Why categorical type matters in Pandas - See It in Action

Why categorical type matters

📖 Scenario: Imagine you work in a store that sells different types of fruits. You have a list of fruits sold each day, but some fruits appear many times. You want to organize this data efficiently and understand how many times each fruit was sold.

🎯 Goal: You will create a list of fruits sold, convert it to a categorical type in pandas, and then compare the memory usage and counts of each fruit. This will show why using categorical data is helpful.

📋 What You'll Learn

Create a pandas Series with fruit names

Create a categorical version of the Series

Compare memory usage of both Series

Count how many times each fruit appears

💡 Why This Matters

🌍 Real World

Stores, surveys, and many datasets have repeated categories like product types or answers. Using categorical types helps save memory and speeds up analysis.

💼 Career

Data analysts and scientists often work with large datasets. Knowing how to use categorical data types helps optimize performance and resource use.

1

Create a pandas Series with fruit names

Import pandas as pd and create a pandas Series called fruits with these exact values: ["apple", "banana", "apple", "orange", "banana", "apple"].

Pandas

# Import pandas and create the fruits Series
# Your code here

Need a hint?

Use pd.Series() to create the Series from the list of fruits.

2

Create a categorical version of the Series

Create a new variable called fruits_cat by converting the fruits Series to categorical type using pd.Series.astype('category').

Pandas

import pandas as pd
fruits = pd.Series(["apple", "banana", "apple", "orange", "banana", "apple"])
# Convert fruits to categorical type and store in fruits_cat
# Your code here

Need a hint?

Use astype('category') on the fruits Series.

3

Compare memory usage of both Series

Create two variables: mem_fruits and mem_fruits_cat. Use the memory_usage(deep=True) method on fruits and fruits_cat respectively to get their memory usage.

Pandas

import pandas as pd
fruits = pd.Series(["apple", "banana", "apple", "orange", "banana", "apple"])
fruits_cat = fruits.astype('category')
# Get memory usage of fruits and fruits_cat
# Your code here

Need a hint?

Use memory_usage(deep=True) on both Series and save the results.

4

Print memory usage and counts of each fruit

Print the memory usage variables mem_fruits and mem_fruits_cat on separate lines. Then print the counts of each fruit in fruits_cat using the value_counts() method.

Pandas

import pandas as pd
fruits = pd.Series(["apple", "banana", "apple", "orange", "banana", "apple"])
fruits_cat = fruits.astype('category')
mem_fruits = fruits.memory_usage(deep=True)
mem_fruits_cat = fruits_cat.memory_usage(deep=True)
# Print memory usage and counts of fruits_cat
# Your code here

Need a hint?

Use print() to show memory usage and value_counts() to count fruits.