Category codes and labels in Pandas

Introduction

Category codes and labels help us work with data that has fixed groups or categories. They make data smaller and faster to use.

When you have a column with repeated text values like colors or types.

When you want to save memory by storing categories as numbers instead of long text.

When you want to sort or compare categories easily.

When you want to see the numeric codes behind category labels for analysis.

When you want to change or rename category labels.

Syntax

Pandas

df['column'].cat.codes
# To get category labels:
df['column'].cat.categories

cat.codes gives the numeric codes for each category in the column.

cat.categories shows the list of category labels in order.

Examples

This converts a text column to categories and shows the numeric codes for each color.

Pandas

import pandas as pd

colors = pd.Series(['red', 'blue', 'green', 'blue', 'red'])
colors = colors.astype('category')
print(colors.cat.codes)

This shows the list of unique category labels in the column.

Pandas

print(colors.cat.categories)

This changes the order and names of the categories.

Pandas

colors.cat.categories = ['green', 'blue', 'red']
print(colors)

Sample Program

This program shows how to convert text data to categories, get numeric codes, see labels, and change labels.

Pandas

import pandas as pd

# Create a Series with repeated fruit names
fruits = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'apple'])

# Convert to category type
fruits = fruits.astype('category')

# Show the category codes (numbers)
print('Category codes:')
print(fruits.cat.codes)

# Show the category labels
print('\nCategory labels:')
print(fruits.cat.categories)

# Change category labels order
fruits.cat.categories = ['banana', 'orange', 'apple']

print('\nAfter changing category labels:')
print(fruits)

Important Notes

Category codes start at 0 and go up to the number of categories minus one.

Changing category labels changes how categories are ordered and named.

Using categories can save memory and speed up operations on repeated text data.

Summary

Categories store repeated text as numbers for efficiency.

Use cat.codes to see numeric codes and cat.categories for labels.

You can rename or reorder categories by assigning to cat.categories.

Practice

1. What does cat.codes show in a pandas categorical Series?

easy

A. The data type of the Series

B. The numeric codes representing each category

C. The count of each category

D. The text labels of the categories

Solution

1. Step 1: Understand what cat.codes returns

   cat.codes gives the integer codes for each category in the Series, replacing repeated text with numbers.

2. Step 2: Differentiate from cat.categories

   cat.categories shows the text labels, not the codes.

3. Final Answer:

   The numeric codes representing each category -> Option B

4. Quick Check:

   cat.codes = numeric codes [OK]

Hint: Remember: codes are numbers, categories are labels [OK]

Common Mistakes:

• Confusing codes with category labels
• Thinking codes show counts
• Assuming codes show data type

2. Which of the following is the correct way to access the category labels of a pandas categorical Series s?

easy

A. s.cat.categories

B. s.cat.codes

C. s.categories

D. s.cat.labels

Solution

1. Step 1: Recall the attribute for category labels

   The category labels are accessed via cat.categories on the Series.

2. Step 2: Check the options for correct syntax

   s.cat.categories is the correct syntax; s.categories is invalid, and cat.labels does not exist.

3. Final Answer:

   s.cat.categories -> Option A

4. Quick Check:

   Category labels = s.cat.categories [OK]

Hint: Use cat.categories to get labels, not just categories [OK]

Common Mistakes:

• Using s.categories instead of s.cat.categories
• Confusing cat.codes with labels
• Trying to use non-existent cat.labels

3. Given the code:

import pandas as pd
s = pd.Series(['apple', 'banana', 'apple', 'orange'], dtype='category')
print(s.cat.codes.tolist())

What is the output?

medium

A. [0, 1, 0, 2]

B. [1, 2, 1, 3]

C. [0, 0, 1, 2]

D. [1, 0, 1, 2]

Solution

1. Step 1: Identify categories and their order

   Categories are sorted alphabetically by default: ['apple', 'banana', 'orange'] with codes 0,1,2 respectively.

2. Step 2: Map each value to its code

   'apple' -> 0, 'banana' -> 1, 'apple' -> 0, 'orange' -> 2, so the list is [0, 1, 0, 2].

3. Final Answer:

   [0, 1, 0, 2] -> Option A

4. Quick Check:

   Alphabetical categories -> codes match [OK]

Hint: Categories sorted alphabetically by default [OK]

Common Mistakes:

• Assuming codes start at 1 instead of 0
• Ignoring alphabetical sorting of categories
• Mixing up category order

4. Which of the following is correct about this code snippet?

import pandas as pd
s = pd.Series(['cat', 'dog', 'cat'], dtype='category')
s.cat.categories = ['dog', 'cat']
print(s.cat.codes.tolist())

medium

A. You cannot assign new categories with different order directly

B. The categories list must be sorted alphabetically

C. You must convert Series to string before assigning categories

D. The code will run without errors and print [0, 1, 0]

Solution

1. Step 1: Understand category assignment rules

   Assigning to cat.categories redefines the labels for existing codes without changing the codes or raising errors, as long as new list has same length and unique values.

2. Step 2: Trace execution

   Original categories ['cat', 'dog'], codes [0,1,0]. New ['dog', 'cat'] same length/unique, codes unchanged, prints [0, 1, 0].

3. Final Answer:

   The code will run without errors and print [0, 1, 0] -> Option D

4. Quick Check:

   New categories redefine code labels, codes unchanged [OK]

Hint: Reassigning cat.categories changes labels but keeps codes intact [OK]

Common Mistakes:

• Thinking reordering causes ValueError
• Expecting codes to adjust to new order like [1, 0, 1]
• Assuming categories must stay alphabetical

5. You have a pandas categorical Series s with categories ['low', 'medium', 'high']. You want to rename the categories to ['L', 'M', 'H'] without changing the data. Which code correctly returns the renamed Series?

hard

A. s.cat.categories = ['L', 'M', 'H']

B. s.cat.categories.rename(['L', 'M', 'H'])

C. s.cat.rename_categories(['L', 'M', 'H'])

D. s.rename_categories(['L', 'M', 'H'])

Solution

1. Step 1: Understand how to rename categories

   The method cat.rename_categories() returns a new Series with renamed categories without changing data codes.

2. Step 2: Check other options for correctness

   Direct assignment to cat.categories works but can cause errors if not careful; cat.categories.rename() is invalid; rename_categories() is not a Series method.

3. Final Answer:

   s.cat.rename_categories(['L', 'M', 'H']) -> Option C

4. Quick Check:

   Use cat.rename_categories() to rename safely [OK]

Hint: Use cat.rename_categories() to rename categories safely [OK]

Common Mistakes:

• Trying to rename categories by invalid methods
• Directly assigning categories without matching length
• Confusing Series methods with cat accessor methods