Converting to categorical in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of converting a column to categorical

What is the output of this code snippet that converts a DataFrame column to categorical and then prints the categories?

Pandas

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue', 'green', 'blue', 'red']})
df['color'] = df['color'].astype('category')
print(df['color'].cat.categories.tolist())

A ['blue', 'red', 'green']

B ['red', 'blue', 'green']

C ['blue', 'green', 'red']

D ['red', 'green', 'blue']

💡 Hint

Categories are sorted by default in pandas categorical dtype.

When converting a column to categorical, pandas sorts the unique values alphabetically by default. So the categories are ['blue', 'green', 'red'].

❓ data_output

intermediate

Number of categories after conversion

After converting the 'grade' column to categorical, how many categories does it have?

Pandas

import pandas as pd

df = pd.DataFrame({'grade': ['A', 'B', 'A', 'C', 'B', 'A']})
df['grade'] = df['grade'].astype('category')
print(len(df['grade'].cat.categories))

A 3

B 6

C 4

D 2

💡 Hint

Count unique values in the column before conversion.

The unique grades are 'A', 'B', and 'C', so there are 3 categories after conversion.

🔧 Debug

advanced

Identify the error in categorical conversion

What error does this code raise when trying to convert a column to categorical with specified categories?

Pandas

import pandas as pd

df = pd.DataFrame({'size': ['S', 'M', 'L', 'XL']})
df['size'] = pd.Categorical(df['size'], categories=['S', 'M', 'L'])

A TypeError: categories must be a list

B No error, conversion succeeds with 'XL' as NaN category

C ValueError: 'XL' not in categories

D KeyError: 'size'

💡 Hint

Check how pandas handles values not in specified categories.

Pandas converts values not in the specified categories to NaN without raising an error.

🚀 Application

advanced

Effect of ordered categorical on sorting

Given this DataFrame with an ordered categorical column, what is the output after sorting by 'priority'?

Pandas

import pandas as pd

df = pd.DataFrame({'task': ['task1', 'task2', 'task3'], 'priority': ['high', 'low', 'medium']})
priority_type = pd.CategoricalDtype(categories=['low', 'medium', 'high'], ordered=True)
df['priority'] = df['priority'].astype(priority_type)
sorted_df = df.sort_values('priority')
print(sorted_df['task'].tolist())

A ['task1', 'task3', 'task2']

B ['task2', 'task1', 'task3']

C ['task3', 'task2', 'task1']

D ['task2', 'task3', 'task1']

💡 Hint

Ordered categorical sorts according to category order, not alphabetically.

The order is low < medium < high, so sorting by 'priority' puts 'low' first (task2), then 'medium' (task3), then 'high' (task1).

🧠 Conceptual

expert

Memory usage difference with categorical dtype

Which statement best describes the memory usage difference when converting a large text column to categorical dtype in pandas?

A Memory usage decreases because pandas stores categories as integers internally

B Memory usage increases because categories add overhead for mapping

C Memory usage stays the same because data is unchanged

D Memory usage decreases only if categories are sorted alphabetically

💡 Hint

Think about how categorical data is stored compared to strings.

Categorical dtype stores data as integer codes with a separate category list, reducing memory compared to storing repeated strings.