Categorical data type optimization in Data Analysis Python - Mini Project: Build & Apply

Categorical data type optimization

📖 Scenario: You work in a company that collects customer feedback. The feedback data includes customer names and their satisfaction levels. The satisfaction levels are repeated many times and take only a few unique values.Storing this data efficiently can save memory and speed up analysis.

🎯 Goal: You will create a data structure to hold customer feedback, then convert the satisfaction levels to a categorical data type to optimize memory usage.

📋 What You'll Learn

Create a pandas DataFrame with customer names and satisfaction levels

Create a variable to hold the list of satisfaction levels

Convert the satisfaction levels column to a categorical data type

Print the DataFrame info to show memory usage

💡 Why This Matters

🌍 Real World

Categorical data types are used in data science to save memory and speed up analysis when working with repeated text values, such as survey responses or product categories.

💼 Career

Data analysts and data scientists often optimize data storage using categorical types to handle large datasets efficiently and improve performance.

1

Create the initial DataFrame

Create a pandas DataFrame called feedback with two columns: 'customer' and 'satisfaction'. Use these exact values for 'customer': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Helen'] and for 'satisfaction': ['Good', 'Bad', 'Good', 'Excellent', 'Bad', 'Good', 'Excellent', 'Bad'].

Data Analysis Python

import pandas as pd
# Create the DataFrame called feedback with the given customer and satisfaction values
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the two lists for columns.

2

Create a list of satisfaction levels

Create a variable called satisfaction_levels and assign it the list ['Bad', 'Good', 'Excellent'] representing the unique satisfaction categories.

Data Analysis Python

import pandas as pd
feedback = pd.DataFrame({
    'customer': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Helen'],
    'satisfaction': ['Good', 'Bad', 'Good', 'Excellent', 'Bad', 'Good', 'Excellent', 'Bad']
})
# Create the satisfaction_levels list with the unique categories
# Your code here

Hint

Just assign the list of unique satisfaction values to the variable satisfaction_levels.

3

Convert satisfaction to categorical type

Convert the 'satisfaction' column in the feedback DataFrame to a categorical data type using the satisfaction_levels list as categories. Assign the result back to feedback['satisfaction'].

Data Analysis Python

import pandas as pd
feedback = pd.DataFrame({
    'customer': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Helen'],
    'satisfaction': ['Good', 'Bad', 'Good', 'Excellent', 'Bad', 'Good', 'Excellent', 'Bad']
})
satisfaction_levels = ['Bad', 'Good', 'Excellent']
# Convert the 'satisfaction' column to categorical using satisfaction_levels
# Your code here

Hint

Use pd.Categorical() with the categories parameter set to satisfaction_levels.

4

Print DataFrame info to show memory usage

Print the information of the feedback DataFrame using feedback.info() to display the memory usage and data types.

Data Analysis Python

import pandas as pd
feedback = pd.DataFrame({
    'customer': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank', 'Grace', 'Helen'],
    'satisfaction': ['Good', 'Bad', 'Good', 'Excellent', 'Bad', 'Good', 'Excellent', 'Bad']
})
satisfaction_levels = ['Bad', 'Good', 'Excellent']
feedback['satisfaction'] = pd.Categorical(feedback['satisfaction'], categories=satisfaction_levels)
# Print the DataFrame info to show memory usage
# Your code here

Hint

Use feedback.info() to print the DataFrame details.

Practice

1.

What is the main benefit of converting a DataFrame column to category type in pandas?

easy

A. It reduces memory usage by storing unique values once

B. It automatically sorts the column values alphabetically

C. It converts numeric values to strings

D. It removes missing values from the column

Solution

1. Step 1: Understand what categorical type does

   The categorical type stores unique values only once and uses codes internally, saving memory.

2. Step 2: Compare with other options

   Sorting, converting to strings, or removing missing values are not automatic effects of categorical conversion.

3. Final Answer:

   It reduces memory usage by storing unique values once -> Option A

4. Quick Check:

   Categorical type = Memory saving [OK]

Hint: Categorical stores unique values once to save memory [OK]

Common Mistakes:

• Thinking it sorts values automatically
• Confusing with string conversion
• Assuming it removes missing data

2.

