Why categorical type matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'color' column to categorical type.

Pandas

df['color'] = df['color'].[1]()

Drag options to blanks, or click blank then click option'

Aastype('category')

Bto_numeric

Cstr

Dcopy

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric tries to convert to numbers, which is wrong here.

Using str converts to string, not categorical.

Using astype('category') converts the column to categorical type, which saves memory and speeds up operations.

2fill in blank

medium

Complete the code to check the memory usage of the DataFrame.

Pandas

memory_before = df.memory_[1](deep=True)

Drag options to blanks, or click blank then click option'

Ausage

Bsize

Cinfo

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using size returns number of elements, not memory.

Using info prints info but does not return memory usage.

The memory_usage(deep=True) method returns the memory used by the DataFrame including object types.

3fill in blank

hard

Fix the error in the code to convert the 'size' column to categorical.

Pandas

df['size'] = df['size'].astype([1])

Drag options to blanks, or click blank then click option'

Astr

B'category'

C'categorical'

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the type name without quotes causes an error.

Using 'categorical' is not a valid dtype.

The argument to astype must be the string 'category' to convert to categorical type.

4fill in blank

hard

Fill both blanks to create a dictionary of counts for categories with more than 2 entries.

Pandas

counts = {cat: count for cat, count in df['fruit'].value_counts().items() if count [1] [2]

Drag options to blanks, or click blank then click option'

A>

B2

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' will filter wrong categories.

Forgetting to compare count to a number.

This dictionary comprehension filters categories with counts greater than 2.

5fill in blank

hard

Fill all three blanks to create a new categorical column with ordered categories.

Pandas

df['rating_cat'] = pd.Categorical(df['rating'], categories=[1], ordered=[2])

unique_cats = df['rating_cat'].[3]()

Drag options to blanks, or click blank then click option'

A['low', 'medium', 'high']

BTrue

Cunique

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting ordered to False when order matters.

Using unique without parentheses.

We define ordered categories, set ordered=True, and get unique categories with unique().

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