numpy.zeros_like () in Python

numpy.zeros_like (array, dtype = None, order = & # 39; K & # 39 ;, subok = True): return an array of the given shape and type as a given array with zeros,
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 (byDeafult)] Data type of returned array. 

Returns :

 ndarray of zeros having given shape, order and datatype. 


Code 1 :

# Python Programming Illustrative
# numpy.zeros_like method

 

import numpy as geek

 

array = geek.arange ( 10 ). reshape ( 5 , 2 )

print ( " Original array: " , array) < / code>

 

 

b = geek.zeros_like (array, float )

print ( "Matrix b:" , b)

  

array = geek.arange ( 8 )

c = geek.zeros_like (array)

print ( "Matrix c:" , c)

Exit:

 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 Illustrative
# numpy.zeros_like method

 

import numpy as geek

 

array = geek.arange ( 10 ). reshape ( 5 , 2 )

print ( "Original array:" , array)

  

array = geek.arange ( 4 ) .reshape ( 2 , 2 )

c = geek.zeros_like (array, dtype = 'float' )

print ( "Matrix :" , c)

 

array = geek.arange ( 8 )

c = geek.zeros_like (array, dtype = 'float' , order = 'C' )

print ( "Matrix :" , 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.] 

Links:
https://docs.scipy.org/doc/numpy-dev/reference/generated/ numpy.zeros_like.html # numpy.zeros_like
Notes:
Also, these codes will not work for online IDs. Please run them on your systems to see how they work

This article is courtesy of Mohit Gupta_OMG



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