5 Effective Ways to Remove Specified Categories from CategoricalIndex in Python Pandas

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💡 Problem Formulation: When working with data in Pandas, you might encounter a CategoricalIndex that carries multiple categories. Suppose you have a DataFrame with a categorical column that includes categories such as ‘apple’, ‘banana’, and ‘cherry’. If you desire to remove ‘banana’ from this CategoricalIndex, you’ll need a method to do so while maintaining the integrity of the DataFrame. Below, we explore various methods on how to selectively remove categories from a CategoricalIndex in Pandas.

Method 1: Using remove_categories()

The remove_categories() method is explicitly designed to remove specified categories from a CategoricalIndex. This operation returns a new CategoricalIndex with the specified categories removed, without touching any associated data that does not match the removed categories.

Here’s an example:

import pandas as pd

ci = pd.CategoricalIndex(['apple', 'banana', 'cherry', 'banana'])
new_ci = ci.remove_categories('banana')
print(new_ci)

Output:

CategoricalIndex(['apple', 'cherry'], categories=['apple', 'cherry'], ordered=False, dtype='category')

In this snippet, we created a CategoricalIndex with four items, including ‘banana’ categories that we want to remove. We called the remove_categories() method on the CategoricalIndex to create a new index where ‘banana’ has been removed. The resulting CategoricalIndex no longer includes ‘banana’ as a category.

Method 2: Using drop() with levels

The drop() method, used in conjunction with the levels attribute, can effectively remove categories from a MultiIndex, which behaves like a CategoricalIndex with hierarchies. By specifying the level and labels to drop, this method provides an efficient way to manipulate the MultiIndex categories.

Here’s an example:

import pandas as pd

multi_idx = pd.MultiIndex.from_arrays([['apple', 'banana', 'cherry'], ['fruit'] * 3], names=('category', 'type'))
filtered_idx = multi_idx.drop('banana', level='category')
print(filtered_idx)

Output:

MultiIndex([('apple', 'fruit'),
            ('cherry', 'fruit')],
           names=['category', 'type'])

We begin with a MultiIndex of categories and types including ‘banana’. Utilizing the drop() method, ‘banana’ is specified to be removed at the ‘category’ level. Consequently, the new MultiIndex excludes ‘banana’ while retaining the hierarchical structure.

Method 3: Using Boolean Indexing

Boolean indexing in Pandas can be an effective and intuitive method to filter out unwanted categories. By creating a boolean array that represents the condition of categories to be excluded, you can filter the original CategoricalIndex to receive a new one with only the desired categories.

Here’s an example:

import pandas as pd

ci = pd.CategoricalIndex(['apple', 'banana', 'cherry', 'banana'])
filter = ci != 'banana'
new_ci = ci[filter]
print(new_ci)

Output:

CategoricalIndex(['apple', 'cherry'], categories=['apple', 'banana', 'cherry'], ordered=False, dtype='category')

In the example, ci != 'banana' creates a boolean array where each element is True if it’s not ‘banana’. We then apply this filter to the original CategoricalIndex resulting in a new one that excludes ‘banana’. Note that the original categories are preserved but filtered out in the new index.

Method 4: Using cat.set_categories()

The cat.set_categories() method allows for setting new categories to a CategoricalIndex, replacing the old ones. It can be used to both add and remove categories. By specifying which categories we would like to keep, we can effectively remove unwanted categories.

Here’s an example:

import pandas as pd

ci = pd.CategoricalIndex(['apple', 'banana', 'cherry', 'banana'])
new_categories = ['apple', 'cherry']  # Categories we want to keep
new_ci = ci.set_categories(new_categories)
print(new_ci)

Output:

CategoricalIndex(['apple', 'cherry'], categories=['apple', 'cherry'], ordered=False, dtype='category')

In this code fragment, the set_categories() method is applied to the original CategoricalIndex ci to define a new set of desired categories, specifically excluding ‘banana’. The resultant CategoricalIndex reflects this desired subset of categories.

Bonus One-Liner Method 5: Using List Comprehension

For those who prefer a Pythonic approach, a one-line list comprehension can also be employed to seamlessly filter out specified categories from a CategoricalIndex. This method is both concise and expressive.

Here’s an example:

import pandas as pd

ci = pd.CategoricalIndex(['apple', 'banana', 'cherry', 'banana'])
new_ci = pd.CategoricalIndex([cat for cat in ci if cat != 'banana'])
print(new_ci)

Output:

CategoricalIndex(['apple', 'cherry'], categories=['apple', 'banana', 'cherry'], ordered=False, dtype='category')

This example leverages a list comprehension to iterate through the CategoricalIndex and include only those elements that are not ‘banana’. The resulting list passes into the construction of a new CategoricalIndex.

Summary/Discussion

  • Method 1: remove_categories(). Direct and purpose-built. However, it may not be as intuitive for users unfamiliar with the CategoricalIndex methods.
  • Method 2: drop() with levels. Powerful for MultiIndex data structures. Can be slightly more verbose and requires understanding of multi-level indexes.
  • Method 3: Boolean Indexing. Intuitive and aligns with general Pandas filtering techniques. Preserves the original categories, which may or may not be desirable.
  • Method 4: cat.set_categories(). Versatile for changing the full set of categories. It requires manually specifying all desired categories.
  • Method 5: List Comprehension. Pythonic and concise. However, this method might not be as efficient for large datasets.