mode() function in Python statistics module
Last Updated :
23 Aug, 2021
The mode of a set of data values is the value that appears most often. It is the value at which the data is most likely to be sampled. A mode of a continuous probability distribution is often considered to be any value x at which its probability density function has a local maximum value, so any peak is a mode.
Python is very robust when it comes to statistics and working with a set of a large range of values. The statistics module has a very large number of functions to work with very large data-sets. The mode() function is one of such methods. This function returns the robust measure of a central data point in a given range of data-sets.
Example :
Given data-set is : [1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]
The mode of the given data-set is 4
Logic: 4 is the most occurring/ most common element from the given list
Syntax :
mode([data-set])
Parameters :
[data-set] which is a tuple, list or a iterator of
real valued numbers as well as Strings.
Return type :
Returns the most-common data point from discrete or nominal data.
Errors and Exceptions :
Raises StatisticsError when data set is empty.
Code #1 : This piece will demonstrate mode() function through a simple example.
Python3
import statistics
set1 = [ 1 , 2 , 3 , 3 , 4 , 4 , 4 , 5 , 5 , 6 ]
print ( "Mode of given data set is % s" % (statistics.mode(set1)))
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Output
Mode of given data set is 4
Code #2 : In this code we will be demonstrating the mode() function a various range of data-sets.
Python3
from statistics import mode
from fractions import Fraction as fr
data1 = ( 2 , 3 , 3 , 4 , 5 , 5 , 5 , 5 , 6 , 6 , 6 , 7 )
data2 = ( 2.4 , 1.3 , 1.3 , 1.3 , 2.4 , 4.6 )
data3 = (fr( 1 , 2 ), fr( 1 , 2 ), fr( 10 , 3 ), fr( 2 , 3 ))
data4 = ( - 1 , - 2 , - 2 , - 2 , - 7 , - 7 , - 9 )
data5 = ( "red" , "blue" , "black" , "blue" , "black" , "black" , "brown" )
print ( "Mode of data set 1 is % s" % (mode(data1)))
print ( "Mode of data set 2 is % s" % (mode(data2)))
print ( "Mode of data set 3 is % s" % (mode(data3)))
print ( "Mode of data set 4 is % s" % (mode(data4)))
print ( "Mode of data set 5 is % s" % (mode(data5)))
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Output
Mode of data set 1 is 5
Mode of data set 2 is 1.3
Mode of data set 3 is 1/2
Mode of data set 4 is -2
Mode of data set 5 is black
Code #3 : In this piece of code will demonstrate when StatisticsError is raised
Python3
import statistics
data1 = [ 1 , 1 , 1 , - 1 , - 1 , - 1 ]
print (statistics.mode(data1))
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Output
Traceback (most recent call last):
File "/home/38fbe95fe09d5f65aaa038e37aac20fa.py", line 20, in
print(statistics.mode(data1))
File "/usr/lib/python3.5/statistics.py", line 474, in mode
raise StatisticsError('no mode for empty data') from None
statistics.StatisticsError: no mode for empty data
NOTE: In newer versions of Python, like Python 3.8, the actual mathematical concept will be applied when there are multiple modes for a sequence, where, the smallest element is considered as a mode.
Say, for the above code, the frequencies of -1 and 1 are the same, however, -1 will be the mode, because of its smaller value.
Applications: The mode() is a statistics function and mostly used in Financial Sectors to compare values/prices with past details, calculate/predict probable future prices from a price distribution set. mean() is not used separately but along with two other pillars of statistics mean and median creates a very powerful tool that can be used to reveal any aspect of your data.
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