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Fonction nanquantile - module numpy.matlib

Signature de la fonction nanquantile

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

Description

nanquantile.__doc__

    Compute the qth quantile of the data along the specified axis,
    while ignoring nan values.
    Returns the qth quantile(s) of the array elements.

    .. versionadded:: 1.15.0

    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
        Quantile or sequence of quantiles to compute, which 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.
    interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
        This optional parameter specifies the interpolation method to
        use when the desired quantile lies between two data points
        ``i < j``:

        * linear: ``i + (j - i) * fraction``, where ``fraction``
          is the fractional part of the index surrounded by ``i``
          and ``j``.
        * lower: ``i``.
        * higher: ``j``.
        * nearest: ``i`` or ``j``, whichever is nearest.
        * midpoint: ``(i + j) / 2``.

    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.

    Returns
    -------
    quantile : scalar or ndarray
        If `q` is a single percentile and `axis=None`, then the result
        is a scalar. If multiple quantiles 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].

    Examples
    --------
    >>> 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)
    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)