Ordered categories in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of ordered categorical sorting

What is the output of this code snippet?

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
s_sorted = s.sort_values()
print(s_sorted.tolist())

Pandas

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
s_sorted = s.sort_values()
print(s_sorted.tolist())

A ['high', 'medium', 'medium', 'low', 'low']

B ['low', 'low', 'medium', 'medium', 'high']

C ['low', 'medium', 'high', 'medium', 'low']

D ['medium', 'medium', 'low', 'low', 'high']

💡 Hint

Remember that ordered categories sort according to the order defined in categories.

The series is sorted by the order defined in the categorical: 'low' < 'medium' < 'high'. So the sorted list places all 'low' first, then 'medium', then 'high'.

❓ data_output

intermediate

Number of elements less than 'medium' in ordered category

Given this ordered categorical series, how many elements are less than 'medium'?

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
count = (s < 'medium').sum()
print(count)

Pandas

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
count = (s < 'medium').sum()
print(count)

A 0

B 3

C 1

D 2

💡 Hint

Check which values are strictly less than 'medium' in the order.

Only 'low' is less than 'medium'. There are two 'low' values, so the count is 2.

🔧 Debug

advanced

Identify the error in creating ordered categories

What error does this code raise?

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium'], ordered=True)

Pandas

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium', 'low'], categories=['low', 'medium'], ordered=True)

A ValueError: 'high' is not in categories

B ValueError: items in categories are not in the data

C No error, code runs fine

D TypeError: categories must be a list

💡 Hint

Check if all values in the data are included in the categories list.

The data contains 'high' but the categories list does not include 'high'. This causes a ValueError indicating 'high' is not in categories.

🚀 Application

advanced

Using ordered categories to compare values

Which option correctly creates an ordered categorical series and checks if each value is greater than or equal to 'medium'?

A

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high'], categories=['high', 'medium', 'low'], ordered=True)
s = pd.Series(cats)
result = s >= 'medium'
print(result.tolist())

B

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
result = s > 'medium'
print(result.tolist())

C

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high'], categories=['low', 'medium', 'high'], ordered=True)
s = pd.Series(cats)
result = s >= 'medium'
print(result.tolist())

D

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high'], categories=['low', 'medium', 'high'])
s = pd.Series(cats)
result = s >= 'medium'
print(result.tolist())

💡 Hint

Ordered categories require the categories list to be in the correct order and ordered=True.

Option C correctly defines categories in ascending order with ordered=True. It then compares each value to 'medium' producing a boolean list. Option C misses categories argument, so order is inferred but may not be correct. Option C reverses category order, so comparison logic is wrong. Option C lacks ordered=True, so comparison operators raise an error.

🧠 Conceptual

expert

Why use ordered categories in data analysis?

Which is the best reason to use ordered categories in a dataset?

A To enable meaningful comparison and sorting of categorical data with a logical order

B To convert categorical data into numerical data for machine learning models

C To allow missing values to be automatically filled with the most frequent category

D To save memory by storing data as integers internally

💡 Hint

Think about what ordered categories add beyond normal categories.

Ordered categories allow comparisons like <, >, and sorting based on a logical order defined by the user. This is useful for data like ratings or sizes. While ordered categories do save memory (A), that is true for all categorical types, not just ordered ones. Filling missing values (C) and converting to numerical (B) are separate processes.