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.
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 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)))
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 mean of the above data-sets print("Harmonic mean of data set 1 is % s" % (mode(data1))) print("Harmonic mean of data set 2 is % s" % (mode(data2))) print("Harmonic mean of data set 3 is % s" % (mode(data3))) print("Harmonic mean of data set 4 is % s" % (mode(data4))) print("Harmonic mean of data set 5 is % s" % (mode(data5)))
Harmonic mean of data set 1 is 5 Harmonic mean of data set 2 is 1.3 Harmonic mean of data set 3 is 1/2 Harmonic mean of data set 4 is -2 Harmonic mean 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 print(statistics.mode(data1))
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 '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.
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