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 = 0Returns : 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 : \n" , 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 multi-dimensional 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 : \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 )) |
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]]))