaling charts, by reading in the data we are plotting dynamically, averaging it, and then adjusting our chart.
While we would typically read in the data from an external source, in this recipe we'll create the data we are plotting using Python lists, as can be seen in the code in the following section.
We are creating our own data in our Python module by assigning lists with data to the xValues and yValues variables.
In many graphs, the beginning of the x and y coordinate system starts at (0, 0). This is usually a good idea, so let's adjust our chart coordinate code accordingly.
Let's modify the code to set limits on both the x and y dimensions:
Matplotlib_labels_two_charts_scaled_dynamic...