Managing Categorical Data in pandas: Using pandas.CategoricalIndex.remove_categories

CategoricalIndex and Index Objects in pandas

• Index Objects
  The underlying data structure for labeling rows or columns in pandas DataFrames and Series. Index objects can be of various types, including RangeIndex, Int64Index, and CategoricalIndex.
• CategoricalIndex
  A specialized Index type in pandas that treats data as categorical variables. It stores both the category labels and codes (integer representations) for efficient handling of categorical data.

pandas.CategoricalIndex.remove_categories

This method is specifically designed for CategoricalIndex objects and allows you to remove unwanted categories from the index. It takes the following arguments:

• removals (category or list of categories)
  The categories you want to eliminate from the index.

How it Works

1. Identification
   The method identifies the categories specified in the removals argument within the existing categories of the CategoricalIndex.
2. Filtering
   It filters out the identified categories from the internal representation of the index.
3. Code Adjustment
   If any data points in the DataFrame or Series (that uses this CategoricalIndex for indexing) belong to the removed categories, their category codes are adjusted to reflect the new set of categories. This ensures data integrity and avoids errors due to missing categories.
4. Returns
   The method returns a new CategoricalIndex object with the removed categories excluded.

Important Points

• Error Handling
  If a specified category is not present in the original index, a ValueError is raised.
• In-Place Modification
  pandas.CategoricalIndex.remove_categories does not modify the original CategoricalIndex in-place. It creates a new index with the filtered categories.

Example

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Red', 'Purple']
index = pd.CategoricalIndex(data)

# Remove 'Purple' from the categories
new_index = index.remove_categories('Purple')

print(index)
print(new_index)

This code will output:

Index(['Red', 'Green', 'Blue', 'Red', 'Purple'], dtype='category', categories=['Red', 'Green', 'Blue', 'Purple'])
Index(['Red', 'Green', 'Blue', 'Red'], dtype='category', categories=['Red', 'Green', 'Blue'])

As you can see, the original index (index) remains unchanged, while the new index (new_index) has "Purple" removed from its categories.

---

Removing Multiple Categories

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Yellow', 'Red', 'Orange']
index = pd.CategoricalIndex(data)

# Remove 'Yellow' and 'Orange'
new_index = index.remove_categories(['Yellow', 'Orange'])

print(index)
print(new_index)

This code removes both "Yellow" and "Orange" from the categories, resulting in an index with only "Red", "Green", and "Blue".

Handling Non-Existent Categories

import pandas as pd

data = ['Red', 'Green', 'Blue']
index = pd.CategoricalIndex(data)

# Try to remove a non-existent category
try:
  new_index = index.remove_categories('Purple')
except ValueError as e:
  print(f"Error: {e}")

This code attempts to remove "Purple", which isn't present in the original index. It will raise a ValueError indicating that the category to be removed doesn't exist.

Removing Unused Categories

While remove_categories removes specified categories, pandas.CategoricalIndex.remove_unused_categories specifically removes categories that are not used in any data points.

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Red', 'Red']  # 'Green' is unused
index = pd.CategoricalIndex(data)

# Remove unused categories
new_index = index.remove_unused_categories()

print(index)
print(new_index)

This code removes "Green" because it's not used in any data point. The resulting index only has "Red" and "Blue".

Using remove_categories with a Series

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Red', 'Purple']
s = pd.Series(data)

# Remove 'Purple' from the underlying CategoricalIndex
new_s = s.remove_categories('Purple')

print(s.cat.categories)
print(new_s.cat.categories)

This code demonstrates using remove_categories with a Series that has a CategoricalIndex. It removes "Purple" from the categories of the underlying index, reflected in both the original and modified Series' categories.

---

Filtering with Conditions

If you have a clear condition for identifying the categories you want to exclude, you can use boolean indexing or filtering to create a new CategoricalIndex.

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Yellow', 'Red', 'Orange']
index = pd.CategoricalIndex(data)

# Filter out categories starting with 'O'
colors_to_keep = ~index.categories.str.startswith('O')
new_index = index[colors_to_keep]

print(index)
print(new_index)

This code filters the original index (index) to keep only categories that don't start with "O" (excluding "Orange").

Assigning New Categories

For more granular control, you can directly assign new categories to data points using conditional logic or vectorized operations.

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Yellow', 'Red', 'Orange']
index = pd.CategoricalIndex(data)

# Replace 'Yellow' and 'Orange' with a new category 'Other'
new_data = index.copy()
new_data[index.categories.isin(['Yellow', 'Orange'])] = 'Other'

print(index)
print(pd.CategoricalIndex(new_data))

This code creates a copy of the index (new_data) and assigns the category "Other" to data points originally labeled as "Yellow" or "Orange". The result is a new CategoricalIndex with the modified categories.

Using recode (if applicable)

If you're using pandas version 1.5.0 or later, the recode method provides a more concise way to map specific categories to new values.

import pandas as pd

data = ['Red', 'Green', 'Blue', 'Yellow', 'Red', 'Orange']
index = pd.CategoricalIndex(data)

# Map 'Yellow' and 'Orange' to a new category 'Other'
new_index = index.recode({'Yellow': 'Other', 'Orange': 'Other'})

print(index)
print(new_index)

This code (assuming pandas version 1.5.0 or later) utilizes recode to map "Yellow" and "Orange" to the new category "Other", resulting in a new CategoricalIndex with the recoded categories.

Choosing the Right Alternative

The best alternative depends on your specific use case and data structure:

• recode (pandas 1.5.0+)
  This method offers a concise way to map specific categories to new values (available in newer pandas versions).
• Assigning New Categories
  Opt for this approach if you need to reassign categories based on specific data points or conditions.
• Filtering with Conditions
  Use this if you have a clear filtering condition for the categories to exclude.