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scipy.stats.Uniform.

# kurtosis

Uniform.kurtosis(*, method=None, convention='non-excess')

  Kurtosis (standardized fourth moment)

  By default, this is the standardized fourth moment, also known as the “non-excess” or “Pearson” kurtosis (e.g. the kurtosis of the normal distribution is 3). The “excess” or “Fisher” kurtosis (the standardized fourth moment minus 3) is available via the convention parameter.

  Parameters:
    method{None, ‘formula’, ‘general’, ‘transform’, ‘normalize’, ‘cache’}

      Method used to calculate the standardized fourth moment. Not all methods are available for all distributions. See `moment` for details.

    convention{‘non-excess’, ‘excess’}

      Two distinct conventions are available:

      - `'non-excess'`: the standardized fourth moment (Pearson’s kurtosis)

      - `'excess'`: the standardized fourth moment minus 3 (Fisher’s kurtosis)

      The default is `'non-excess'`.

  See also

  `moment`
  `mean`
  `variance`

  References

  [1]

  Kurtosis, Wikipedia, https://en.wikipedia.org/wiki/Kurtosis

  Examples

  Instantiate a distribution with the desired parameters:

      >>> from scipy import stats
      >>> X = stats.Normal(mu=1., sigma=2.)

  Evaluate the kurtosis:

      >>> X.kurtosis()
      3.0
      >>> (X.kurtosis()
      ...  == X.kurtosis(convention='excess') + 3.
      ...  == X.moment(order=4, kind='standardized'))
      True