Categorical data type optimization in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of memory usage after converting to categorical

What is the output of the following Python code that optimizes a DataFrame column by converting it to a categorical type?

Data Analysis Python

import pandas as pd
import numpy as np

# Create a DataFrame with 100000 rows
np.random.seed(0)
data = pd.DataFrame({
    'color': np.random.choice(['red', 'green', 'blue'], size=100000)
})

# Memory usage before conversion
mem_before = data.memory_usage(deep=True).sum()

# Convert 'color' column to categorical
data['color'] = data['color'].astype('category')

# Memory usage after conversion
mem_after = data.memory_usage(deep=True).sum()

print(round(mem_before), round(mem_after))

A 240000 240000

B 240000 80000

C 300000 300000

D 80000 240000

💡 Hint

Think about how categorical data stores repeated values efficiently compared to strings.

Converting a string column to categorical reduces memory usage significantly because it stores unique values once and uses integer codes for each row.

❓ data_output

intermediate

Number of unique categories after optimization

Given a DataFrame with a 'city' column converted to categorical, what is the number of unique categories stored?

Data Analysis Python

import pandas as pd

cities = ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'New York', 'Chicago']
data = pd.DataFrame({'city': cities})
data['city'] = data['city'].astype('category')

num_categories = len(data['city'].cat.categories)
print(num_categories)

A 4

B 7

C 6

D 5

💡 Hint

Count unique city names ignoring duplicates.

The unique cities are New York, Los Angeles, Chicago, Houston, and Phoenix, totaling 5 categories.

🔧 Debug

advanced

Identify the error in categorical conversion code

What error does the following code produce?

Data Analysis Python

import pandas as pd

values = ['apple', 'banana', 'apple', 'orange']
data = pd.DataFrame({'fruit': values})
data['fruit'] = data['fruit'].astype('category', categories=['apple', 'banana'])

A TypeError: astype() got an unexpected keyword argument 'categories'

B ValueError: Cannot set categories with astype()

C KeyError: 'categories'

D No error, runs successfully

💡 Hint

Check the correct way to specify categories when converting to categorical.

The astype() method does not accept a 'categories' argument. Categories must be set using pd.Categorical or cat.set_categories().

🚀 Application

advanced

Choosing categorical dtype for large dataset

You have a dataset with a column 'status' containing 3 unique values repeated millions of times. Which approach optimizes memory best?

A Keep 'status' as object dtype (string)

B Convert 'status' to integer dtype without mapping

C Convert 'status' to category dtype

D Convert 'status' to float dtype

💡 Hint

Think about how categorical dtype stores repeated values efficiently.

Category dtype stores unique values once and uses integer codes internally, saving memory for repeated values.

🧠 Conceptual

expert

Effect of categorical ordering on sorting performance

How does setting the ordered=True parameter in a categorical column affect sorting performance in pandas?

A Sorting is faster because pandas uses the category order for comparisons

B Sorting is slower because ordered categories require extra checks

C Sorting raises an error if categories are ordered

D Sorting performance is unchanged by ordered parameter

💡 Hint

Consider how ordered categories allow direct integer comparison during sorting.

When categories are ordered, pandas can sort by comparing integer codes directly, which is faster than sorting strings.