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

arr :  [1, 2, 7, 0, nan]
Min of arr :  nan
nanMin of arr :  0.0

# Python Program illustrating 
# numpy.nanmin() method 
  
import numpy as np
  
# 2D array 
arr = [[np.nan, 17, 12, 33, 44],  
       [15, 6, 27, 8, 19]] 
print("\narr : \n", arr) 
     
# Minimum of the flattened array 
print("\nMin of arr, axis = None : ", np.nanmin(arr)) 
     
# Minimum along the first axis 
# axis 0 means vertical 
print("Min of arr, axis = 0 : ", np.nanmin(arr, axis = 0)) 
     
# Minimum along the second axis 
# axis 1 means horizontal 
print("Min of arr, axis = 1 : ", np.nanmin(arr, axis = 1))  

arr : 
 [[14, 17, 12, 33, 44], [15, 6, 27, 8, 19]]

Min of arr, axis = None :  6
Min of arr, axis = 0 :  [14  6 12  8 19]
Min of arr, axis = 1 :  [12  6]

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

Initial arr1 :  [0 1 2 3 4]
Changed arr1(having results) :  [14  6 12  8 19]