# scipy stats.mode () function | python

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

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