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Fonction empty_like - module numpy

Signature de la fonction empty_like

Description

empty_like.__doc__

    empty_like(prototype, dtype=None, order='K', subok=True, shape=None)

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

    Parameters
    ----------
    prototype : array_like
        The shape and data-type of `prototype` define these same attributes
        of the returned array.
    dtype : data-type, optional
        Overrides the data type of the result.

        .. versionadded:: 1.6.0
    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 `prototype` is Fortran
        contiguous, 'C' otherwise. 'K' means match the layout of `prototype`
        as closely as possible.

        .. versionadded:: 1.6.0
    subok : bool, optional.
        If True, then the newly created array will use the sub-class
        type of `prototype`, 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 uninitialized (arbitrary) data with the same
        shape and type as `prototype`.

    See Also
    --------
    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_like : Return a new array with shape of input filled with value.
    empty : Return a new uninitialized array.

    Notes
    -----
    This function does *not* initialize the returned array; to do that use
    `zeros_like` or `ones_like` instead.  It may be marginally faster than
    the functions that do set the array values.

    Examples
    --------
    >>> a = ([1,2,3], [4,5,6])                         # a is array-like
    >>> np.empty_like(a)
    array([[-1073741821, -1073741821,           3],    # uninitialized
           [          0,           0, -1073741821]])
    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
    >>> np.empty_like(a)
    array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000], # uninitialized
           [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])