Vous êtes un professionnel et vous avez besoin d'une formation ? Coder avec une
Intelligence Artificielle Voir le programme détaillé

Module « scipy.stats »

Fonction percentileofscore - module scipy.stats

Signature de la fonction percentileofscore

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

Description

help(scipy.stats.percentileofscore)

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
    A 1-D array to which `score` is compared.
score : array_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
-------
pcos : float
    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])

Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé