crosstab() for cross-tabulation in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Output of simple crosstab() usage

What is the output of the following code?

import pandas as pd

data = {'Gender': ['Male', 'Female', 'Female', 'Male', 'Female'],
        'Preference': ['Coffee', 'Coffee', 'Tea', 'Coffee', 'Tea']}
df = pd.DataFrame(data)

result = pd.crosstab(df['Gender'], df['Preference'])
print(result)

Pandas

import pandas as pd

data = {'Gender': ['Male', 'Female', 'Female', 'Male', 'Female'],
        'Preference': ['Coffee', 'Coffee', 'Tea', 'Coffee', 'Tea']}
df = pd.DataFrame(data)

result = pd.crosstab(df['Gender'], df['Preference'])
print(result)

A

Preference  Coffee  Tea
Gender                 
Female          1     2
Male            2     0

B

Preference  Coffee  Tea
Gender                 
Female          2     1
Male            1     1

C

Preference  Coffee  Tea
Gender                 
Female          1     2
Male            1     0

D

Preference  Coffee  Tea
Gender                 
Female          1     1
Male            2     1

💡 Hint

Count how many times each gender prefers each drink.

The crosstab counts occurrences of each combination. Females prefer Tea 2 times and Coffee 1 time. Males prefer Coffee 2 times and Tea 0 times.

🧠 Conceptual

intermediate

Understanding margins in crosstab()

What does the margins=True parameter add to the output of pd.crosstab()?

A It filters the data to only include rows with missing values.

B It adds a row and column showing the total counts for each category.

C It normalizes the counts to show proportions instead of raw counts.

D It sorts the rows and columns alphabetically.

💡 Hint

Think about what 'margins' means in a table context.

Margins add totals for rows and columns, helping to see overall counts.

❓ Predict Output

advanced

Output with multiple aggregation functions

What is the output of this code?

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'A'],
        'Result': ['Win', 'Lose', 'Win', 'Lose', 'Win'],
        'Points': [3, 0, 3, 0, 3]}
df = pd.DataFrame(data)

result = pd.crosstab(df['Team'], df['Result'], values=df['Points'], aggfunc='sum', margins=True)
print(result)

Pandas

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'A'],
        'Result': ['Win', 'Lose', 'Win', 'Lose', 'Win'],
        'Points': [3, 0, 3, 0, 3]}
df = pd.DataFrame(data)

result = pd.crosstab(df['Team'], df['Result'], values=df['Points'], aggfunc='sum', margins=True)
print(result)

A

Result  Lose  Win  All
Team                   
A          0    3    3
B          0    3    3
All        0    6    6

B

Result  Lose  Win  All
Team                   
A          0    9    9
B          0    3    3
All        0   12   12

C

Result  Lose  Win  All
Team                   
A          0    6    9
B          0    3    3
All        0    9   12

D

Result  Lose  Win  All
Team                   
A          0    6    6
B          0    3    3
All        0    9    9

💡 Hint

Sum points for each team and result, then add totals.

Team A has two wins (3+3=6 points) and one lose (0). Team B has one win (3) and one lose (0). Totals sum accordingly.

🔧 Debug

advanced

Identify the error in crosstab() usage

What error will this code produce?

import pandas as pd

data = {'Category': ['X', 'Y', 'X'], 'Value': [10, 20, 30]}
df = pd.DataFrame(data)

result = pd.crosstab(df['Category'], df['Value'], aggfunc='sum')
print(result)

Pandas

import pandas as pd

data = {'Category': ['X', 'Y', 'X'], 'Value': [10, 20, 30]}
df = pd.DataFrame(data)

result = pd.crosstab(df['Category'], df['Value'], aggfunc='sum')
print(result)

A TypeError: Cannot specify 'aggfunc' without 'values' parameter

B No error, outputs a frequency table

C ValueError: aggfunc must be a string, function or list of functions

D KeyError: 'Value' column not found

💡 Hint

Check if 'values' parameter is provided when using 'aggfunc'.

aggfunc requires a 'values' argument to know what to aggregate. Without it, pandas raises a TypeError.

🚀 Application

expert

Using crosstab() to analyze survey data

You have survey data with columns AgeGroup and FavoriteFruit. You want to find the percentage distribution of favorite fruits within each age group. Which code snippet produces this result?

A pd.crosstab(df['AgeGroup'], df['FavoriteFruit'], normalize=True)

B pd.crosstab(df['AgeGroup'], df['FavoriteFruit'], normalize='columns')

C pd.crosstab(df['AgeGroup'], df['FavoriteFruit'], normalize='index')

D pd.crosstab(df['AgeGroup'], df['FavoriteFruit'], margins=True)

💡 Hint

Think about normalizing rows to get percentages within each age group.

normalize='index' normalizes counts row-wise, giving percentage distribution within each age group.