Category codes and labels in Pandas - Mini Project: Build & Apply

Category codes and labels

📖 Scenario: You work in a small store that tracks product categories. Each product belongs to a category like 'Fruit', 'Vegetable', or 'Dairy'. You want to organize these categories using codes to make analysis easier.

🎯 Goal: Create a pandas DataFrame with product names and categories. Then convert the category column to a categorical type. Finally, extract and print the category codes and labels.

📋 What You'll Learn

Create a pandas DataFrame called products with columns 'Product' and 'Category' using exact data.

Create a categorical version of the 'Category' column called cat_categories.

Extract the category codes into a variable called category_codes.

Extract the category labels into a variable called category_labels.

Print category_codes and category_labels.

💡 Why This Matters

🌍 Real World

Stores and businesses often use category codes to simplify data storage and speed up analysis when working with product categories.

💼 Career

Data analysts and scientists use categorical data types to optimize memory and perform faster grouping and filtering in real datasets.

1

Create the products DataFrame

Create a pandas DataFrame called products with columns 'Product' and 'Category'. Use these exact entries: 'Apple' in 'Fruit', 'Carrot' in 'Vegetable', 'Milk' in 'Dairy', 'Banana' in 'Fruit', and 'Cheese' in 'Dairy'.

Pandas

import pandas as pd
# Create the products DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of entries.

2

Convert Category column to categorical

Create a categorical version of the 'Category' column from products and assign it to a variable called cat_categories.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Milk', 'Banana', 'Cheese'],
    'Category': ['Fruit', 'Vegetable', 'Dairy', 'Fruit', 'Dairy']
})
# Convert the 'Category' column to categorical and assign to cat_categories
# Your code here

Hint

Use astype('category') on the 'Category' column.

3

Extract category codes and labels

Extract the category codes from cat_categories into a variable called category_codes. Extract the category labels from cat_categories into a variable called category_labels.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Milk', 'Banana', 'Cheese'],
    'Category': ['Fruit', 'Vegetable', 'Dairy', 'Fruit', 'Dairy']
})
cat_categories = products['Category'].astype('category')
# Extract category codes into category_codes
# Extract category labels into category_labels
# Your code here

Hint

Use .cat.codes for codes and .cat.categories for labels.

4

Print category codes and labels

Print the variables category_codes and category_labels to display the category codes and labels.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Carrot', 'Milk', 'Banana', 'Cheese'],
    'Category': ['Fruit', 'Vegetable', 'Dairy', 'Fruit', 'Dairy']
})
cat_categories = products['Category'].astype('category')
category_codes = cat_categories.cat.codes
category_labels = cat_categories.cat.categories
# Print category_codes and category_labels
# Your code here

Hint

Use two print() statements, one for category_codes and one for category_labels.

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