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