Which of the following is the correct syntax to convert a pandas DataFrame column df['color'] to categorical type?

import pandas as pd
df = pd.DataFrame({'color': ['red', 'blue', 'red', 'green']})

easy

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

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

C. df['color'] = pd.category(df['color'])

D. df['color'] = df['color'].to_category()

Solution

1. Step 1: Identify correct method to convert column type

   The astype('category') method converts a Series to categorical type.

2. Step 2: Check other options for errors

   df['color'] = df.astype('category') tries to convert whole DataFrame, not column. df['color'] = pd.category(df['color']) uses non-existent pd.category(). df['color'] = df['color'].to_category() uses wrong method name.

3. Final Answer:

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

4. Quick Check:

   astype('category') = Correct syntax [OK]

Hint: Use astype('category') on the specific column [OK]

Common Mistakes:

• Applying astype to whole DataFrame instead of column
• Using non-existent pd.category() function
• Using wrong method like to_category()

3.

What will be the output of the following code?

import pandas as pd

df = pd.DataFrame({'fruit': ['apple', 'banana', 'apple', 'orange', 'banana']})
df['fruit'] = df['fruit'].astype('category')
print(df['fruit'].cat.categories)

medium

A. Index(['apple', 'banana', 'orange'], dtype='object')

B. ['apple', 'banana', 'apple', 'orange', 'banana']

C. ['apple', 'banana', 'orange', 'apple', 'banana']

D. Error: 'cat' attribute not found

Solution

1. Step 1: Convert column to categorical and check categories

   After conversion, cat.categories shows unique sorted categories.

2. Step 2: Identify unique values in sorted order

   Unique fruits are 'apple', 'banana', 'orange' in alphabetical order.

3. Final Answer:

   Index(['apple', 'banana', 'orange'], dtype='object') -> Option A

4. Quick Check:

   cat.categories = unique sorted values [OK]

Hint: cat.categories shows unique sorted categories [OK]

Common Mistakes:

• Expecting original list instead of unique categories
• Confusing categories with values
• Thinking cat attribute is missing

4.

Identify the error in this code snippet that tries to convert a column to categorical:

import pandas as pd
df = pd.DataFrame({'size': ['S', 'M', 'L', 'M']})
df['size'] = df['size'].astype(category)

medium

A. astype() cannot convert to categorical

B. DataFrame creation syntax is wrong

C. Missing quotes around 'category' in astype()

D. Column 'size' does not exist

Solution

1. Step 1: Check astype() argument

   The argument to astype() must be a string 'category', not a bare word.

2. Step 2: Verify other parts of code

   DataFrame creation and column name are correct; astype() supports 'category'.

3. Final Answer:

   Missing quotes around 'category' in astype() -> Option C

4. Quick Check:

   Quotes needed for dtype string [OK]

Hint: Always put quotes around dtype names in astype() [OK]

Common Mistakes:

• Forgetting quotes around 'category'
• Assuming astype() can't convert to category
• Mistaking column name errors

5.

You have a DataFrame with a column 'city' containing 1 million rows but only 5 unique city names. Which approach optimizes memory and speeds up grouping by city?

hard

A. Use df.groupby('city') without any conversion

B. Convert 'city' column to string type before grouping

C. Drop duplicates from 'city' column before grouping

D. Convert 'city' column to categorical type before grouping

Solution

1. Step 1: Understand memory and speed benefits of categorical

   Converting to categorical stores unique values once, reducing memory and speeding grouping.

2. Step 2: Evaluate other options

   String type uses more memory; dropping duplicates loses data; grouping without conversion is slower.

3. Final Answer:

   Convert 'city' column to categorical type before grouping -> Option D

4. Quick Check:

   Categorical = Memory + speed optimization [OK]

Hint: Convert to categorical before grouping large repeated data [OK]

Common Mistakes:

• Using string type wastes memory
• Dropping duplicates loses data
• Grouping without conversion is slower