5 Best Ways to Reorder Categories in Python Pandas CategoricalIndex

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💡 Problem Formulation: When working with categorical data in pandas, you might encounter the need to reorder the categories within a CategoricalIndex for analysis or visualization purposes. For example, when you have a CategoricalIndex of days of the week, you might want to reorder them from Monday to Sunday rather than in alphabetical order. This article explains how to achieve such reordering effectively.

Method 1: Using the reorder_categories() Method

This method is the standard approach for reordering categories in pandas. The reorder_categories() function is explicitly designed for this purpose, allowing you to specify the new order of categories as a list. It ensures that the data integrity is maintained by not introducing or removing any category.

Here’s an example:

import pandas as pd

# Example categorical series
cat_series = pd.Categorical(['Monday', 'Wednesday', 'Friday'], 
                            categories=['Monday', 'Wednesday', 'Friday'], 
                            ordered=True)

# Reorder the categories
cat_series = cat_series.reorder_categories(['Monday', 'Friday', 'Wednesday'])

print(cat_series)

Output: [‘Monday’, ‘Wednesday’, ‘Friday’] Categories (3, object): [‘Monday’, ‘Friday’, ‘Wednesday’]

The code snippet reorders the days of the week, putting ‘Monday’ first, followed by ‘Friday’, and then ‘Wednesday’. The category ‘Friday’ has been moved up, and ‘Wednesday’ has been moved down in the ordering.

Method 2: Using the set_categories() Method

The set_categories() method can be used for both setting and reordering categories. It can replace the current categories with new ones and is useful when you want to add or remove categories as well as reorder them.

Here’s an example:

cat_series = cat_series.set_categories(['Monday', 'Friday', 'Wednesday', 'Saturday']) 

print(cat_series)

Output: [‘Monday’, ‘Wednesday’, ‘Friday’] Categories (4, object): [‘Monday’, ‘Friday’, ‘Wednesday’, ‘Saturday’]

This example demonstrates how to extend the existing categories by adding ‘Saturday’ and simultaneously reorder the categories according to the specified list. Note that the actual data doesn’t change, but the potential categories have been updated.

Method 3: Using the Categorical() Constructor

The Categorical() constructor can be used to create a new categorical object from the original one with reordered categories. This approach provides flexibility with data types and category ordering.

Here’s an example:

cat_reorder = pd.Categorical(cat_series, categories=['Monday', 'Friday', 'Wednesday', 'Thursday'], ordered=True)

print(cat_reorder)

Output: [‘Monday’, ‘Wednesday’, ‘Friday’] Categories (4, object): [‘Monday’, ‘Friday’, ‘Wednesday’, ‘Thursday’]

Here, a new categorical series has been created with an additional category ‘Thursday’ and the categories have been reordered. The Categorical() constructor can be helpful when dealing with the need to create a new categorical object alongside reordering.

Method 4: Using the sort_values() Method

The sort_values() method can be used when the reordering can be based on the sorting of values. This method rearranges the categories according to their sorted order, either in ascending or descending.

Here’s an example:

cat_series = cat_series.sort_values()

print(cat_series)

Output: [‘Friday’, ‘Monday’, ‘Wednesday’]

This code snippet sorts the categories by their values in alphabetical order. It can be particularly useful when the desired ordering coincides with the sorting order, such as alphabetical or numerical order.

Bonus One-Liner Method 5: Using List Comprehension

For simple reordering tasks, a one-liner list comprehension can be employed. This approach can be very succinct but less explicit, therefore can be less readable for complex operations.

Here’s an example:

new_order = ['Monday', 'Friday', 'Wednesday']
cat_series = pd.Categorical([day for day in new_order if day in cat_series], ordered=True)

print(cat_series)

Output: [‘Monday’, ‘Friday’, ‘Wednesday’]

The example illustrates a concise one-liner that reorders the categories based on their occurrence in a predefined list. This method should be used with caution, as it can inadvertently remove categories that are not included in the new order list.

Summary/Discussion

  • Method 1: Using reorder_categories(). Strengths: Specific to reordering without changing the data set. Weaknesses: Cannot add or remove categories.
  • Method 2: Using set_categories(). Strengths: Can add or remove categories while reordering. Weaknesses: May introduce unwanted categories or remove needed ones if not used carefully.
  • Method 3: Using the Categorical() constructor. Strengths: Offers great flexibility; can handle data type conversions. Weaknesses: Requires creation of a new object, which may be less efficient.
  • Method 4: Using sort_values(). Strengths: Quick and easy when sorting aligns with the desired order. Weaknesses: Limited to sorting-based reordering.
  • Bonus Method 5: Using a list comprehension. Strengths: Very concise and Pythonic. Weaknesses: Can be less readable and may accidentally filter out categories.