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
# 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
# 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