An Introduction to Statistics Mode

In Statistics, the value which occurs more often in a provided set of data values is known as the mode. In other terms, the number or value which has a high frequency or appears repeatedly is known as the mode or the modal value. Mode is among the three measures of the Central Tendency. The other two measures are Mean and Median, respectively.

For instance –

We have a set A = {4, 5, 6, 6, 7, 8, 9}. Since the digit 6 has a higher frequency, therefore the mode of set A is 6. Thus, it is easy to find the mode for a finite number of observations. A set of data values may have one modal value or multiple modal values or no mode at all. A mode of the continuous probability distribution is often said to be any value x. Its probability density function has a maximum local value, so any peak is a mode.

The mode() function in Python

Python becomes a pretty strong programming language while dealing with Statistics and engaging with a set of a large range of data values. Python provides the statistics module with many functions to work with pretty large datasets, and the mode() function is one of them. The mode() function is used to return the robust measure of a central data point in a provided range of datasets.

The mode() function is the only function in the standard statistics library of the Python programming language which can be applied to non-numeric (nominal) data.

Let us look at the syntax of the mode function in Python.

Syntax:

The syntax of the mode() function is shown below:

```
statistics.mode(data)
```

Parameters of the mode() function in Python

The parameter of the mode() function is data. It can be an iterative or sequence – for example, lists, tuples, and a lot more.

Note: A StatisticsError will be raised by the mode() function if the data parameter is empty.

Return Value of the mode() function in Python

The mode() function will return a floating-point number or non-numeric (nominal) value as per the provided data in the parameter once calculated the mode of the provided data in iterators (For example, lists, tuples, and a lot more).

Let us consider some examples based on the mode() function of the Standard statistics library of Python programming language.

Example 1: Finding the mode of the dataset given below:

```
# importing the statistics library
import statistics
# creating the data set
my_set = [10, 20, 30, 30, 40, 40, 40, 50, 50, 60]
# estimating the mode of the given set
my_mode = statistics.mode( my_set)
# printing the estimated mode to the users
print("Mode of given set of data values is", my_mode)
```

Output:

`Mode of given set of data values is 40`

Explanation:

In the above example, we have imported the statistics library and created a set as my_set. We have then estimated the mode of the given set using the statistics.mode() function and printed its value to the user. As a result, the value having the highest frequency in the set has been printed successfully.

Example 2: Demonstrating the mode() function working on different varieties of data types.

```
# importing the statistics library
import statistics
# importing the fractions module
from fractions import Fraction as fr
# creating the tuple of positive integer numbers
data_1 = (20, 30, 30, 40, 50, 50, 50, 60, 70, 70)
# creating the tuple of floating point values
data_2 = (1.2, 2.3, 2.3, 3.4, 4.5, 4.5, 4.5, 5.6, 5.6, 7.8)
# creating the tuple of fractional numbers
data_3 = (fr(1,3), fr(1,5), fr(1,5), fr(2,3), fr(3,4), fr(8,9))
# creating the tuple of negative integer numbers
data_4 = (-9, -8, -7, -7, -7, -6, -5, -5, -4, -2)
# creating the tuple of strings
data_5 = ("apple", "mango", "mango", "mango", "banana", "guava", "guava")
# estimating the mode of the given datasets
mode_1 = statistics.mode( data_1)
mode_2 = statistics.mode( data_2)
mode_3 = statistics.mode( data_3)
mode_4 = statistics.mode( data_4)
mode_5 = statistics.mode( data_5)
# printing the estimated modes to the users
print("1. Mode of First Data set is", mode_1)
print("2. Mode of Second Data set is", mode_2)
print("3. Mode of Third Data set is", mode_3)
print("4. Mode of Forth Data set is", mode_4)
print("5. Mode of Fifth Data set is", mode_5)
```

Output:

```
1. Mode of First Data set is 50
2. Mode of Second Data set is 4.5
3. Mode of Third Data set is 1/5
4. Mode of Forth Data set is -7
5. Mode of Fifth Data set is mango
```

Explanation:

In the above example, we have imported the statistics library and the fractions module. We have then created a different range of tuples to check if the mode() function works on various data types. We have created a tuple of positive integers, floating-point values, fractional numbers, negative integers, and strings. We have then used the statistics.mode() function to calculate the mode of each dataset. We have then printed these estimated values to the users.

Some Applications of mode() function

The mode() function is a statistics function that is generally used in Financial Sectors in order to compare the prices and values with previous records. It also helps in calculating and predicting the probable future prices from a price distribution set. The mode() function is not utilized separately; however, along with two other measures of statistics called mean and median. These three together work as a powerful utility to reveal many aspects of the data.

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