Mode represents the single most frequent value in a list, while multimode provides a list of all the values that occur with the highest frequency.
In statistical analysis, the mode is a measure of central tendency that identifies the most frequently occurring value in a dataset. However, datasets can sometimes exhibit multiple modes, where two or more values share the highest frequency. In such cases, the multimode becomes a valuable descriptive statistic, providing a more complete picture of the data’s distribution by highlighting all values that achieve this maximum frequency.
This tutorial shows how to calculate multimode in Python using the statistics module, returning all values that share the highest frequency in a list.
Multimode Calculation Using the statistics Module
To calculate multimode, we need to import the statistics module.
Fortunately, Python’s statistics.multimode() function makes it easy to calculate multimode in Python, automatically collecting every value that occurs with maximum frequency in your dataset.
import statistics as s
x = [1, 5, 7, 5, 43, 43, 8, 43, 6]
multimode = s.multimode(x)
print("Multimode equals: " + str(multimode))
In this example the list x contains several numbers, with 43 appearing most frequently (three times). The multimode function returns a list containing 43, which is the value with the highest frequency.
Understanding the Difference Between Mode and Multimode
The mode is the most frequent number in a list of numbers.
The multimode is a list of the most frequent numbers in a list of numbers.
For example, if a list of numbers is [1, 2, 3, 3, 4, 4], then the mode would be either 3 or 4, and the multimode would be [3, 4], assuming both values appear with the highest frequency.
The difference between mode and multimode is that mode is a single number, while multimode is a list of numbers. While mode provides a quick snapshot of the most frequent value, multimode offers a more detailed view, especially in datasets where multiple values occur with the same highest frequency.
While the mode offers a single, representative value for the most frequent data point, the multimode provides a more comprehensive perspective, particularly in datasets exhibiting multiple values sharing the highest frequency of occurrence.