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Module « numpy.matlib »

Fonction nanquantile - module numpy.matlib

Signature de la fonction nanquantile

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

Description

help(numpy.matlib.nanquantile)

Compute the qth quantile of the data along the specified axis,
while ignoring nan values.
Returns the qth quantile(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
    Probability or sequence of probabilities for the quantiles to compute.
    Values must be between 0 and 1 inclusive.
axis : {int, tuple of int, None}, optional
    Axis or axes along which the quantiles are computed. The
    default is to compute the quantile(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
    quantile.  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 quantile 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
-------
quantile : scalar or ndarray
    If `q` is a single probability and `axis=None`, then the result
    is a scalar. If multiple probability levels are given, first axis of
    the result corresponds to the quantiles. 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
--------
quantile
nanmean, nanmedian
nanmedian : equivalent to ``nanquantile(..., 0.5)``
nanpercentile : same as nanquantile, but with q in the range [0, 100].

Notes
-----
The behavior of `numpy.nanquantile` is the same as that of
`numpy.quantile` (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.quantile(a, 0.5)
np.float64(nan)
>>> np.nanquantile(a, 0.5)
3.0
>>> np.nanquantile(a, 0.5, axis=0)
array([6.5, 2. , 2.5])
>>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
array([[7.],
       [2.]])
>>> m = np.nanquantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanquantile(a, 0.5, axis=0, out=out)
array([6.5, 2. , 2.5])
>>> m
array([6.5,  2. ,  2.5])
>>> b = a.copy()
>>> np.nanquantile(b, 0.5, 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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