scipy.stats.entropy(pk, qk=None, base=None)[source]

Calculate the entropy of a distribution for given probability values.

If only probabilities pk are given, the entropy is calculated as S = -sum(pk * log(pk), axis=0).

If qk is not None, then compute the Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0).

This routine will normalize pk and qk if they don’t sum to 1.

pk : sequence

Defines the (discrete) distribution. pk[i] is the (possibly unnormalized) probability of event i.

qk : sequence, optional

Sequence against which the relative entropy is computed. Should be in the same format as pk.

base : float, optional

The logarithmic base to use, defaults to e (natural logarithm).

S : float

The calculated entropy.