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**Relative frequency** measures how frequently a certain value occurs in a dataset *relative *to the total number of values in a dataset.

You can use the following function in Python to calculate relative frequencies:

def rel_freq(x):freqs = [(value, x.count(value) / len(x)) for value in set(x)]return freqs

The following examples show how to use this function in practice.

**Example 1: Relative Frequencies for a List of Numbers**

The following code shows how to use this function to calculate relative frequencies for a list of numbers:

#define data data = [1, 1, 1, 2, 3, 4, 4] #calculate relative frequencies for each value in list rel_freq(data) [(1, 0.42857142857142855), (2, 0.14285714285714285), (3, 0.14285714285714285), (4, 0.2857142857142857)]

The way to interpret this output is as follows:

- The value “1” has a relative frequency of
**0.42857**in the dataset. - The value “2” has a relative frequency of
**0.142857**in the dataset. - The value “3” has a relative frequency of
**0.142857**in the dataset. - The value “4” has a relative frequency of
**0.28571**in the dataset.

You’ll notice that all of the relative frequencies add up to 1.

**Example 2: Relative Frequencies for a List of Characters**

The following code shows how to use this function to calculate relative frequencies for a list of characters:

#define data data = ['a', 'a', 'b', 'b', 'c'] #calculate relative frequencies for each value in list rel_freq(data) [('a', 0.4), ('b', 0.4), ('c', 0.2)]

The way to interpret this output is as follows:

- The value “a” has a relative frequency of
**0.4**in the dataset. - The value “b” has a relative frequency of
**0.4**in the dataset. - The value “c” has a relative frequency of
**0.2**in the dataset.

Once again, all of the relative frequencies add up to 1.

**Example 3: Relative Frequencies for a Column in a pandas DataFrame**

The following code shows how to use this function to calculate relative frequencies for a specific column in a pandas DataFrame:

import pandas as pd #define data data = pd.DataFrame({'A': [25, 15, 15, 14, 19], 'B': [5, 7, 7, 9, 12], 'C': [11, 8, 10, 6, 6]}) #calculate relative frequencies of values in column 'A' rel_freq(list(data['A'])) [(25, 0.2), (19, 0.2), (14, 0.2), (15, 0.4)]

The way to interpret this output is as follows:

- The value “25” has a relative frequency of
**0.2**in the column. - The value “19” has a relative frequency of
**0.2**in the column. - The value “14” has a relative frequency of
**0.2**in the column. - The value “15” has a relative frequency of
**0.4**in the column.

Once again, all of the relative frequencies add up to 1.

**Additional Resources**

Relative Frequency Calculator

Relative Frequency Histogram: Definition + Example

How to Calculate Relative Frequency in Excel