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numpy.ones_like() in Python
  • Last Updated : 22 Oct, 2020

The numpy.one_like() function returns an array of given shape and type as a given array, with ones.

Syntax: numpy.ones_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-wise 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 ones having given shape, order and datatype.

# Python Programming illustrating
# numpy.ones_like method
import numpy as geek
array = geek.arange(10).reshape(5, 2)
print("Original array : \n", array)
b = geek.ones_like(array, float)
print("\nMatrix b : \n", b)
array = geek.arange(8)
c = geek.ones_like(array)
print("\nMatrix c : \n", c)


Original array : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

Matrix b : 
 [[ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]]

Matrix c : 
 [1 1 1 1 1 1 1 1]

References :
Note :
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working

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