NumPy array creation: zeros_like() function

Last update on February 26 2020 08:08:51 (UTC/GMT +8 hours)

zeros_like() function

The zeros_like() function is used to get an array of zeros with the same shape and type as a given array.

Syntax:

numpy.zeros_like(a, dtype=None, order=’K’, subok=True)

Version: 1.15.0

Parameter:

Name	Description	Required / Optional
a	The shape and data-type of a define these same attributes of the returned array.	Required
dtype	Overrides the data type of the result. New in version 1.6.0.	optional
order	Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if a is Fortran contiguous, 'C' otherwise. 'K' means match the layout of a 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 zeros with the same shape and type as a.

Example: numpy.zeros()

>>> import numpy as np
>>> a = np.arange(4)
>>> a = a.reshape((2, 2))
>>> a
array([[0, 1],
       [2, 3]])
>>> np.zeros_like(a)
array([[0, 0],
       [0, 0]])

Pictorial Presentation:

Example: numpy.zeros() where data type is int

>>> import numpy as np
>>> b = np.arange(5, dtype=float)
>>> b
array([ 0.,  1.,  2.,  3.,  4.])
>>> np.zeros_like(b)
array([ 0.,  0.,  0.,  0.,  0.])

Pictorial Presentation:

Python - NumPy Code Editor:

Previous: zeros()
Next: full()