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numpy.full_like () in Python

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numpy.full_like (a, fill_value, dtype = None, order = & # 39; K & # 39 ;, subok = True): return a new array with the same shape and type as at the given array. 
Parameters :

  shape:  Number of rows  order:  C_contiguous or F_contiguous  dtype:  [optional, float (by Default)] Data type of returned array.  subok:  [bool, optional] to make subclass of a or not 

Returns :

 ndarray 

# Python Programming Illustrative
# numpy.full_like method

 

import numpy as geek

 

x = geek.arange ( 10 , dtype = int ). reshape ( 2 , 5 )

print ( "x before full_like:" , x)

  
# using full_like

print ( " x after full_like: " , geek.full_like (x, 10.0 ))

  

y = geek.arange ( 10 , dtype = float ). reshape ( 2 , 5 )

print ( " y before full_like: " , x) 

 
# using full_like

print ( "y after full_like:" , geek.full_like (y, 0.01 ))

Output:

 x before full_like: [[0 1 2 3 4] [5 6 7 8 9]] x after full_like: [[10 10 10 10 10] [10 10 10 10 10]] y before full_like: [[0 1 2 3 4] [5 6 7 8 9]] y after full_like: [[0.01 0.01 0.01 0.01 0.01] [0.01 0.01 0.01 0.01 0.01]] 

Links:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.full_like.html#numpy.full_like
Notes:
These codes will not work for online IDs. Please run them on your systems to see how they work.
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This article is provided by Mohit Gupta_OMG

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