KL-divergence returns infinity Given an original probability distribution P, I want to measure how much an approximation Q differs from the initial distribution. For that I calculate the KL-divergence via scipy.stats.entropy, which returns infinity due to the large difference. However, as with time the approximation becomes better, I still want to quantify the divergence between the two sets. 

The question is, is there any hack to avoid inf values or should I circumvent the behaviour by using some other distance measure?
The infinity is due to divide by zero. 
Just replace any zero value with a very small value.
This question is very common , if you search for it you will find many questions similar to it . 
Remember that kl is not a metric