Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé

Module « numpy »

Fonction percentile - module numpy

Signature de la fonction percentile

def percentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, weights=None, interpolation=None) 

Description

help(numpy.percentile)

Compute the q-th percentile of the data along the specified axis.

Returns the q-th percentile(s) of the array elements.

Parameters
----------
a : array_like of real numbers
    Input array or object that can be converted to an array.
q : array_like of float
    Percentage or sequence of percentages for the percentiles to compute.
    Values must be between 0 and 100 inclusive.
axis : {int, tuple of int, None}, optional
    Axis or axes along which the percentiles are computed. The
    default is to compute the percentile(s) along a flattened
    version of the array.
out : ndarray, optional
    Alternative output array in which to place the result. It must
    have the same shape and buffer length as the expected output,
    but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
    If True, then allow the input array `a` to be modified by intermediate
    calculations, to save memory. In this case, the contents of the input
    `a` after this function completes is undefined.
method : str, optional
    This parameter specifies the method to use for estimating the
    percentile.  There are many different methods, some unique to NumPy.
    See the notes for explanation.  The options sorted by their R type
    as summarized in the H&F paper [1]_ are:

    1. 'inverted_cdf'
    2. 'averaged_inverted_cdf'
    3. 'closest_observation'
    4. 'interpolated_inverted_cdf'
    5. 'hazen'
    6. 'weibull'
    7. 'linear'  (default)
    8. 'median_unbiased'
    9. 'normal_unbiased'

    The first three methods are discontinuous.  NumPy further defines the
    following discontinuous variations of the default 'linear' (7.) option:

    * 'lower'
    * 'higher',
    * 'midpoint'
    * 'nearest'

    .. versionchanged:: 1.22.0
        This argument was previously called "interpolation" and only
        offered the "linear" default and last four options.

keepdims : bool, optional
    If this is set to True, the axes which are reduced are left in
    the result as dimensions with size one. With this option, the
    result will broadcast correctly against the original array `a`.

 weights : array_like, optional
    An array of weights associated with the values in `a`. Each value in
    `a` contributes to the percentile according to its associated weight.
    The weights array can either be 1-D (in which case its length must be
    the size of `a` along the given axis) or of the same shape as `a`.
    If `weights=None`, then all data in `a` are assumed to have a
    weight equal to one.
    Only `method="inverted_cdf"` supports weights.
    See the notes for more details.

    .. versionadded:: 2.0.0

interpolation : str, optional
    Deprecated name for the method keyword argument.

    .. deprecated:: 1.22.0

Returns
-------
percentile : scalar or ndarray
    If `q` is a single percentile and `axis=None`, then the result
    is a scalar. If multiple percentiles are given, first axis of
    the result corresponds to the percentiles. The other axes are
    the axes that remain after the reduction of `a`. If the input
    contains integers or floats smaller than ``float64``, the output
    data-type is ``float64``. Otherwise, the output data-type is the
    same as that of the input. If `out` is specified, that array is
    returned instead.

See Also
--------
mean
median : equivalent to ``percentile(..., 50)``
nanpercentile
quantile : equivalent to percentile, except q in the range [0, 1].

Notes
-----
The behavior of `numpy.percentile` with percentage `q` is
that of `numpy.quantile` with argument ``q/100``.
For more information, please see `numpy.quantile`.

Examples
--------
>>> import numpy as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> np.percentile(a, 50)
3.5
>>> np.percentile(a, 50, axis=0)
array([6.5, 4.5, 2.5])
>>> np.percentile(a, 50, axis=1)
array([7.,  2.])
>>> np.percentile(a, 50, axis=1, keepdims=True)
array([[7.],
       [2.]])

>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])

>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
array([7.,  2.])
>>> assert not np.all(a == b)

The different methods can be visualized graphically:

.. plot::

    import matplotlib.pyplot as plt

    a = np.arange(4)
    p = np.linspace(0, 100, 6001)
    ax = plt.gca()
    lines = [
        ('linear', '-', 'C0'),
        ('inverted_cdf', ':', 'C1'),
        # Almost the same as `inverted_cdf`:
        ('averaged_inverted_cdf', '-.', 'C1'),
        ('closest_observation', ':', 'C2'),
        ('interpolated_inverted_cdf', '--', 'C1'),
        ('hazen', '--', 'C3'),
        ('weibull', '-.', 'C4'),
        ('median_unbiased', '--', 'C5'),
        ('normal_unbiased', '-.', 'C6'),
        ]
    for method, style, color in lines:
        ax.plot(
            p, np.percentile(a, p, method=method),
            label=method, linestyle=style, color=color)
    ax.set(
        title='Percentiles for different methods and data: ' + str(a),
        xlabel='Percentile',
        ylabel='Estimated percentile value',
        yticks=a)
    ax.legend(bbox_to_anchor=(1.03, 1))
    plt.tight_layout()
    plt.show()

References
----------
.. [1] R. J. Hyndman and Y. Fan,
   "Sample quantiles in statistical packages,"
   The American Statistician, 50(4), pp. 361-365, 1996

Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé