scipy stats.mode() function | Python
Last Updated :
11 Feb, 2019
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:
Python3
# 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
Python3
# 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]]))
Similar Reads
sciPy stats.mean() function | Python scipy.stats.mean(array, axis=0) function calculates the arithmetic mean of the array elements along the specified axis of the array (list in python). It's formula - Parameters : array: Input array or object having the elements to calculate the arithmetic mean. axis: Axis along which the mean is to b
1 min read
scipy stats.normaltest() function | Python scipy.stats.normaltest(array, axis=0) function test whether the sample is different from the normal distribution. This function tests the null hypothesis of the population that the sample was drawn from. Parameters : array : Input array or object having the elements. axis : Axis along which the norm
1 min read
sciPy stats.sem() function | Python scipy.stats.sem(arr, axis=0, ddof=0) function is used to compute the standard error of the mean of the input data. Parameters : arr : [array_like]Input array or object having the elements to calculate the standard error. axis : Axis along which the mean is to be computed. By default axis = 0. ddof :
1 min read
scipy stats.kurtosis() function | Python scipy.stats.kurtosis(array, axis=0, fisher=True, bias=True) function calculates the kurtosis (Fisher or Pearson) of a data set. It is the fourth central moment divided by the square of the variance. It is a measure of the "tailedness" i.e. descriptor of shape of probability distribution of a real-va
2 min read
sciPy stats.tmax() function | Python scipy.stats.tmax(array, lowerlimit=None, axis=0, inclusive=True) function calculates the trimmed maximum of the array elements along with ignoring the values lying outside the specified limits, along the specified axis. Parameters : array: Input array or object having the elements to calculate the t
2 min read