Module « scipy.stats »

Fonction percentileofscore - module scipy.stats

Signature de la fonction percentileofscore

def percentileofscore(a, score, kind='rank') 

Description

percentileofscore.__doc__

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
    ----------
    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
        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
    -------
    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