The numpy.full_like() function return a new array with the same shape and type as a given array.
Syntax : 
 

numpy.full_like(a, fill_value, dtype = None, order = 'K', subok = True)

Parameters : 

shape : Number of rows
order : C_contiguous or F_contiguous
dtype : [optional, float(by Default )] Data type of returned array.  
subok : [bool, optional] to make subclass of a or not

Returns : 

ndarray

# Python Programming illustrating
# numpy.full_like method

import numpy as geek

x = geek.arange(10, dtype = int).reshape(2, 5)
print("x before full_like : \n", x)

# using full_like
print("\nx after full_like : \n", geek.full_like(x, 10.0))

y = geek.arange(10, dtype = float).reshape(2, 5)
print("\n\ny before full_like : \n", x)

# using full_like
print("\ny after full_like : \n", geek.full_like(y, 0.01))

Output : 
 

x before full_like : 
 [[0 1 2 3 4]
 [5 6 7 8 9]]

x after full_like : 
 [[10 10 10 10 10]
 [10 10 10 10 10]]


y before full_like : 
 [[0 1 2 3 4]
 [5 6 7 8 9]]

y after full_like : 
 [[ 0.01  0.01  0.01  0.01  0.01]
 [ 0.01  0.01  0.01  0.01  0.01]]

References : 
https://numpy.org/doc/stable/reference/generated/numpy.full_like.html#numpy.full_like 
Note : 
These codes won’t run on online IDE's. So please, run them on your systems to explore the working.