Pandas rename_categories() Method

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The rename_categories() method allows you to rename categories in a pandas categorical objects, such as Pandas Categorical Series or CategoricalIndex with minimal effort. It supports multiple input types, such as lists, dictionaries, or callable. This method is recommended for renaming categorical data due to its speed, memory efficiency, and semantic benefits.

This is one of the specialized method available in the Pandas Series.cat accessor, an alias for CategoricalAccessor and can also be directly applied to CategoricalIndex. It is particularly useful for handling categorical data by renaming categories for clarity and consistency.

Syntax

Below you can see the syntax of the Python Pandas rename_categories() method, it differs slightly depending on whether you're working with a Categorical Series or a CategoricalIndex object.

Syntax for a Pandas Categorical Series −

Series.cat.rename_categories(new_categories, *args, **kwargs)

Syntax for a CategoricalIndex −

CategoricalIndex.rename_categories(new_categories, *args, **kwargs)

When using the rename_categories() method on a CategoricalIndex, there is no need to use the .cat accessor. This is because the CategoricalIndex is inherently categorical, and methods can be called directly.

Parameters

The Python Pandas rename_categories() method accepts the below parameters −

• new_categories − This parameter accepts three types of inputs for renaming categories. It supports three input types: a list-like object, a dictionary, or a callable. When using a list-like input, it specifies a list of new category names where the number of items must match the existing categories length, and all names must be unique. For a dict-like input, it maps old categories to new ones, with categories not included in the mapping remaining unchanged. Alternatively, you can provide a callable function, which dynamically generates new category names by applying the function to each existing category.

• **kwargs: Additional key word arguments for customization.

Return Value

The Pandas rename_categories() method returns a new categorical object with renamed categories.

Example: Renaming Categories with a List

Here is a basic example demonstrating renaming categories with a list. Using a list is the simplest way to rename all categories in a Pandas categorical object. For this you should ensure that list length must match the existing categories, and names must be unique.

import pandas as pd

# Creating a categorical Series
s = pd.Series(["a", "b", "c", "a"], dtype="category")
print("Original Series:\n", s)

# Renaming categories using a list
new_categories = ["Group A", "Group B", "Group C"]
s = s.cat.rename_categories(new_categories)
print("\nRenamed Categories:\n", s)

When we run above program, it produces following result −

Original Series:
 0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): ['a', 'b', 'c']

Renamed Categories:
 0    Group A
1    Group B
2    Group C
3    Group A
dtype: category
Categories (3, object): ['Group A', 'Group B', 'Group C']

Example: Renaming categories of a CategoricalIndex object

A CategoricalIndex can only take a limited, fixed set of values. You can use the rename_categories() method to rename categories within such an index. The following example demonstrates the same.

import pandas as pd

# Creating a CategoricalIndex
catIndex = pd.CategoricalIndex(["p", "q", "r", "s", "p", "q", "r", "s"],
ordered=True,
categories=["p", "q", "r", "s"]
)
print("CategoricalIndex...\n", catIndex)

# Get the categories
print("\nDisplaying Categories from CategoricalIndex...\n", catIndex.categories)

# Rename categories using a list
print("\nCategoricalIndex after renaming categories...\n", catIndex.rename_categories([5, 10, 15, 20]))

While executing the above code we get the following output −

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

Displaying Categories from CategoricalIndex...
 Index(['p', 'q', 'r', 's'], dtype='object')

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

Example: Renaming CategoricalIndex with a Dictionary

The following example demonstrates using the rename_categories() method for renaming the categories of a CategoricalIndex object with a dictionary.

import pandas as pd

# Creating a CategoricalIndex
catIndex = pd.CategoricalIndex(["p", "q", "r", "s", "p", "q", "r", "s"],
ordered=True,
categories=["p", "q", "r", "s"]
)
print("CategoricalIndex...\n", catIndex)

# Get the categories
print("\nDisplaying Categories from CategoricalIndex...\n", catIndex.categories)

# Rename categories using a dictionary
print("\nCategoricalIndex after renaming categories...\n", catIndex.rename_categories({'p': 5, 'q': 10, 'r': 15, 's': 20}))

Following is an output of the above code −

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

Displaying Categories from CategoricalIndex...
 Index(['p', 'q', 'r', 's'], dtype='object')

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

Example: Renaming Categories with a Callable

The rename_categories() method can also accepts a callable. This is useful for scenarios where category names need to follow a specific pattern, such as appending prefixes, converting case. The following example transforms all categories to uppercase.

import pandas as pd

# Creating a categorical Series
s = pd.Series(["cat", "dog", "fish", "cat"], dtype="category")
print("Original Series:\n", s)

# Renaming categories using a callable
s = s.cat.rename_categories(lambda x: x.upper())
print("\nRenamed Categories (to uppercase):\n", s)

Following is an output of the above code −

Original Series:
 0     cat
1     dog
2    fish
3     cat
dtype: category
Categories (3, object): ['cat', 'dog', 'fish']

Renamed Categories (to uppercase):
 0     CAT
1     DOG
2    FISH
3     CAT
dtype: category
Categories (3, object): ['CAT', 'DOG', 'FISH']