NumPy: empty_like() function

Last update on May 28 2022 11:21:10 (UTC/GMT +8 hours)

empty_like() function

The empty_like() function is used to create a new array with the same shape and type as a given array.

Syntax:

numpy.empty_like(prototype, dtype=None, order='K', subok=True)

Version: 1.15.0

Parameter:

Name	Description	Required / Optional
prototype	The shape and data-type of prototype define these same attributes of the returned array.	Required
dtype	Overrides the data type of the result.	optional
order	Overrides the memory layout of the result. ‘C’ means Corder, ‘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.	optional
subok	If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True.	optional

Return value:

[ndarray] Array of uninitialized (arbitrary) data with the same shape and type as prototype

Example: NumPy.empty_like()

>>> import numpy as np
>>> a = ([1,2,3], [4,5,6])
>>> np.empty_like(a)
array([[139706697202600,        28351168, 139706713266976],
       [139706682360248, 139706669155640, 139706713256944]])
>>>
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[  6.92340539e-310,   9.95630813e-317,   6.92340619e-310],
       [  6.92340619e-310,   1.66406600e-287,   9.67127608e-292]])
>>>

Pictorial Presentation:

Example: NumPy.empty_like() where dtype is int

>>> import numpy as np
>>> a = np.empty_like([2, 2], dtype = int)
>>> print(a)
[139942630210440 139942630210440]
>>> 
>>> a = np.empty_like([2, 2], dtype = float)
>>> print(a)
[  6.93999521e-310   6.93999521e-310]

Example: NumPy.empty_like() where order is 'C' or 'F'

>>> import numpy as np
>>> a = np.empty_like([2, 2], dtype = int, order='C')
>>> print(a)
[140209094482824 140209094482824]
>>> a = np.empty_like([2, 2], dtype = int, order='F')
>>> print(a)
[0 0]
>>>

Example: NumPy.empty_like() using subok parameter (True/False)

>>> import numpy as np
>>> a = np.empty_like([2, 2], dtype = int, order='C', subok=True)
>>> print(a)
[0 0]
>>> a = np.empty_like([2, 2], dtype = int, order='C', subok=False)
>>> print(a)
[0 0]
>>>

Python - NumPy Code Editor:

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