crosstab() for cross-tabulation in Pandas - Mini Project: Build & Apply

Using crosstab() for Cross-Tabulation in pandas

📖 Scenario: You work in a small store and have collected data about customers' gender and whether they bought a product or not. You want to see how many males and females bought or did not buy the product.

🎯 Goal: Build a simple cross-tabulation table using pandas crosstab() to count the number of customers by gender and purchase status.

📋 What You'll Learn

Create a pandas DataFrame with exact customer data

Create a variable for the DataFrame

Use pandas crosstab() with the correct columns

Print the resulting cross-tabulation table

💡 Why This Matters

🌍 Real World

Cross-tabulation helps businesses quickly see relationships between categories, like customer gender and buying behavior.

💼 Career

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

1

Create the customer data DataFrame

Create a pandas DataFrame called df with two columns: 'Gender' and 'Purchased'. Use these exact values: ['Male', 'Female', 'Female', 'Male', 'Female', 'Male'] for 'Gender' and ['Yes', 'No', 'Yes', 'No', 'Yes', 'Yes'] for 'Purchased'.

Pandas

import pandas as pd
# Create the DataFrame df with Gender and Purchased columns
# Your code here

Hint

Use pd.DataFrame() with a dictionary for columns.

2

Set up the cross-tabulation variable

Create a variable called cross_tab and assign it to the result of pd.crosstab() using df['Gender'] as rows and df['Purchased'] as columns.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Gender': ['Male', 'Female', 'Female', 'Male', 'Female', 'Male'],
    'Purchased': ['Yes', 'No', 'Yes', 'No', 'Yes', 'Yes']
})

# Create cross_tab using pd.crosstab with df['Gender'] and df['Purchased']
# Your code here

Hint

Use pd.crosstab() with the two DataFrame columns as arguments.

3

Print the cross-tabulation table

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

Pandas

import pandas as pd

df = pd.DataFrame({
    'Gender': ['Male', 'Female', 'Female', 'Male', 'Female', 'Male'],
    'Purchased': ['Yes', 'No', 'Yes', 'No', 'Yes', 'Yes']
})

cross_tab = pd.crosstab(df['Gender'], df['Purchased'])

# Print the cross_tab variable
# Your code here

Hint

Use print(cross_tab) to show the table.

4

Add totals to the cross-tabulation table

Update the cross_tab variable to include row and column totals by adding margins=True inside pd.crosstab(). Then print cross_tab again.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Gender': ['Male', 'Female', 'Female', 'Male', 'Female', 'Male'],
    'Purchased': ['Yes', 'No', 'Yes', 'No', 'Yes', 'Yes']
})

# Update cross_tab with margins=True
cross_tab = pd.crosstab(df['Gender'], df['Purchased'], margins=True)

# Print the updated cross_tab
# Your code here

Hint

Add margins=True inside pd.crosstab() to get totals.

Practice

1. What does the pandas.crosstab() function primarily do?

easy

A. Filters rows based on a condition

B. Sorts data in ascending order

C. Counts how often values from two columns appear together

D. Calculates the mean of numerical columns

Solution

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

   The crosstab() function creates a table showing frequency counts of combinations of values from two or more columns.

2. Step 2: Compare with other options

   Sorting, calculating mean, and filtering are different operations not done by crosstab().

3. Final Answer:

   Counts how often values from two columns appear together -> Option C

4. Quick Check:

   crosstab() = frequency count [OK]

Hint: Remember: crosstab counts pairs of values [OK]

Common Mistakes:

• Confusing crosstab with sorting or filtering
• Thinking crosstab calculates averages
• Assuming crosstab modifies original data

2. Which of the following is the correct syntax to create a crosstab of columns 'A' and 'B' from DataFrame df?

easy

A. pd.crosstab(df.A, df.B, axis=1)

B. pd.crosstab(df['A'], df['B'])

C. df.crosstab('A', 'B')

D. pd.crosstab(df['A', 'B'])

Solution

1. Step 1: Recall correct crosstab syntax

   The correct syntax is pd.crosstab(df['A'], df['B']) where you pass two series as arguments.

2. Step 2: Identify incorrect options

   pd.crosstab(df.A, df.B, axis=1) wrongly uses axis parameter; df.crosstab('A', 'B') tries to call crosstab as a DataFrame method which doesn't exist; pd.crosstab(df['A', 'B']) passes a tuple incorrectly.

