numpy.maximum() in Python

Syntax : numpy.maximum(arr1, arr2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, ufunc 'maximum') Parameters : arr1 : [array_like] Input array. arr2 : [array_like] 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. **kwargs : allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function. where : [array_like, optional]True value means to calculate the universal functions(ufunc) at that position, False value means to leave the value in the output alone. Return : [ndarray or scalar] Result. The maximum of arr1 and arr2, element-wise. This is a scalar if both arr1 and arr2 are scalars.

# Python program explaining
# maximum() function

import numpy as geek
in_num1 = 10
in_num2 = 21

print ("Input  number1 : ", in_num1)
print ("Input  number2 : ", in_num2) 
  
out_num = geek.maximum(in_num1, in_num2) 
print ("maximum of 10 and 21 : ", out_num) 

Input  number1 :  10
Input  number2 :  21
maximum of 10 and 21 :  21

# Python program explaining
# maximum() function

import numpy as geek

in_arr1 = [2, 8, 125]
in_arr2 = [3, 3, 15]
 
print ("Input array1 : ", in_arr1) 
print ("Input array2 : ", in_arr2)
  
out_arr = geek.maximum(in_arr1, in_arr2) 
print ("Output array after selecting maximum: ", out_arr) 

Input array1 :  [2, 8, 125]
Input array2 :  [3, 3, 15]
Output array after selecting maximum:  [  3   8 125]

# Python program explaining
# maximum() function

import numpy as geek

in_arr1 = [geek.nan, 0, geek.nan]
in_arr2 = [geek.nan, geek.nan, 0]
 
print ("Input array1 : ", in_arr1) 
print ("Input array2 : ", in_arr2)
  
out_arr = geek.maximum(in_arr1, in_arr2) 
print ("Output array after selecting maximum: ", out_arr) 

Input array1 :  [nan, 0, nan]
Input array2 :  [nan, nan, 0]
Output array after selecting maximum:  [ nan  nan  nan]

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Article Tags :

• Python
• Python-numpy
• Python numpy-Mathematical Function

Practice Tags :

• python