Python Pandas - Return the relative frequency from Index object

To return the relative frequency from an Index object, use the index.value_counts() method with the normalize parameter set to True. This calculates the proportion of each unique value relative to the total count.

Syntax

index.value_counts(normalize=True)

Creating a Pandas Index

First, let's create a Pandas Index with some duplicate values ?

import pandas as pd

# Creating Pandas index with duplicate values
index = pd.Index([50, 10, 70, 110, 90, 50, 110, 90, 30])

print("Pandas Index...")
print(index)
print(f"\nNumber of elements: {index.size}")
print(f"Data type: {index.dtype}")

Pandas Index...
Index([50, 10, 70, 110, 90, 50, 110, 90, 30], dtype='int64')

Number of elements: 9
Data type: int64

Getting Relative Frequency

Use value_counts(normalize=True) to get the relative frequency of each unique value ?

import pandas as pd

index = pd.Index([50, 10, 70, 110, 90, 50, 110, 90, 30])

# Get relative frequency (proportions)
relative_freq = index.value_counts(normalize=True)
print("Relative frequency:")
print(relative_freq)

# Compare with absolute counts
absolute_counts = index.value_counts(normalize=False)
print("\nAbsolute counts:")
print(absolute_counts)

Relative frequency:
50     0.222222
110    0.222222
90     0.222222
10     0.111111
70     0.111111
30     0.111111
Name: count, dtype: float64

Absolute counts:
50     2
110    2
90     2
10     1
70     1
30     1
Name: count, dtype: int64

How It Works

The relative frequency is calculated by dividing each count by the total number of elements. In our example:

• Values 50, 110, 90 appear 2 times each: 2/9 ? 0.222222
• Values 10, 70, 30 appear 1 time each: 1/9 ? 0.111111
• All frequencies sum to 1.0 (100%)

Conclusion

Use value_counts(normalize=True) to get relative frequencies as proportions instead of raw counts. This is useful for understanding the distribution of values in your Index as percentages of the total.