Why categorical type matters in Pandas - Quick Recap

Recall & Review

beginner

What is a categorical data type in pandas?

A categorical data type in pandas is a special type used to represent variables that have a fixed number of possible values, called categories. It saves memory and can speed up operations compared to using plain strings.

beginner

How does using categorical type save memory?

Categorical type stores data as integer codes internally instead of full strings. This reduces the amount of memory needed, especially when many values repeat the same categories.

intermediate

Why can categorical data speed up some operations?

Because categorical data uses integer codes, pandas can perform comparisons and groupings faster than with strings. This makes sorting, filtering, and grouping more efficient.

beginner

When should you use categorical data type?

Use categorical type when your data has a limited set of repeated values, like colors, categories, or labels. It helps with memory and speed, especially for large datasets.

intermediate

Can categorical data have an order? What does it mean?

Yes, categorical data can be ordered. This means the categories have a meaningful sequence, like 'small', 'medium', 'large'. Ordered categorical data allows comparisons like greater than or less than.

What is the main benefit of using categorical type in pandas?

A Saves memory and speeds up operations

B Allows storing images

C Automatically cleans data

D Converts numbers to strings

Categorical type saves memory by storing codes and speeds up operations like grouping.

How does pandas store categorical data internally?

A As boolean values

B As floating point numbers

C As full strings

D As integer codes

Pandas stores categorical data as integer codes representing categories.

When is it NOT a good idea to use categorical type?

A When data has few repeated categories

B When data has many unique values with few repeats

C When data is numeric

D When data is ordered

Categorical type is less useful if most values are unique and not repeated.

What does ordered categorical data allow you to do?

A Compare categories with greater or less than

B Store images

C Automatically fill missing values

D Convert categories to numbers

Ordered categorical data lets you compare categories in a meaningful sequence.

Which pandas function converts a column to categorical type?

A pd.to_datetime()

B pd.to_numeric()

C pd.Categorical()

D pd.read_csv()

pd.Categorical() converts data to categorical type.

Explain why using categorical data type can improve performance and memory usage in pandas.

Think about how storing repeated strings as numbers helps.

Describe a real-life example where converting a column to categorical type would be helpful.

Consider something like colors, days of the week, or product types.

Practice

1. Why is using the categorical data type in pandas helpful when you have a column with many repeated text values?

easy

A. It converts text to numbers for calculations.

B. It automatically sorts the data alphabetically.

C. It saves memory by storing repeated values efficiently.

D. It removes duplicate rows from the DataFrame.

Solution

1. Step 1: Understand memory usage of text data

   Repeated text values take more memory because each string is stored separately.

2. Step 2: How categorical type optimizes memory

   Categorical type stores unique values once and uses codes to represent them, saving memory.

3. Final Answer:

   It saves memory by storing repeated values efficiently. -> Option C

4. Quick Check:

   Memory saving = A [OK]

Hint: Categorical stores unique values once, saving memory [OK]

Common Mistakes:

• Thinking categorical sorts data automatically
• Believing categorical removes duplicates
• Assuming categorical converts text to numbers for math

2. Which of the following is the correct way to convert a pandas column df['color'] to categorical type?

easy

A. df['color'] = df['color'].convert('category')

B. df['color'] = pd.to_numeric(df['color'])

C. df['color'] = df['color'].astype('string')

D. df['color'] = df['color'].astype('category')

Solution

1. Step 1: Identify correct pandas method for type conversion

   The astype() method is used to change data types in pandas.

2. Step 2: Check correct argument for categorical type

   Passing the string 'category' to astype() converts the column to categorical type.

3. Final Answer:

   df['color'] = df['color'].astype('category') -> Option D

4. Quick Check:

   astype('category') = D [OK]

Hint: Use astype('category') to convert column type [OK]

Common Mistakes:

• Using pd.to_numeric for text columns
• Trying astype('string') instead of 'category'
• Using a non-existent convert() method

3. Given this code:

import pandas as pd
colors = ['red', 'blue', 'red', 'green', 'blue', 'blue']
df = pd.DataFrame({'color': colors})
df['color'] = df['color'].astype('category')
print(df['color'].cat.codes.tolist())

What is the output?

medium

A. [2, 0, 2, 1, 0, 0]

B. [1, 2, 1, 3, 2, 2]

C. [0, 1, 0, 2, 1, 1]

D. [0, 0, 0, 0, 0, 0]

Solution

1. Step 1: Understand category codes assignment

   Categories are assigned codes in alphabetical order: 'blue' = 0, 'green' = 1, 'red' = 2.

2. Step 2: Check actual categories order

   By default, pandas assigns categories in alphabetical order, not order of appearance.

3. Step 3: Map colors to codes

   Mapping list: 'red'->2, 'blue'->0, 'red'->2, 'green'->1, 'blue'->0, 'blue'->0.

4. Final Answer:

   [2, 0, 2, 1, 0, 0] -> Option A

5. Quick Check:

   Category codes = A [OK]

Hint: Category codes start at 0 in alphabetical order [OK]

Common Mistakes:

• Assuming order of appearance for codes
• Thinking codes start at 1
• Confusing category codes with original values

4. You run this code:

df['size'] = ['small', 'medium', 'large', 'medium']
df['size'] = df['size'].astype('category')
df['size'] = df['size'].cat.set_categories(['small', 'medium', 'large'], ordered=True)
print(df['size'].sort_values())

But the output is not sorted as expected. What is the likely problem?

medium

A. The set_categories method does not sort the data.

B. The categories were not set as ordered before conversion.

C. You must convert to string type before sorting.

D. The sort_values() method does not work on categorical data.

Solution

1. Step 1: Understand what set_categories does

   It sets the categories and order but does not reorder the actual data.

2. Step 2: How sorting works on ordered categorical

   Sorting uses the category order, but sort_values() sorts rows, not the category levels themselves.

3. Final Answer:

   The set_categories method does not sort the data. -> Option A

4. Quick Check:

   set_categories() sets order, doesn't sort data [OK]

Hint: set_categories sets order, sort_values sorts rows [OK]

Common Mistakes:

• Expecting set_categories to reorder data automatically
• Thinking sort_values fails on categorical columns
• Trying to convert to string before sorting

5. You have a large DataFrame with a column 'city' containing many repeated city names. You want to reduce memory usage and speed up filtering for cities in a specific order: 'New York', 'Los Angeles', 'Chicago'. Which approach is best?

hard

A. Keep 'city' as string type and sort the DataFrame by 'city' alphabetically

B. Convert 'city' to categorical with ordered categories: ['New York', 'Los Angeles', 'Chicago']

C. Convert 'city' to numeric codes without setting categories

D. Remove duplicate city names from the DataFrame

Solution

1. Step 1: Why use categorical with order

   Ordered categorical saves memory and allows meaningful sorting/filtering by specified city order.

2. Step 2: Why other options are less suitable

   Keeping strings uses more memory; numeric codes without categories lose order meaning; removing duplicates loses data.

3. Final Answer:

   Convert 'city' to categorical with ordered categories: ['New York', 'Los Angeles', 'Chicago'] -> Option B

4. Quick Check:

   Ordered categorical = A [OK]

Hint: Use ordered categorical for memory and custom order [OK]

Common Mistakes:

• Not setting categories order
• Using numeric codes without categories
• Removing duplicates instead of optimizing type