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 ofprototype define these same attributesof the returned array.

dtypedata-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’ ifprototype is Fortrancontiguous, ‘C’ otherwise. ‘K’ means match the layout ofprototypeas closely as possible.

subokbool, optional.

If True, then the newly created array will use the sub-classtype ofprototype, otherwise it will be a base-class array. Defaultsto True.

shapeint or sequence of ints, optional.

Overrides the shape of the result. If order=’K’ and the number ofdimensions is unchanged, will try to keep order, otherwise,order=’C’ is implied.

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 sameshape and type asprototype.

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 thenewly allocated array are arbitrary. For reproducible behavior, be sureto set each element of the array before reading.

Examples

>>>importnumpyasnp>>>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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