numpy.add() in Python
numpy.add()
function is used when we want to compute the addition of two array. It add arguments element-wise. If shape of two arrays are not same, that is arr1.shape != arr2.shape
, they must be broadcastable to a common shape (which may be the shape of one or the other).
Syntax : numpy.add(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj], ufunc ‘add’)
Parameters :
arr1 : [array_like or scalar] Input array.
arr2 : [array_like or scalar] Input array.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
where : [array_like, optional] Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs :Allows to pass keyword variable length of argument to a function. Used when we want to handle named argument in a function.Return : [ndarray or scalar] The sum of arr1 and arr2, element-wise. Returns a scalar if both arr1 and arr2 are scalars.
Code #1 : Working
# Python program explaining # numpy.add() function # when inputs are scalar import numpy as geek in_num1 = 10 in_num2 = 15 print ( "1st Input number : " , in_num1) print ( "2nd Input number : " , in_num2) out_num = geek.add(in_num1, in_num2) print ( "output number after addition : " , out_num) |
Output :
1st Input number : 10 2nd Input number : 15 output number after addition : 25
Code #2 :
# Python program explaining # numpy.add() function # when inputs are array import numpy as geek in_arr1 = geek.array([[ 2 , - 7 , 5 ], [ - 6 , 2 , 0 ]]) in_arr2 = geek.array([[ 5 , 8 , - 5 ], [ 3 , 6 , 9 ]]) print ( "1st Input array : " , in_arr1) print ( "2nd Input array : " , in_arr2) out_arr = geek.add(in_arr1, in_arr2) print ( "output added array : " , out_arr) |
Output :
1st Input array : [[ 2 -7 5] [-6 2 0]] 2nd Input array : [[ 5 8 -5] [ 3 6 9]] output added array : [[ 7 1 0] [-3 8 9]]
Last Updated on March 1, 2022 by admin