I agree — transitions are a really bad way to think about animations. Another approach is to think of an interpolation curve as the step response to an LTI filter. This approach lets you turn any interpolation curve into a FIR filter that can handle interrupted transitions seamlessly.
You might want to consider using kernel density plots rather than histograms, as histograms exhibit aliasing artifacts.
In general, binning/bucketing can be seen as filtering the empirical density function of your dataset with a box filter and then sampling. The frequency response of a box filter is the sinc function, which has a lot of energy above the Nyquist of this sampling rate. Kernel density plots with a gaussian kernel, on the other hand, can be seen as filtering the empirical density function with a gaussian filter and are thus approximately bandlimited.
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