scipy.stats.percentileofscore

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

Compute 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

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

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