Python statistics | mode function

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 locally maximum value, so any peak is a mode.

Python is very robust when it comes to statistics and working with a set of 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 :
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 there are two modes 
present in a single list, or when data set is empty .

Code #1 :
This piece will demonstrate mode() function through a simple example.

# Python code to demonstrate the 
# use of mode() function
# mode() function a sub-set of the statistics module
# We need to import statistics module before doing any work
import statistics
# declaring a simple data-set consisting of real valued
# positive integers.
set1 =[1, 2, 3, 3, 4, 4, 4, 5, 5, 6]
# In the given data-set
# Count of 1 is 1
# Count of 2 is 1
# Count of 3 is 2
# Count of 4 is 3
# Count of 5 is 2
# Count of 6 is 1
# We can infer that 4 has the highest population distribution
# So mode of set1 is 4
# Printing out mode of given data-set
print("Mode of given data set is % s" % (statistics.mode(set1)))

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.

# Python code to demonstrate the
# working of mode() function
# on a various range of data types
# Importing the statistics module
from statistics import mode
# Importing fractions module as fr
# Enables to calculate harmonic_mean of a
# set in Fraction
from fractions import Fraction as fr
# tuple of positive integer numbers
data1 = (2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 7)
# tuple of a set of floating point values
data2 = (2.4, 1.3, 1.3, 1.3, 2.4, 4.6)
# tuple of a set of fractional numbers
data3 = (fr(1, 2), fr(1, 2), fr(10, 3), fr(2, 3))
# tuple of a set of negaitve integers
data4 = (-1, -2, -2, -2, -7, -7, -9)
# tuple of strings
data5 = ("red", "blue", "black", "blue", "black", "black", "brown")
# Printing out the mode of the above data-sets
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)))

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

# Python code to demonstrate the 
# statistics error in mode function
StatisticsError is raised while using mode when there are
two equal modes present in a data set and when the data set
is empty or null
# importing statistics module
import statistics
# creating a data set consisting of two equal data-sets
data1 =[1, 1, 1, -1, -1, -1]
# In the above data set
# Count of 1 is 3
# Count of -1 is also 3
# StatisticsError will be raised

Output L

Traceback (most recent call last):
  File "/home/", line 20, in 
  File "/usr/lib/python3.5/", line 474, in mode
    'no unique mode; found %d equally common values' % len(table)
statistics.StatisticsError: no unique mode; found 2 equally common values

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 seperately but along with two other pillars of statistics mean and meadian creates a very powerful tool which can be used to reveal any aspect of your data.

My Personal Notes arrow_drop_up

Its lonely at the top

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