numpy.full_like() in Python
Last Updated :
09 Mar, 2022
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
import numpy as geek
x = geek.arange( 10 , dtype = int ).reshape( 2 , 5 )
print ( "x before full_like : \n" , x)
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)
print ( "\ny after full_like : \n" , geek.full_like(y, 0.01 ))
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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 IDE’s. So please, run them on your systems to explore the working.
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