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
# Python Programming illustrating# numpy.full_like methodimport numpy as geekx = geek.arange(10, dtype = int).reshape(2, 5)print("x before full_like : \n", x)# using full_likeprint("\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_likeprint("\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 IDE’s. So please, run them on your systems to explore the working.
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