scipy stats.mode() function | Python

scipy.stats.mode(array, axis=0) function calculates the mode of the array elements along the specified axis of the array (list in python).


Its formula –

where,
l : Lower Boundary of modal class
h : Size of modal class
fm : Frequency corresponding to modal class
f1 : Frequency preceding  to modal class
f2 : Frequency proceeding to modal class

Parameters :
array : Input array or object having the elements to calculate the mode.
axis : Axis along which the mode is to be computed. By default axis = 0

Returns : Modal values of the array elements based on the set parameters.

Code #1:

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# Arithmetic mode  
from scipy import stats
import numpy as np 
  
arr1 = np.array([[1, 3, 27, 13, 21, 9],
                [8, 12, 8, 4, 7, 10]]) 
   
  
print("Arithmetic mode is : \n", stats.mode(arr1)) 

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Output :

Arithmetic mode is : 
 ModeResult(mode=array([[1, 3, 8, 4, 7, 9]]), count=array([[1, 1, 1, 1, 1, 1]]))

 
Code #2: With multi-dimensional data

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# Arithmetic mode 
from scipy import stats
import numpy as np 
  
arr1 = [[1, 3, 27], 
        [3, 4, 6], 
        [7, 6, 3], 
        [3, 6, 8]] 
    
print("Arithmetic mode is : \n", stats.mode(arr1)) 
  
print("\nArithmetic mode is : \n", stats.mode(arr1, axis = None)) 
  
print("\nArithmetic mode is : \n", stats.mode(arr1, axis = 0)) 
  
print("\nArithmetic mode is : \n", stats.mode(arr1, axis = 1)) 

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Output :

Arithmetic mode is : 
 ModeResult(mode=array([[3, 6, 3]]), count=array([[2, 2, 1]]))

Arithmetic mode is : 
 ModeResult(mode=array([3]), count=array([4]))

Arithmetic mode is : 
 ModeResult(mode=array([[3, 6, 3]]), count=array([[2, 2, 1]]))

Arithmetic mode is : 
 ModeResult(mode=array([[1],
       [3],
       [3],
       [3]]), count=array([[1],
       [1],
       [1],
       [1]]))


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