Ordered categories in Pandas - Mini Project: Build & Apply

Ordered categories

📖 Scenario: You work in a store that sells clothes. You have a list of T-shirt sizes from customers. The sizes are small, medium, and large. You want to organize these sizes in order so you can analyze them better.

🎯 Goal: Create a pandas Series with T-shirt sizes as an ordered category. Then, sort the sizes in the correct order: small, medium, large.

📋 What You'll Learn

Create a pandas Series called sizes with the values: 'medium', 'small', 'large', 'medium', 'small'.

Create an ordered categorical type called size_order with categories: 'small', 'medium', 'large' in this order.

Convert the sizes Series to use the ordered categorical type size_order.

Sort the sizes Series by the ordered categories.

Print the sorted sizes Series.

💡 Why This Matters

🌍 Real World

Stores often have product sizes or ratings that have a natural order. Using ordered categories helps sort and analyze such data correctly.

💼 Career

Data analysts and scientists use ordered categories to prepare and clean data for reports and machine learning models.

1

Create the sizes Series

Import pandas as pd. Create a pandas Series called sizes with these exact values in this order: 'medium', 'small', 'large', 'medium', 'small'.

Pandas

import pandas as pd
# Create the sizes Series with the given values
# Your code here

Need a hint?

Use pd.Series and pass the list of sizes exactly as given.

2

Create the ordered category type

Create an ordered categorical type called size_order using pd.CategoricalDtype with categories 'small', 'medium', 'large' in this order. Make sure it is ordered.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
# Create the ordered categorical type size_order
# Your code here

Need a hint?

Use pd.CategoricalDtype with the categories list and ordered=True.

3

Convert sizes to ordered category

Convert the sizes Series to use the ordered categorical type size_order by using astype(size_order) and assign it back to sizes.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
size_order = pd.CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True)
# Convert sizes to ordered categorical type
# Your code here

Need a hint?

Use sizes.astype(size_order) to convert the Series.

4

Sort and print the sizes

Sort the sizes Series using sort_values() and print the sorted Series.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
size_order = pd.CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True)
sizes = sizes.astype(size_order)
# Sort sizes and print the result
# Your code here

Need a hint?

Use sizes.sort_values() to sort and then print() to show the sorted Series.