numpy.zeros_like() in Python

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