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Fonction full_like - module numpy.matlib

Signature de la fonction full_like

def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None) 

Description

full_like.__doc__

    Return a full array with the same shape and type as a given array.

    Parameters
    ----------
    a : array_like
        The shape and data-type of `a` define these same attributes of
        the returned array.
    fill_value : scalar
        Fill value.
    dtype : data-type, optional
        Overrides the data type of the result.
    order : {'C', 'F', 'A', or 'K'}, optional
        Overrides the memory layout of the result. 'C' means C-order,
        'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
        'C' otherwise. 'K' means match the layout of `a` as closely
        as possible.
    subok : bool, optional.
        If True, then the newly created array will use the sub-class
        type of `a`, otherwise it will be a base-class array. Defaults
        to True.
    shape : int or sequence of ints, optional.
        Overrides the shape of the result. If order='K' and the number of
        dimensions is unchanged, will try to keep order, otherwise,
        order='C' is implied.

        .. versionadded:: 1.17.0

    Returns
    -------
    out : ndarray
        Array of `fill_value` with the same shape and type as `a`.

    See Also
    --------
    empty_like : Return an empty array with shape and type of input.
    ones_like : Return an array of ones with shape and type of input.
    zeros_like : Return an array of zeros with shape and type of input.
    full : Return a new array of given shape filled with value.

    Examples
    --------
    >>> x = np.arange(6, dtype=int)
    >>> np.full_like(x, 1)
    array([1, 1, 1, 1, 1, 1])
    >>> np.full_like(x, 0.1)
    array([0, 0, 0, 0, 0, 0])
    >>> np.full_like(x, 0.1, dtype=np.double)
    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
    >>> np.full_like(x, np.nan, dtype=np.double)
    array([nan, nan, nan, nan, nan, nan])

    >>> y = np.arange(6, dtype=np.double)
    >>> np.full_like(y, 0.1)
    array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])