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 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.
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Improved By : retr0
- Python | Program to implement Jumbled word game
- Gradient Descent in Linear Regression
- Applying Convolutional Neural Network on mnist dataset
- Python | Tensorflow nn.relu() and nn.leaky_relu()
- Python | Plotting bar charts in excel sheet using XlsxWriter module
- Python | Plotting Radar charts in excel sheet using XlsxWriter module
- ML | Linear Regression
- Pygorithm module in Python
- Python | Getting started with SymPy module
- numpy.polysub() in Python