empty_like(prototype, dtype=None, order='K', subok=True, shape=None)
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.
The shape and data-type of :None:None:`prototype` define these same attributes of the returned array.
Overrides the data type of the result.
Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if :None:None:`prototype` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of :None:None:`prototype` as closely as possible.
If True, then the newly created array will use the sub-class type of :None:None:`prototype`, otherwise it will be a base-class array. Defaults to True.
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.
Array of uninitialized (arbitrary) data with the same shape and type as :None:None:`prototype`.
Return a new array with the same shape and type as a given array.
empty
Return a new uninitialized array.
full_like
Return a new array with shape of input filled with value.
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.
>>> 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.]])See :
... np.empty_like(a) array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.full_like
numpy.zeros_like
numpy.empty
numpy.ones_like
numpy.array
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