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Module « scipy.stats »

Fonction rankdata - module scipy.stats

Signature de la fonction rankdata

def rankdata(a, method='average', *, axis=None, nan_policy='propagate') 

Description

help(scipy.stats.rankdata)

Assign ranks to data, dealing with ties appropriately.

By default (``axis=None``), the data array is first flattened, and a flat
array of ranks is returned. Separately reshape the rank array to the
shape of the data array if desired (see Examples).

Ranks begin at 1.  The `method` argument controls how ranks are assigned
to equal values.  See [1]_ for further discussion of ranking methods.

Parameters
----------
a : array_like
    The array of values to be ranked.
method : {'average', 'min', 'max', 'dense', 'ordinal'}, optional
    The method used to assign ranks to tied elements.
    The following methods are available (default is 'average'):

      * 'average': The average of the ranks that would have been assigned to
        all the tied values is assigned to each value.
      * 'min': The minimum of the ranks that would have been assigned to all
        the tied values is assigned to each value.  (This is also
        referred to as "competition" ranking.)
      * 'max': The maximum of the ranks that would have been assigned to all
        the tied values is assigned to each value.
      * 'dense': Like 'min', but the rank of the next highest element is
        assigned the rank immediately after those assigned to the tied
        elements.
      * 'ordinal': All values are given a distinct rank, corresponding to
        the order that the values occur in `a`.
axis : {None, int}, optional
    Axis along which to perform the ranking. If ``None``, the data array
    is first flattened.
nan_policy : {'propagate', 'omit', 'raise'}, optional
    Defines how to handle when input contains nan.
    The following options are available (default is 'propagate'):

      * 'propagate': propagates nans through the rank calculation
      * 'omit': performs the calculations ignoring nan values
      * 'raise': raises an error

    .. note::

        When `nan_policy` is 'propagate', the output is an array of *all*
        nans because ranks relative to nans in the input are undefined.
        When `nan_policy` is 'omit', nans in `a` are ignored when ranking
        the other values, and the corresponding locations of the output
        are nan.

    .. versionadded:: 1.10

Returns
-------
ranks : ndarray
     An array of size equal to the size of `a`, containing rank
     scores.

References
----------
.. [1] "Ranking", https://en.wikipedia.org/wiki/Ranking

Examples
--------
>>> import numpy as np
>>> from scipy.stats import rankdata
>>> rankdata([0, 2, 3, 2])
array([ 1. ,  2.5,  4. ,  2.5])
>>> rankdata([0, 2, 3, 2], method='min')
array([ 1,  2,  4,  2])
>>> rankdata([0, 2, 3, 2], method='max')
array([ 1,  3,  4,  3])
>>> rankdata([0, 2, 3, 2], method='dense')
array([ 1,  2,  3,  2])
>>> rankdata([0, 2, 3, 2], method='ordinal')
array([ 1,  2,  4,  3])
>>> rankdata([[0, 2], [3, 2]]).reshape(2,2)
array([[1. , 2.5],
      [4. , 2.5]])
>>> rankdata([[0, 2, 2], [3, 2, 5]], axis=1)
array([[1. , 2.5, 2.5],
       [2. , 1. , 3. ]])
>>> rankdata([0, 2, 3, np.nan, -2, np.nan], nan_policy="propagate")
array([nan, nan, nan, nan, nan, nan])
>>> rankdata([0, 2, 3, np.nan, -2, np.nan], nan_policy="omit")
array([ 2.,  3.,  4., nan,  1., nan])

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