scipy.stats.percentileofscore¶

scipy.stats.
percentileofscore
(a, score, kind='rank')[source]¶ 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
Returns: pcos : float
Percentileposition of score (0100) relative to a.
See also
Examples
Threequarters 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