Adding and removing categories in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding a new category to a pandas Categorical

What is the output of this code snippet?

Pandas

import pandas as pd
cat = pd.Categorical(['apple', 'banana'], categories=['apple', 'banana'])
cat = cat.add_categories(['cherry'])
print(cat.categories.tolist())

A ['apple', 'banana', 'cherry']

B ['apple', 'banana']

C ['cherry']

D ['apple', 'banana', 'cherry', 'date']

💡 Hint

Think about what add_categories does to the categories list.

The add_categories method adds new categories to the existing categories without changing the current values. So the categories list will include the original plus the new one.

❓ data_output

intermediate

Result of removing a category from a pandas Categorical

What is the output of this code?

Pandas

import pandas as pd
cat = pd.Categorical(['apple', 'banana', 'cherry'], categories=['apple', 'banana', 'cherry'])
cat = cat.remove_categories(['banana'])
print(cat.categories.tolist())

A ['apple', 'cherry']

B ['apple', 'banana']

C ['banana']

D ['apple', 'banana', 'cherry']

💡 Hint

Removing a category deletes it from the categories list.

The remove_categories method removes the specified categories from the categories list. So 'banana' is removed.

🔧 Debug

advanced

Behavior when removing a non-existent category

What happens when running this code?

Pandas

import pandas as pd
cat = pd.Categorical(['apple', 'banana'], categories=['apple', 'banana'])
cat = cat.remove_categories(['cherry'])

A ValueError because 'cherry' is not in the categories

B TypeError because remove_categories expects a string, not a list

C No error, code runs fine

D KeyError because 'cherry' is missing

💡 Hint

remove_categories ignores categories that do not exist.

No error is raised. remove_categories only removes categories present in the current categories list; others are ignored.

🚀 Application

advanced

Effect of removing a category on data values

Given this code, what is the output of the print statement?

Pandas

import pandas as pd
cat = pd.Categorical(['apple', 'banana', 'cherry', 'banana'], categories=['apple', 'banana', 'cherry'])
cat = cat.remove_categories(['banana'])
print(cat.tolist())

A ['apple', 'cherry', 'cherry', 'cherry']

B ['apple', nan, 'cherry', nan]

C ['apple', 'banana', 'cherry', 'banana']

D ['apple', None, 'cherry', None]

💡 Hint

Removing a category replaces its values with NaN in the data.

When a category is removed, any data values with that category become NaN (missing).

🧠 Conceptual

expert

Why use add_categories instead of directly assigning new categories?

Why is it better to use add_categories to add new categories instead of assigning a new categories list directly?

A Direct assignment automatically updates data values to new categories

B Direct assignment is faster and safer than add_categories

C add_categories removes old categories automatically

D add_categories preserves existing data values and only adds new categories, avoiding errors

💡 Hint

Think about what happens to existing data when categories change.

add_categories safely adds new categories without changing existing data values, preventing errors or data loss.

Practice

1. What does the add_categories() method do in pandas categorical data?

easy

A. It converts a column to a categorical type.

B. It removes categories from a categorical column.

C. It adds new possible categories to an existing categorical column.

D. It renames existing categories in a categorical column.

Solution

1. Step 1: Understand the purpose of add_categories()

   This method is used to add new possible categories to a pandas categorical column without changing existing data.

2. Step 2: Differentiate from other methods

   Unlike remove_categories() which removes categories, add_categories() only adds new ones.

3. Final Answer:

   It adds new possible categories to an existing categorical column. -> Option C

4. Quick Check:

   add_categories() adds categories [OK]

Hint: Remember: add_categories adds new options, doesn't remove [OK]

Common Mistakes:

• Confusing add_categories with remove_categories
• Thinking it changes existing data values
• Assuming it renames categories

2. Which of the following is the correct syntax to remove categories from a pandas categorical column named df['color']?

easy

A. df['color'].remove_categories(['red', 'blue'])

B. df['color'].drop_categories(['red', 'blue'])

C. df['color'].delete_categories(['red', 'blue'])

D. df['color'].pop_categories(['red', 'blue'])

Solution

1. Step 1: Recall the correct method name

   The pandas method to remove categories is remove_categories().

