Adding and removing categories in Pandas - Step-by-Step Execution

Concept Flow - Adding and removing categories

Start with Categorical Data

↓

Add new category

↓

Check categories updated?

No→Error or Retry

Yes↓

Remove category

↓

Check categories updated?

No→Error or Retry

Yes↓

End

Start with categorical data, add new categories, verify update, then remove categories and verify again.

Execution Sample

Pandas

import pandas as pd
cat = pd.Categorical(['a', 'b', 'a'], categories=['a', 'b'])
cat = cat.add_categories(['c'])
cat = cat.remove_categories(['b'])
print(cat.categories)

Create a categorical variable, add a new category 'c', remove category 'b', then print remaining categories.

Execution Table

Step	Action	Categories Before	Categories After	Notes
1	Create categorical with categories ['a', 'b']	[]	['a', 'b']	Initial categories set
2	Add category 'c'	['a', 'b']	['a', 'b', 'c']	New category 'c' added
3	Remove category 'b'	['a', 'b', 'c']	['a', 'c']	Category 'b' removed
4	Print categories	['a', 'c']	['a', 'c']	Final categories displayed
5	End	['a', 'c']	['a', 'c']	No more steps

💡 All categories updated correctly; execution ends.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
cat.categories	[]	['a', 'b']	['a', 'b', 'c']	['a', 'c']	['a', 'c']

Key Moments - 2 Insights

Why does the category list change after adding a new category?

Because add_categories adds the new category to the existing list without changing existing data, as shown in step 2 of the execution_table.

What happens to data points with a removed category?

Data points with the removed category become NaN or missing, since the category no longer exists, as implied after step 3 in the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, what are the categories after step 2?

A ['a', 'c']

B ['a', 'b']

C ['a', 'b', 'c']

D ['b', 'c']

💡 Hint

Check the 'Categories After' column for step 2 in the execution_table.

At which step is the category 'b' removed?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Action' column in the execution_table to find when 'b' is removed.

If we add category 'd' after step 3, what will be the categories?

A ['a', 'c', 'd']

B ['a', 'b', 'c', 'd']

C ['a', 'b', 'd']

D ['a', 'c']

💡 Hint

After step 3, categories are ['a', 'c'], adding 'd' appends it.

Concept Snapshot

pandas Categorical categories can be changed.
Use add_categories(['new_cat']) to add.
Use remove_categories(['old_cat']) to remove.
Removing a category affects data points with it.
Always check categories after changes.

Full Transcript

We start with a pandas Categorical variable with categories 'a' and 'b'. Then we add a new category 'c' using add_categories, which updates the categories list to include 'c'. Next, we remove the category 'b' using remove_categories, which removes 'b' from the categories list. Finally, we print the categories to see the updated list, which is now ['a', 'c']. This process shows how to add and remove categories step-by-step in pandas.

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