Cross-tabulation advanced usage in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of multi-index cross-tabulation with margins

What is the output of this code snippet using pandas crosstab with multi-index and margins?

Pandas

import pandas as pd

data = {'Gender': ['Male', 'Female', 'Female', 'Male', 'Female'],
        'AgeGroup': ['Adult', 'Adult', 'Child', 'Child', 'Adult'],
        'Preference': ['B', 'A', 'A', 'B', 'A']}
df = pd.DataFrame(data)

result = pd.crosstab(index=[df['Gender'], df['AgeGroup']], columns=df['Preference'], margins=True)
print(result)

A {'A': {'Female': {'Adult': 2, 'Child': 1}, 'Male': {'Adult': 0, 'