Adding and removing categories in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a category in pandas and why use it?

A category in pandas is a special data type for variables with a fixed set of possible values. It saves memory and speeds up operations when you have repeated values like colors or types.

beginner

How do you add a new category to a pandas Categorical column?

Use the method cat.add_categories() on the column. For example, df['col'] = df['col'].cat.add_categories(['new_cat']) adds 'new_cat' as a new category.

beginner

How can you remove a category from a pandas Categorical column?

Use cat.remove_categories() method. For example, df['col'] = df['col'].cat.remove_categories(['old_cat']) removes 'old_cat' from categories.

intermediate

What happens if you remove a category that is currently used in the data?

The values in that category become NaN (missing) because the category no longer exists in the list of allowed categories.

intermediate

How do you reset categories to only those currently used in the data?

Use cat.remove_unused_categories() to drop categories not used in the data, cleaning up the category list.

Which pandas method adds a new category to a Categorical column?

A cat.append_categories()

B cat.insert_categories()

C cat.add_categories()

D cat.new_category()

The correct method to add categories is cat.add_categories().

What happens to data values in a category that is removed using cat.remove_categories()?

A They are duplicated

B They become NaN (missing)

C They are converted to strings

D They stay unchanged

Removing a category causes its values to become NaN because the category no longer exists.

How do you remove categories that are not used in the data?

A cat.remove_unused_categories()

B cat.drop_unused()

C cat.clean_categories()

D cat.trim_categories()

The method cat.remove_unused_categories() removes categories not present in the data.

Why use categories in pandas?

A To sort numbers faster

B To store images

C To create plots automatically

D To save memory and speed up operations

Categories save memory and speed up operations for repeated values.

Which of these is NOT a valid way to modify categories in pandas?

A cat.update_categories()

B cat.remove_categories()

C cat.add_categories()

D cat.remove_unused_categories()

There is no cat.update_categories() method in pandas.

Explain how to add and remove categories in a pandas Categorical column. Include what happens to data when categories are removed.

Think about how categories control allowed values and what happens if you remove one.

Describe why and how you would clean up unused categories in a pandas Categorical column.

Consider categories that no longer appear in the data.

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