numpy.zeros_like() in Python
Last Updated :
08 Mar, 2024
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
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)
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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
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)
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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
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