Its formula is —

 where,  l:  Lower Boundary of modal class  h:  Size of modal class  f  m :  Frequency corresponding to modal class  f  1 :  Frequency preceding to modal class  f  2 :  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 :

# 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: " , stats.mode (arr1))

Output:

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

Code # 2: with multidimensional data

 

# 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:" , stats.mode (arr1))     print ( "Arithmetic mode is:" , stats.mode (arr1, axis = None ))    print ( "Arithmetic mode is:" , stats.mode (arr1, axis = 0 ))    print ( "Arithmetic mode is:" , stats.mode (arr1, axis = 1 ))

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]]))