How to visualize change in a distribution with a few outliers that account for a very large percent of the total? I'm working on an edtech product where some of our traffic lands on webpages about textbooks. 

Textbooks belong to subjects like Algebra, Calculus and Spanish. In each of our subjects, we have "whales" - individual books that account for a large percent (~20%) of total subject traffic.

Year over year, these whales grow or shrink (sometimes books are replaced in a school, or schools drop textbooks altogether). This change in whale traffic contributes to a big change in overall subject traffic.

I'm trying to figure out how to visualize this change, given a dataset that looks something like the table attached below (it shows only 2 months traffic per book, but I have access to all the months).

I've tried overlapping histograms (and boxplots), where each histogram is a month. But this visualization doesn't indicate how huge my whales (outliers) are, and how much influence they have.

Any help on chart types or how to otherwise tell this story would be much appreciated.
To visualize change in the size of multiple entities that are contributing to a total through time, e.g. total (t) = book_1 (t) + book_2 (t) + ..., we can use Stacked Area Plot. This plot can be used for normalized and un-normalized (absolute) values.



Preprocessing


For large number of entities, to avoid cognitive load, we can keep only 
those entities that become significant (whale) at some point in the plot, and group all the others under an "ordinary" entity. This way, cognitive load is minimized and only those entities that matter at some point are distinguished. For example, distinguishing books that have more than 10% of traffic at some point in the plotted time span.
If total fluctuation is very high, logarithm of values can be plugged into the plot.