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Fonction nanprod - module numpy.matlib

Signature de la fonction nanprod

def nanprod(a, axis=None, dtype=None, out=None, keepdims=<no value>) 

Description

nanprod.__doc__

    Return the product of array elements over a given axis treating Not a
    Numbers (NaNs) as ones.

    One is returned for slices that are all-NaN or empty.

    .. versionadded:: 1.10.0

    Parameters
    ----------
    a : array_like
        Array containing numbers whose product is desired. If `a` is not an
        array, a conversion is attempted.
    axis : {int, tuple of int, None}, optional
        Axis or axes along which the product is computed. The default is to compute
        the product of the flattened array.
    dtype : data-type, optional
        The type of the returned array and of the accumulator in which the
        elements are summed.  By default, the dtype of `a` is used.  An
        exception is when `a` has an integer type with less precision than
        the platform (u)intp. In that case, the default will be either
        (u)int32 or (u)int64 depending on whether the platform is 32 or 64
        bits. For inexact inputs, dtype must be inexact.
    out : ndarray, optional
        Alternate output array in which to place the result.  The default
        is ``None``. If provided, it must have the same shape as the
        expected output, but the type will be cast if necessary. See
        :ref:`ufuncs-output-type` for more details. The casting of NaN to integer
        can yield unexpected results.
    keepdims : bool, optional
        If 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 `arr`.

    Returns
    -------
    nanprod : ndarray
        A new array holding the result is returned unless `out` is
        specified, in which case it is returned.

    See Also
    --------
    numpy.prod : Product across array propagating NaNs.
    isnan : Show which elements are NaN.

    Examples
    --------
    >>> np.nanprod(1)
    1
    >>> np.nanprod([1])
    1
    >>> np.nanprod([1, np.nan])
    1.0
    >>> a = np.array([[1, 2], [3, np.nan]])
    >>> np.nanprod(a)
    6.0
    >>> np.nanprod(a, axis=0)
    array([3., 2.])