This numpy method returns an array of given shape and type as given array, with zeros. 
 

Syntax: numpy.zeros_like(array, dtype = None, order = 'K', subok = True)

Parameters : 

array : array_like input
subok  : [optional, boolean]If true, then newly created array will be sub-class of array; 
                 otherwise, a base-class array
order  : C_contiguous or F_contiguous
         C-contiguous order in memory(last index varies the fastest)
         C order means that operating row-rise on the array will be slightly quicker
         FORTRAN-contiguous order in memory (first index varies the fastest).
         F order means that column-wise operations will be faster. 
dtype  : [optional, float(byDefault)] Data type of returned array.  

Returns : 

ndarray of zeros having given shape, order and datatype.

Code 1 :  

# Python Programming illustrating
# numpy.zeros_like method

import numpy as geek

array = geek.arange(10).reshape(5, 2)
print("Original array : \n", array)


b = geek.zeros_like(array, float)
print("\nMatrix b : \n", b)

array = geek.arange(8)
c = geek.zeros_like(array)
print("\nMatrix c : \n", c)

Output: 

Original array : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

Matrix b : 
 [[ 0.  0.]
 [ 0.  0.]
 [ 0.  0.]
 [ 0.  0.]
 [ 0.  0.]]

Matrix c : 
 [0 0 0 0 0 0 0 0]

Code 2 : 

# Python Programming illustrating
# numpy.zeros_like method

import numpy as geek

array = geek.arange(10).reshape(5, 2)
print("Original array : \n", array)

array = geek.arange(4).reshape(2, 2)
c = geek.zeros_like(array, dtype = 'float')
print("\nMatrix  : \n", c)

array = geek.arange(8)
c = geek.zeros_like(array, dtype = 'float', order ='C')
print("\nMatrix  : \n", c)

Output : 

Original array : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

Matrix  : 
 [[ 0.  0.]
 [ 0.  0.]]

Matrix  : 
 [ 0.  0.  0.  0.  0.  0.  0.  0.]

 
Note : 
Also, these codes won’t run on online IDE's. Please run them on your systems to explore the working