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# scipy.stats.entropy

`scipy.stats.``entropy`(pk, qk=None, base=None)

  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.

  Parameters: | 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).
  Returns: | S : float The calculated entropy.