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numpy.full_like() in Python
  • Difficulty Level : Basic
  • Last Updated : 07 Dec, 2020

The numpy.full_like() function return a new array with the same shape and type as a given array.

Syntax :

numpy.full_like(a, fill_value, dtype = None, order = 'K', subok = True)

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 illustrating
# numpy.full_like method
  
import numpy as geek
  
x = geek.arange(10, dtype = int).reshape(2, 5)
print("x before full_like : \n", x)
  
# using full_like
print("\nx after full_like : \n", geek.full_like(x, 10.0))
  
y = geek.arange(10, dtype = float).reshape(2, 5)
print("\n\ny before full_like : \n", x)
  
# using full_like
print("\ny after full_like : \n", 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]]

References :
https://docs.scipy.org/doc/numpy/reference/generated/numpy.full_like.html#numpy.full_like
Note :
These codes won’t run on online-ID. Please run them on your systems to explore the working
.
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