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# scipy.stats.percentileofscore

`scipy.stats.``percentileofscore`(a, score, kind='rank')

  The percentile rank of a score relative to a list of scores.

  A `percentileofscore` of, for example, 80% means that 80% of the scores in a are below the given score. In the case of gaps or ties, the exact definition depends on the optional keyword, kind.

  Parameters:

  a : array_like

  > Array of scores to which score is compared.

  score : int or float

  > Score that is compared to the elements in a.

  kind : {‘rank’, ‘weak’, ‘strict’, ‘mean’}, optional

  > This optional parameter specifies the interpretation of the resulting score:

  > - “rank”: Average percentage ranking of score. In case of

  >     multiple matches, average the percentage rankings of all matching scores.

  > - “weak”: This kind corresponds to the definition of a cumulative

  >     distribution function. A percentileofscore of 80% means that 80% of values are less than or equal to the provided score.

  > - “strict”: Similar to “weak”, except that only values that are

  >     strictly less than the given score are counted.

  > - “mean”: The average of the “weak” and “strict” scores, often used in

  >     testing. See

  >     http://en.wikipedia.org/wiki/Percentile_rank

  Returns:

  pcos : float

  > Percentile-position of score (0-100) relative to a.

  See also

  `numpy.percentile`

  Examples

  Three-quarters of the given values lie below a given score:

      >>> from scipy import stats
      >>> stats.percentileofscore([1, 2, 3, 4], 3)
      75.0

  With multiple matches, note how the scores of the two matches, 0.6 and 0.8 respectively, are averaged:

      >>> stats.percentileofscore([1, 2, 3, 3, 4], 3)
      70.0

  Only 2/5 values are strictly less than 3:

      >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='strict')
      40.0

  But 4/5 values are less than or equal to 3:

      >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='weak')
      80.0

  The average between the weak and the strict scores is

      >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='mean')
      60.0