Why Adding and removing categories in Pandas? - Purpose & Use Cases

The Big Idea

What if you could fix messy labels in your data with just a few simple commands?

The Scenario

Imagine you have a list of customer feedback with many different labels. You want to group similar feedback into categories manually by writing down each label and sorting them on paper or in a simple text file.

The Problem

This manual sorting is slow and confusing. You might miss some labels or put them in the wrong group. If new labels appear, you have to redo everything. It's easy to make mistakes and hard to keep track.

The Solution

Using pandas categories, you can add or remove categories easily in your data. This helps you organize labels clearly and update groups quickly without errors. It makes your data neat and ready for analysis.

Before vs After

✗ Before

labels = ['apple', 'banana', 'orange']
# Manually track categories in a list
categories = ['fruit', 'vegetable']
# No easy way to add or remove categories

✓ After

import pandas as pd
cats = pd.Categorical(['apple', 'banana', 'orange'], categories=['fruit', 'vegetable'])
cats = cats.add_categories(['berry'])
cats = cats.remove_categories(['vegetable'])

What It Enables

You can quickly organize and update your data categories, making analysis faster and more accurate.

Real Life Example

A store manager can add new product categories or remove outdated ones in sales data to keep reports accurate and up to date.

Key Takeaways

Manual category management is slow and error-prone.

pandas categories let you add or remove groups easily.

This keeps your data organized and ready for analysis.

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