MENU
Edit
In Place Editing Advanced Editing

numpy.ma.empty_like()

numpy.ma.empty_like(a, dtype=None, order='K', subok=True) =

Return a new 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.

dtype : data-type, optional

Overrides the data type of the result.

New in version 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 a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible.

New in version 1.6.0.

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.
Returns:

out : ndarray

Array of uninitialized (arbitrary) data with the same shape and type as a.

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.
empty
Return a new uninitialized array.
ones
Return a new array setting values to one.
zeros
Return a new array setting values to zero.

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

1
2
3
4
5
6
7
8
>>> a = ([1,2,3], [4,5,6])                         # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821,           3],    #random
       [          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],#random
       [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])
doc_NumPy
doc_NumPy
2025-01-10 15:47:30
Comments
Leave a Comment

Please login to continue.