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.

Practice

1. What does the pd.crosstab() function do in data analysis?

easy

A. It sorts data in ascending order.

B. It counts how often categories from two variables appear together.

C. It calculates the average of numerical columns.

D. It removes missing values from the dataset.

Solution

1. Step 1: Understand the purpose of crosstab()

   The function pd.crosstab() is used to count occurrences of combinations of categories from two variables.

2. Step 2: Compare with other options

   Sorting, averaging, and removing missing values are different functions, not related to crosstab.

3. Final Answer:

   It counts how often categories from two variables appear together. -> Option B

4. Quick Check:

   Cross-tabulation = counts of category pairs [OK]

Hint: Remember: crosstab counts category pairs, not calculations [OK]

Common Mistakes:

• Thinking crosstab sorts data
• Confusing crosstab with aggregation functions
• Assuming crosstab cleans data

2. Which of the following is the correct syntax to create a cross-tabulation table using pandas?

easy

A. pd.crosstab(index=df['AgeGroup'], columns=df['Gender'])

B. pd.crosstab(df.Gender, columns='AgeGroup')

C. pd.crosstab(columns=df['Gender'], index=df['AgeGroup'])

D. pd.crosstab(df['Gender'], df['AgeGroup'])

Solution

1. Step 1: Recall the correct parameter order for crosstab()

   The function pd.crosstab() takes the first argument as the row variable and the second as the column variable, both as Series or arrays.

2. Step 2: Check each option

   pd.crosstab(df['Gender'], df['AgeGroup']) correctly passes two Series without naming parameters. pd.crosstab(index=df['AgeGroup'], columns=df['Gender']) uses named parameters with row and column variables reversed. pd.crosstab(df.Gender, columns='AgeGroup') passes a string literal instead of a Series for the columns parameter. pd.crosstab(columns=df['Gender'], index=df['AgeGroup']) swaps index and columns incorrectly.

3. Final Answer:

   pd.crosstab(df['Gender'], df['AgeGroup']) -> Option D

4. Quick Check:

   Use positional args: rows, columns [OK]

Hint: Use positional args: first is rows, second is columns [OK]

Common Mistakes:

• Swapping index and columns parameters
• Mixing positional and named arguments incorrectly
• Passing DataFrame instead of Series

3. Given the DataFrame:

import pandas as pd
data = {'Color': ['Red', 'Blue', 'Red', 'Green', 'Blue', 'Green'],
        'Shape': ['Circle', 'Square', 'Square', 'Circle', 'Circle', 'Square']}
df = pd.DataFrame(data)
print(pd.crosstab(df['Color'], df['Shape']))

What is the output?

medium

A. Shape Circle Square
Color
Blue 1 1
Green 1 1
Red 1 1

B. Shape Circle Square
Color
Blue 2 0
Green 0 2
Red 1 1

C. Shape Circle Square
Color
Blue 1 0
Green 1 1
Red 1 1

D. Shape Circle Square
Color
Blue 1 1
Green 1 0
Red 1 1

Solution

1. Step 1: Count occurrences of each Color-Shape pair

   From the data:
   - Blue: Circle(1), Square(1)
   - Green: Circle(1), Square(1)
   - Red: Circle(1), Square(1)

2. Step 2: Construct the crosstab table

   Each color has one Circle and one Square, so the table shows 1 for each pair.

3. Final Answer:

   Shape Circle Square Color Blue 1 1 Green 1 1 Red 1 1 -> Option A

4. Quick Check:

   Count pairs correctly = Shape Circle Square Color Blue 1 1 Green 1 1 Red 1 1 [OK]

Hint: Count each category pair carefully from data [OK]

Common Mistakes:

• Miscounting pairs
• Swapping rows and columns
• Ignoring repeated categories

4. What is wrong with this code snippet?

import pandas as pd
data = {'Team': ['A', 'B', 'A', 'B'], 'Result': ['Win', 'Lose', 'Lose', 'Win']}
df = pd.DataFrame(data)
ct = pd.crosstab(index=df['Team'], columns='Result')
print(ct)

medium

A. The DataFrame is missing a column named 'Result'.

B. The index parameter should be a list, not a Series.

C. The columns parameter should be a Series, not a string.

D. The crosstab function requires numeric data only.

Solution

1. Step 1: Check the columns argument in crosstab()

   The columns argument must be a Series or array representing categories, not a string literal.

2. Step 2: Identify the error in the code

   Here, columns='Result' is a string, not df['Result']. This causes crosstab to treat it incorrectly.

3. Final Answer:

   The columns parameter should be a Series, not a string. -> Option C

4. Quick Check:

   Columns must be Series, not string [OK]

Hint: Pass Series for index and columns, not strings [OK]

Common Mistakes:

• Passing column name as string instead of Series
• Assuming crosstab needs numeric data
• Confusing index and columns parameters

5. You have a DataFrame with columns 'Department' and 'Satisfaction' (values: 'High', 'Medium', 'Low'). You want to find how many employees in each department have each satisfaction level, but only show departments with at least 3 employees total. Which code correctly does this?

hard

A. ct = pd.crosstab(df['Department'], df['Satisfaction'])
ct = ct[ct.sum(axis=1) >= 3]

B. ct = pd.crosstab(index=df['Satisfaction'], columns=df['Department'])
ct = ct[ct.sum(axis=0) >= 3]

C. ct = pd.crosstab(df['Department'], df['Satisfaction'])
ct = ct[ct.sum(axis=0) >= 3]

D. ct = pd.crosstab(index=df['Department'], columns=df['Satisfaction'])
ct = ct[ct.sum(axis=1) < 3]

Solution

1. Step 1: Create crosstab with Department as rows and Satisfaction as columns

   Use pd.crosstab(df['Department'], df['Satisfaction']) to count satisfaction levels per department.

2. Step 2: Filter departments with total employees >= 3

   Sum counts across columns (axis=1) for each department, then keep rows where sum >= 3.

3. Final Answer:

   ct = pd.crosstab(df['Department'], df['Satisfaction']) ct = ct[ct.sum(axis=1) >= 3] -> Option A

4. Quick Check:

   Filter rows by sum(axis=1) >= 3 [OK]

Hint: Sum rows (axis=1) to filter departments by total count [OK]

Common Mistakes:

• Filtering columns instead of rows
• Swapping index and columns in crosstab
• Using wrong axis in sum()