scipy.stats.percentileofscore#
- scipy.stats.percentileofscore(a, score, kind='rank')[source]#
Compute the percentile rank of a score relative to a list of scores.
A
percentileofscoreof, 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
- aarray_like
Array of scores to which score is compared.
- scoreint or float
Score that is compared to the elements in a.
- kind{‘rank’, ‘weak’, ‘strict’, ‘mean’}, optional
Specifies the interpretation of the resulting score. The following options are available (default is ‘rank’):
‘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 https://en.wikipedia.org/wiki/Percentile_rank
- Returns
- pcosfloat
Percentile-position of score (0-100) relative to a.
See also
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