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

Need a 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

Need a 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

Need a 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

Need a hint?

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