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Module « numpy »

Fonction nanpercentile - module numpy

Signature de la fonction nanpercentile

def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, weights=None, interpolation=None) 

Description

help(numpy.nanpercentile)

Compute the qth percentile of the data along the specified axis,
while ignoring nan values.

Returns the qth percentile(s) of the array elements.

Parameters
----------
a : array_like
    Input array or object that can be converted to an array, containing
    nan values to be ignored.
q : array_like of float
    Percentile or sequence of percentiles to compute, which 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`.

    If this is anything but the default value it will be passed
    through (in the special case of an empty array) to the
    `mean` function of the underlying array.  If the array is
    a sub-class and `mean` does not have the kwarg `keepdims` this
    will raise a RuntimeError.

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.

    .. 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
--------
nanmean
nanmedian : equivalent to ``nanpercentile(..., 50)``
percentile, median, mean
nanquantile : equivalent to nanpercentile, except q in range [0, 1].

Notes
-----
The behavior of `numpy.nanpercentile` with percentage `q` is that of
`numpy.quantile` with argument ``q/100`` (ignoring nan values).
For more information, please see `numpy.quantile`.

Examples
--------
>>> import numpy as np
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
array([[10.,  nan,   4.],
      [ 3.,   2.,   1.]])
>>> np.percentile(a, 50)
np.float64(nan)
>>> np.nanpercentile(a, 50)
3.0
>>> np.nanpercentile(a, 50, axis=0)
array([6.5, 2. , 2.5])
>>> np.nanpercentile(a, 50, axis=1, keepdims=True)
array([[7.],
       [2.]])
>>> m = np.nanpercentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanpercentile(a, 50, axis=0, out=out)
array([6.5, 2. , 2.5])
>>> m
array([6.5,  2. ,  2.5])

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

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

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