# Python Program illustrating 
# numpy.nanmax() method 
    
import numpy as np
    
# 1D array 
arr = [1, 2, 7, 0, np.nan]
print("arr : ", arr) 
print("max of arr : ", np.amax(arr))
  
# nanmax ignores NaN values. 
print("nanmax of arr : ", np.nanmax(arr))
  

arr :  [1, 2, 7, 0, nan]
max of arr :  nan
nanmax of arr :  7.0

import numpy as np
  
# 2D array 
arr = [[np.nan, 17, 12, 33, 44],  
       [15, 6, 27, 8, 19]] 
print("\narr : \n", arr) 
     
# maximum of the flattened array 
print("\nmax of arr, axis = None : ", np.nanmax(arr)) 
     
# maximum along the first axis 
# axis 0 means vertical 
print("max of arr, axis = 0 : ", np.nanmax(arr, axis = 0)) 
     
# maximum along the second axis 
# axis 1 means horizontal 
print("max of arr, axis = 1 : ", np.nanmax(arr, axis = 1)) 

arr : 
 [[nan, 17, 12, 33, 44], [15, 6, 27, 8, 19]]

max of arr, axis = None :  44.0
max of arr, axis = 0 :  [15. 17. 27. 33. 44.]
max of arr, axis = 1 :  [44. 27.]

import numpy as np
  
arr1 = np.arange(5) 
print("Initial arr1 : ", arr1)
   
# using out parameter
np.nanmax(arr, axis = 0, out = arr1)
   
print("Changed arr1(having results) : ", arr1)  

Initial arr1 :  [0 1 2 3 4]
Changed arr1(having results) :  [15 17 27 33 44]