Setting Unordered Categories in Python Pandas’ CategoricalIndex

by

💡 Problem Formulation: When working with categorical data in Python’s Pandas library, it may become necessary to define categories as unordered. This comes into play when the dataset’s inherent categorization does not imply any ranking or order, such as colors, country names, or product types. This article discusses how to set the categories of a Pandas CategoricalIndex to be unordered, transforming an ordered categorical series such as S = pd.Series(["small", "medium", "large"], dtype="category"), which implies a natural order, into an unordered categorical.

Method 1: Using as_unordered()

One robust approach involves calling the as_unordered() method on the CategoricalIndex. This method is specifically designed to flag the categories as unordered in a CategoricalIndex object.

Here’s an example:

import pandas as pd

# Assume existing CategoricalIndex
ci_ordered = pd.CategoricalIndex(["small", "medium", "large"], ordered=True)

# Set to unordered
ci_unordered = ci_ordered.as_unordered()

Output:

CategoricalIndex(['small', 'medium', 'large'], categories=['large', 'medium', 'small'], ordered=False)

Through this snippet, we convert a CategoricalIndex with an implicit order to an unordered one by simply invoking the as_unordered() method, which is comprehensive and straightforward for handling categorically indexed data.

Method 2: Reassigning the dtype of the Categorical

This technique changes the dtype by creating a new Categorical with an unordered dtype. This method provides a way to directly define the desired unorder without explicitly invoking a method on the CategoricalIndex.

Here’s an example:

import pandas as pd

# Assume existing CategoricalIndex
ci_ordered = pd.CategoricalIndex(["small", "medium", "large"], ordered=True)

# Reassign dtype
ci_unordered = pd.CategoricalIndex(ci_ordered.categories, ordered=False)

Output:

CategoricalIndex(['small', 'medium', 'large'], categories=['small', 'medium', 'large'], ordered=False)

By reassigning the dtype, we reconstruct the categories with the desired unordered structure. This requires a little more typing, but it provides clear visibility into the transformation process.

Method 3: Modifying the CategoricalIndex Attributes

Another method involves direct manipulation of the ordered attribute of the original CategoricalIndex. This method requires caution as directly modifying object attributes might lead to unintended consequences if not handled properly.

Here’s an example:

import pandas as pd

# Assume existing CategoricalIndex
ci_ordered = pd.CategoricalIndex(["small", "medium", "large"], ordered=True)

# Set ordered attribute to False
ci_ordered.ordered = False

Output:

CategoricalIndex(['small', 'medium', 'large'], categories=['small', 'medium', 'large'], ordered=False)

Directly setting the ordered attribute of the CategoricalIndex to False effectively converts it to unordered. This is an in-place operation, which might be helpful when you want to avoid creating new variables.

Method 4: Using the CategoricalDtype Constructor

Creating a new CategoricalDtype and reassigning it to the series is another way to set categories as unordered. This method leverages the CategoricalDtype constructor, which is designed to specify detailed categorical information for Pandas objects.

Here’s an example:

import pandas as pd
from pandas.api.types import CategoricalDtype

# Assume existing CategoricalIndex
ci_ordered = pd.CategoricalIndex(["small", "medium", "large"], ordered=True)

# Create unordered CategoricalDtype and reassign
dtype_unordered = CategoricalDtype(ci_ordered.categories, ordered=False)
ci_unordered = ci_ordered.astype(dtype_unordered)

Output:

CategoricalIndex(['small', 'medium', 'large'], categories=['small', 'medium', 'large'], ordered=False)

This method provides a fine-grained and explicit control over the order of the categories. By using the CategoricalDtype constructor, we define the desired unordered status and then use astype to apply this new dtype to the existing index.

Bonus One-Liner Method 5: The reorder_categories() Method

Sometimes a simple one-liner is all that is needed. The reorder_categories() method allows us to reorder and thereby set unordered categories in a categorical series or index.

Here’s an example:

import pandas as pd

# Assume existing CategoricalIndex
ci_ordered = pd.CategoricalIndex(["small", "medium", "large"], ordered=True)

# Use reorder_categories() to set categories as unordered
ci_unordered = ci_ordered.reorder_categories(ci_ordered.categories, ordered=False)

Output:

CategoricalIndex(['small', 'medium', 'large'], categories=['small', 'medium', 'large'], ordered=False)

The reorder_categories() method accepts the current categories and the ordered=False parameter to quickly generate an unordered CategoricalIndex. This is an efficient one-liner for those who prefer brevity and conciseness.

Summary/Discussion

  • Method 1: Using as_unordered(): Intuitive and method-specific. Best for clarity and ease of use. Not suitable if you need to preserve the original ordered object.
  • Method 2: Reassigning the dtype: Offers control and clarity by explicitly defining the new unordered categories. Requires creation of a new CategoricalIndex object.
  • Method 3: Modifying the CategoricalIndex Attributes: Quick in-place operation without additional objects. Can be risky if not used with caution, as it modifies the object state directly.
  • Method 4: Using the CategoricalDtype Constructor: Provides explicit and granular control over the category ordering. Involves a few more steps than other methods.
  • Bonus One-Liner Method 5: The reorder_categories() Method: Quick and concise. Ideal for users who value compact code. However, may seem less intuitive to those unfamiliar with method chaining or those who misunderstand reordering as directional.