bundles / numpy 2.4.3 / numpy / empty_like
_ArrayFunctionDispatcher
numpy:empty_like
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_likeThe shape and data-type of
prototypedefine these same attributes of the returned array.dtype: data-type, optionalOverrides the data type of the result.
order: {'C', 'F', 'A', or 'K'}, optionalOverrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if
prototypeis Fortran contiguous, 'C' otherwise. 'K' means match the layout ofprototypeas 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, optionalThe device on which to place the created array. Default: None. For Array-API interoperability only, so must be
"cpu"if passed.
Returns
out: ndarrayArray 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)
✗See also
- 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.
Aliases
-
numpy.empty_like