numpy.full_like() in Python (original) (raw)

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 `

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 IDE's. So please, run them on your systems to explore the working.