Dear Gyan Prakash Rai,

The data below may help:

How to Calculate KL Divergence in Python (Including Example)

In statistics, the Kullback–Leibler (KL) divergence is a distance metric that quantifies the difference between two probability distributions.

If we have two probability distributions, P and Q, we typically write the KL divergence using the notation KL(P || Q), which means “P’s divergence from Q.”

We calculate it using the following formula:

KL(P || Q) = ΣP(x) ln(P(x) / Q(x))

If the KL divergence between two distributions is zero, then it indicates that the distributions are identical.

https://www.statology.org/kl-divergence-python/

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Measuring the statistical similarity between two samples using Jensen-Shannon and Kullback-Leibler divergences

https://medium.com/datalab-log/measuring-the-statistical-similarity-between-two-samples-using-jensen-shannon-and-kullback-leibler-8d05af514b15

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Efficient way of computing Kullback–Leibler divergence in Python

htps://stackoverflow.com/questions/34007028/efficient-way-of-computing-kullback-leibler-divergence-in-python

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Thank you so much Len Leonid Mizrah