scipy.stats.percentileofscore#

scipy.stats.percentileofscore(a, score, kind='rank', nan_policy='propagate')[source]#

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 to which score is compared.

scorearray_like

Scores to compute percentiles for.

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

nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional

Specifies how to treat nan values in a. The following options are available (default is ‘propagate’):

  • ‘propagate’: returns nan (for each value in score).

  • ‘raise’: throws an error

  • ‘omit’: performs the calculations ignoring nan values

Returns:
pcosfloat

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

See also

numpy.percentile
scipy.stats.scoreatpercentile, scipy.stats.rankdata

Examples

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

>>> import numpy as np
>>> 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

Score arrays (of any dimensionality) are supported:

>>> stats.percentileofscore([1, 2, 3, 3, 4], [2, 3])
array([40., 70.])

The inputs can be infinite:

>>> stats.percentileofscore([-np.inf, 0, 1, np.inf], [1, 2, np.inf])
array([75., 75., 100.])

If a is empty, then the resulting percentiles are all nan:

>>> stats.percentileofscore([], [1, 2])
array([nan, nan])