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Module « numpy »

Fonction empty_like - module numpy

Signature de la fonction empty_like

Description

help(numpy.empty_like)

empty_like(prototype, dtype=None, order='K', subok=True, shape=None, *,
           device=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.
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.
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.
device : str, optional
    The device on which to place the created array. Default: None.
    For Array-API interoperability only, so must be ``"cpu"`` if passed.

    .. versionadded:: 2.0.0

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
-----
Unlike other array creation functions (e.g. `zeros_like`, `ones_like`,
`full_like`), `empty_like` does not initialize the values of the array,
and may therefore be marginally faster. However, the values stored in the
newly allocated array are arbitrary. For reproducible behavior, be sure
to set each element of the array before reading.

Examples
--------
>>> import numpy as np
>>> 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]])

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