numpy.empty_like#
- numpy.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:
- prototypearray_like
The shape and data-type of prototype define these same attributes of the returned array.
- dtypedata-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 prototype is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of prototype as closely as possible.
New in version 1.6.0.
- subokbool, 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.
- shapeint 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.
New in version 1.17.0.
- devicestr, optional
The device on which to place the created array. Default: None. For Array-API interoperability only, so must be
"cpu"
if passed.New in version 2.0.0.
- Returns:
- outndarray
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
>>> 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], # uninit [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])