Converting to categorical in Pandas - Mini Project: Build & Apply

Converting to categorical

📖 Scenario: You work in a company that collects customer feedback. The feedback includes a column for customer satisfaction levels recorded as text labels like 'Low', 'Medium', and 'High'. You want to convert these text labels into a categorical type to save memory and prepare for analysis.

🎯 Goal: Convert a pandas DataFrame column with text labels into a categorical data type.

📋 What You'll Learn

Create a pandas DataFrame with a column named satisfaction containing the exact values: 'Low', 'Medium', 'High', 'Medium', 'Low'.

Create a variable called categories that lists the categories in order: 'Low', 'Medium', 'High'.

Convert the satisfaction column to a categorical type using the categories variable.

Print the satisfaction column's data type after conversion.

💡 Why This Matters

🌍 Real World

Categorical data types save memory and improve performance when working with columns that have repeated text values, like survey responses or product categories.

💼 Career

Data scientists and analysts often convert text columns to categorical types to optimize data storage and prepare data for machine learning models.

1

Create the DataFrame

Create a pandas DataFrame called df with one column named satisfaction containing these exact values in order: 'Low', 'Medium', 'High', 'Medium', 'Low'.

Pandas

import pandas as pd
# Create the DataFrame df with the satisfaction column
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where the key is 'satisfaction' and the value is the list of satisfaction levels.

2

Define the categories

Create a variable called categories that is a list containing the strings 'Low', 'Medium', and 'High' in this exact order.

Pandas

import pandas as pd

df = pd.DataFrame({
    'satisfaction': ['Low', 'Medium', 'High', 'Medium', 'Low']
})

# Create the categories list
# Your code here

Need a hint?

Just assign the list ['Low', 'Medium', 'High'] to the variable categories.

3

Convert the column to categorical

Convert the satisfaction column in df to a categorical type using the categories list. Use pd.Categorical with the categories argument and set ordered=True. Assign the result back to df['satisfaction'].

Pandas

import pandas as pd

df = pd.DataFrame({
    'satisfaction': ['Low', 'Medium', 'High', 'Medium', 'Low']
})

categories = ['Low', 'Medium', 'High']

# Convert satisfaction column to categorical
# Your code here

Need a hint?

Use pd.Categorical to convert the column and assign it back to df['satisfaction'].

4

Print the data type

Print the data type of the satisfaction column in df using print(df['satisfaction'].dtype).

Pandas

import pandas as pd

df = pd.DataFrame({
    'satisfaction': ['Low', 'Medium', 'High', 'Medium', 'Low']
})

categories = ['Low', 'Medium', 'High']

df['satisfaction'] = pd.Categorical(df['satisfaction'], categories=categories, ordered=True)

# Print the dtype of the satisfaction column
# Your code here

Need a hint?

Use print(df['satisfaction'].dtype) to see the data type after conversion.