Python Pandas CategoricalIndex - Rename categories with dict-like new categories

To rename categories with dict-like new categories, use the CategoricalIndex rename_categories() method in Pandas. This method allows you to map old category names to new ones using a dictionary.

What is CategoricalIndex?

CategoricalIndex can only take on a limited, and usually fixed, number of possible values. It's useful for representing data with a finite set of categories.

Creating a CategoricalIndex

First, let's create a CategoricalIndex with some sample data ?

import pandas as pd

# Create CategoricalIndex with ordered categories
catIndex = pd.CategoricalIndex(["p", "q", "r", "s", "p", "q", "r", "s"], 
                              ordered=True, 
                              categories=["p", "q", "r", "s"])

print("Original CategoricalIndex:")
print(catIndex)
print("\nCategories:")
print(catIndex.categories)

Original CategoricalIndex:
CategoricalIndex(['p', 'q', 'r', 's', 'p', 'q', 'r', 's'], categories=['p', 'q', 'r', 's'], ordered=True, dtype='category')

Categories:
Index(['p', 'q', 'r', 's'], dtype='object')

Renaming Categories with Dictionary

Use rename_categories() with a dictionary to map old category names to new ones ?

import pandas as pd

catIndex = pd.CategoricalIndex(["p", "q", "r", "s", "p", "q", "r", "s"], 
                              ordered=True, 
                              categories=["p", "q", "r", "s"])

# Rename categories using dictionary mapping
renamed_catIndex = catIndex.rename_categories({'p': 5, 'q': 10, 'r': 15, 's': 20})

print("CategoricalIndex after renaming categories:")
print(renamed_catIndex)
print("\nNew categories:")
print(renamed_catIndex.categories)

CategoricalIndex after renaming categories:
CategoricalIndex([5, 10, 15, 20, 5, 10, 15, 20], categories=[5, 10, 15, 20], ordered=True, dtype='category')

New categories:
Index([5, 10, 15, 20], dtype='int64')

Practical Example

Here's a more practical example renaming size categories ?

import pandas as pd

# Create size categories
sizes = pd.CategoricalIndex(["S", "M", "L", "XL", "S", "M"], 
                           categories=["S", "M", "L", "XL"])

print("Original sizes:")
print(sizes)

# Rename to full words
full_names = sizes.rename_categories({
    'S': 'Small',
    'M': 'Medium', 
    'L': 'Large',
    'XL': 'Extra Large'
})

print("\nRenamed sizes:")
print(full_names)

Original sizes:
CategoricalIndex(['S', 'M', 'L', 'XL', 'S', 'M'], categories=['S', 'M', 'L', 'XL'], ordered=False, dtype='category')

Renamed sizes:
CategoricalIndex(['Small', 'Medium', 'Large', 'Extra Large', 'Small', 'Medium'], categories=['Small', 'Medium', 'Large', 'Extra Large'], ordered=False, dtype='category')

Conclusion

The rename_categories() method with dictionary mapping provides a clean way to rename categorical values. It maintains the structure and ordering while updating category names to more meaningful labels.