`numpy.ma.core:empty_like`

source: /numpy/ma/core.py :8749

Signature

   def     empty_like (    prototype    ,  / ,     dtype    =  None   ,    order    =  K   ,    subok    =  True   ,    shape    =  None   ,  * ,     device    =  None     )

Summary

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 : MaskedArray: Array of uninitialized (arbitrary) data with the same shape and type as prototype.

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)
a = np.array([[1., 2., 3.],[4.,5.,6.]])
np.empty_like(a)

Aliases

numpy.ma.empty_like