Cross-tabulation with crosstab() in Data Analysis Python - Mini Project: Build & Apply

Cross-tabulation with crosstab()

📖 Scenario: You work in a small bookstore. You have data about customers' favorite book genres and their preferred reading time (Morning, Afternoon, Evening). You want to understand how many customers prefer each genre at different times of the day.

🎯 Goal: Create a cross-tabulation table using pandas.crosstab() to show the count of customers for each combination of Genre and ReadingTime.

📋 What You'll Learn

Create a pandas DataFrame with the exact data provided.

Create a variable for the column names to use in crosstab.

Use pandas.crosstab() with the specified columns.

Print the resulting cross-tabulation table.

💡 Why This Matters

🌍 Real World

Cross-tabulation helps businesses understand relationships between categories, like customer preferences by time or location.

💼 Career

Data analysts and scientists use crosstab to summarize and explore categorical data quickly for reports and decision-making.

1

Create the customer data DataFrame

Create a pandas DataFrame called df with these exact columns and values:
CustomerID: [1, 2, 3, 4, 5, 6, 7, 8]
Genre: ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction', 'Sci-Fi', 'Fiction', 'Sci-Fi']
ReadingTime: ['Morning', 'Afternoon', 'Evening', 'Morning', 'Evening', 'Afternoon', 'Morning', 'Evening']

Data Analysis Python

import pandas as pd
# Create the DataFrame df with the exact data
# Your code here

Hint

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of data.

2

Set the columns for crosstab

Create a list called columns with the exact values ['Genre', 'ReadingTime'] to specify the columns to use in the crosstab.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [1, 2, 3, 4, 5, 6, 7, 8],
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction', 'Sci-Fi', 'Fiction', 'Sci-Fi'],
    'ReadingTime': ['Morning', 'Afternoon', 'Evening', 'Morning', 'Evening', 'Afternoon', 'Morning', 'Evening']
})

# Create the list columns with ['Genre', 'ReadingTime']
# Your code here

Hint

Just assign the list ['Genre', 'ReadingTime'] to the variable columns.

3

Create the cross-tabulation table

Use pandas.crosstab() with df[columns[0]] as rows and df[columns[1]] as columns. Store the result in a variable called cross_tab.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [1, 2, 3, 4, 5, 6, 7, 8],
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction', 'Sci-Fi', 'Fiction', 'Sci-Fi'],
    'ReadingTime': ['Morning', 'Afternoon', 'Evening', 'Morning', 'Evening', 'Afternoon', 'Morning', 'Evening']
})

columns = ['Genre', 'ReadingTime']

# Create cross_tab using pd.crosstab with df[columns[0]] and df[columns[1]]
# Your code here

Hint

Use pd.crosstab(df[columns[0]], df[columns[1]]) to create the table.

4

Print the cross-tabulation table

Print the variable cross_tab to display the cross-tabulation table.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [1, 2, 3, 4, 5, 6, 7, 8],
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction', 'Sci-Fi', 'Fiction', 'Sci-Fi'],
    'ReadingTime': ['Morning', 'Afternoon', 'Evening', 'Morning', 'Evening', 'Afternoon', 'Morning', 'Evening']
})

columns = ['Genre', 'ReadingTime']

cross_tab = pd.crosstab(df[columns[0]], df[columns[1]])

# Print the cross_tab variable
# Your code here

Hint

Use print(cross_tab) to show the table.