The best way to calculate variations between 2 datasets?
What you are attempting to do is calculate the distance between two discrete probability density functions. The standard way to calculate this distance is via the Kullback-Leibler Divergence. It is defined as such:

$$D(P||Q) = \sum_iP(i)*log(\frac{P(i)}{Q(i)})$$

To be technically correct, this is not a metric in a mathematical sense because it is not symmetric and does not abide by the triangle inequality. But never the less it is a "distance" that is used a lot to compare distributions. From an information theoretical perspective, the KL divergence (given that you're using base 2 for the log) represents the number of bits it would take to encode distribution P as Q. 

In your case, you would have to normalize each distribution to ensure that the sum is 1, and then calculate distance via the KL divergence. If you want a real metric you can use the Jensen-Shannon Divergence which is defined as:
$${{\rm {JSD}}}(P\parallel Q)={\frac  {1}{2}}D(P\parallel M)+{\frac  {1}{2}}D(Q\parallel M)$$$$
{\displaystyle M={\frac {1}{2}}(P+Q)} M={\frac  {1}{2}}(P+Q)$$

Hope this helps.