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mode() function in Python statistics module
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
# Python code to demonstrate the# use of mode() function# mode() function a sub-set of the statistics module# We need to import the statistics module before doing any workimport 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-setprint("Mode of given data set is % s" % (statistics.mode(set1))) |
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
# Python code to demonstrate the# working of mode() function# on a various range of data types# Importing the statistics modulefrom statistics import mode# Importing fractions module as fr# Enables to calculate harmonic_mean of a# set in Fractionfrom fractions import Fraction as fr# tuple of positive integer numbersdata1 = (2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 7)# tuple of a set of floating point valuesdata2 = (2.4, 1.3, 1.3, 1.3, 2.4, 4.6)# tuple of a set of fractional numbersdata3 = (fr(1, 2), fr(1, 2), fr(10, 3), fr(2, 3))# tuple of a set of negative integersdata4 = (-1, -2, -2, -2, -7, -7, -9)# tuple of stringsdata5 = ("red", "blue", "black", "blue", "black", "black", "brown")# Printing out the mode of the above data-setsprint("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))) |
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
# Python code to demonstrate the # statistics error in mode function '''StatisticsError is raised while using mode when there aretwo equal modes present in a data set and when the data setis empty or null''' # importing statistics moduleimport statistics # creating a data set consisting of two equal data-setsdata1 =[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 print(statistics.mode(data1)) |
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 dataNOTE: 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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