2. Step 2: Match the syntax with the method

   df['color'].remove_categories(['red', 'blue']) uses remove_categories() correctly on the categorical column.

3. Final Answer:

   df['color'].remove_categories(['red', 'blue']) -> Option A

4. Quick Check:

   Use remove_categories() to remove categories [OK]

Hint: Remember method name is remove_categories, not drop or delete [OK]

Common Mistakes:

• Using non-existent methods like drop_categories
• Confusing remove_categories with add_categories
• Trying to remove categories with pop or delete

3. Given the code:

import pandas as pd
cat = pd.Categorical(['apple', 'banana', 'apple'], categories=['apple', 'banana', 'cherry'])
cat2 = cat.remove_categories(['banana'])
print(cat2)

What will be the output?

medium

A. [apple, NaN, apple]
Categories (2, object): [apple, cherry]

B. [apple, banana, apple]
Categories (2, object): [apple, cherry]

C. [apple, NaN, apple]
Categories (3, object): [apple, banana, cherry]

D. [apple, banana, apple]
Categories (3, object): [apple, banana, cherry]

Solution

1. Step 1: Understand remove_categories effect on data

   Removing 'banana' category replaces all 'banana' values with NaN and removes 'banana' from categories.

2. Step 2: Analyze the printed output

   Values become ['apple', NaN, 'apple'] and categories become ['apple', 'cherry'] with length 2.

3. Final Answer:

   [apple, NaN, apple] Categories (2, object): [apple, cherry] -> Option A

4. Quick Check:

   Removed category replaced by NaN [OK]

Hint: Removing category replaces values with NaN [OK]

Common Mistakes:

• Expecting removed category values to stay unchanged
• Thinking categories length remains same
• Confusing categories list after removal

4. Consider this code snippet:

import pandas as pd
cat = pd.Categorical(['dog', 'cat', 'bird'], categories=['dog', 'cat', 'bird'])
cat = cat.add_categories('fish')
cat = cat.remove_categories('cat')

What is the error in this code?

medium

A. Categorical cannot have more than 3 categories.

B. add_categories cannot add a single category.

C. You cannot chain add_categories and remove_categories.

D. remove_categories expects a list, not a string.

Solution

1. Step 1: Check method argument types

   remove_categories() requires a list or iterable of categories to remove, not a single string.

2. Step 2: Identify the error cause

   Passing 'cat' as a string causes a TypeError or unexpected behavior.

3. Final Answer:

   remove_categories expects a list, not a string. -> Option D

4. Quick Check:

   Pass list to remove_categories [OK]

Hint: Always pass list to remove_categories, not single string [OK]

Common Mistakes:

• Passing single string instead of list to remove_categories
• Thinking add_categories can't add one item
• Assuming chaining methods is invalid

5. You have a pandas categorical column df['status'] with categories ['new', 'in_progress', 'done']. You want to add a new category 'archived' and remove 'new' without losing data for other rows. Which sequence of commands achieves this correctly?

hard

A. df['status'] = df['status'].remove_categories(['new'])
df['status'].add_categories(['archived'])

B. df['status'] = df['status'].add_categories(['archived'])
df['status'] = df['status'].remove_categories(['new'])

C. df['status'].remove_categories(['new'])
df['status'].add_categories(['archived'])

D. df['status'].add_categories('archived')
df['status'].remove_categories('new')

Solution

1. Step 1: Add new category before removing

   Adding 'archived' first ensures it exists as a valid category before removal.

2. Step 2: Remove 'new' category properly

   Removing 'new' replaces its values with NaN, so do this after adding new category and assign back to column.

3. Step 3: Assign results back to the column

   Both methods return new categorical objects, so assign back to df['status'].

4. Final Answer:

   df['status'] = df['status'].add_categories(['archived']) df['status'] = df['status'].remove_categories(['new']) -> Option B

5. Quick Check:

   Add then remove, assign back [OK]

Hint: Add categories first, then remove, assign back [OK]

Common Mistakes:

• Removing category before adding new one
• Not assigning back to the DataFrame column
• Passing string instead of list to methods