3. Final Answer:

   pd.crosstab(df['A'], df['B']) -> Option B

4. Quick Check:

   pd.crosstab(series1, series2) = correct syntax [OK]

Hint: Use pd.crosstab(series1, series2) syntax [OK]

Common Mistakes:

• Calling crosstab as a DataFrame method
• Passing multiple columns as a tuple incorrectly
• Using wrong parameters like axis

3. Given the DataFrame:

import pandas as pd
data = {'Gender': ['M', 'F', 'F', 'M', 'F'], 'Preference': ['Tea', 'Coffee', 'Tea', 'Coffee', 'Tea']}
df = pd.DataFrame(data)

What is the output of pd.crosstab(df['Gender'], df['Preference'])?

medium

A. Preference Coffee Tea
Gender
F 1 2
M 1 1

B. Preference Coffee Tea
Gender
F 2 1
M 1 1

C. Preference Coffee Tea
Gender
F 1 1
M 2 1

D. Preference Coffee Tea
Gender
F 1 2
M 2 0

Solution

1. Step 1: Count occurrences for each Gender and Preference

   For 'F': Coffee appears once, Tea appears twice. For 'M': Coffee appears once, Tea appears once.

2. Step 2: Construct the crosstab table

   Rows are Gender (F, M), columns are Preference (Coffee, Tea), with counts filled accordingly.

3. Final Answer:

   Preference Coffee Tea Gender F 1 2 M 1 1 -> Option A

4. Quick Check:

   Count values by groups = Preference Coffee Tea Gender F 1 2 M 1 1 [OK]

Hint: Count each group pair carefully for crosstab [OK]

Common Mistakes:

• Mixing up row and column labels
• Counting values incorrectly
• Assuming missing pairs have counts > 0

4. What is wrong with this code snippet?

import pandas as pd
data = {'A': ['x', 'y', 'x'], 'B': ['p', 'q', 'p']}
df = pd.DataFrame(data)
result = pd.crosstab(df['A'], df['B'], margins=True, margin_name='Total')
print(result)

It raises an error. Why?

medium

A. The print statement is missing parentheses

B. You cannot use margins=True with crosstab

C. The DataFrame columns must be numeric for crosstab

D. The parameter name should be margins_name, not margin_name

Solution

1. Step 1: Check parameter names for crosstab()

   The correct parameter to name the totals row/column is margins_name, not margin_name.

2. Step 2: Identify the cause of error

   Using margin_name causes a TypeError because it's an unexpected keyword argument.

3. Final Answer:

   The parameter name should be margins_name, not margin_name -> Option D

4. Quick Check:

   Correct parameter is margins_name [OK]

Hint: Use margins_name, not margin_name for totals [OK]

Common Mistakes:

• Misspelling parameter names
• Thinking crosstab requires numeric columns
• Forgetting parentheses in print (Python 3)

5. You have a DataFrame df with columns 'City', 'Product', and 'Sales'. You want to create a crosstab showing total sales for each city-product pair. Which code correctly achieves this?

hard

A. pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum')

B. pd.crosstab(df['City'], df['Product'], values='Sales', aggfunc='sum')

C. pd.crosstab(df['City'], df['Product'], aggfunc='sum')

D. pd.crosstab(df['City'], df['Product'], values=df['Sales'])

Solution

1. Step 1: Understand crosstab with aggregation

   To aggregate values like sales, use values= with the column and aggfunc= with the aggregation function.

2. Step 2: Check each option

   pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum') correctly passes the sales column and sum function. pd.crosstab(df['City'], df['Product'], values='Sales', aggfunc='sum') passes a string instead of a series for values. pd.crosstab(df['City'], df['Product'], aggfunc='sum') misses values parameter. pd.crosstab(df['City'], df['Product'], values=df['Sales']) misses aggfunc, so it defaults to count.

3. Final Answer:

   pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum') -> Option A

4. Quick Check:

   Use values= and aggfunc= for aggregation [OK]

Hint: Use values=column and aggfunc='sum' for totals [OK]

Common Mistakes:

• Passing column name as string instead of series
• Omitting aggfunc for aggregation
• Expecting crosstab to sum without values parameter