numpy.nanmin() in Python

Last Updated : 29 Nov, 2018

numpy.nanmin()function is used when to returns minimum value of an array or along any specific mentioned axis of the array, ignoring any Nan value.

Syntax : numpy.nanmin(arr, axis=None, out=None)
Parameters :
arr :Input array.
axis :Axis along which we want the min value. Otherwise, it will consider arr to be flattened(works on all the axis). axis = 0 means along the column
and axis = 1 means working along the row.
out :Different array in which we want to place the result. The array must have same dimensions as expected output.

Return :Minimum array value(a scalar value if axis is none) or array with minimum value along specified axis.

Code #1 : Working

   # 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)) 
 
  

Output :

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

Code #2 :

   # 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))   
 
  

Output :

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]

Code #3 :

   # 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)

Output :

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

numpy.nansum() in Python

mohit gupta_omg :)

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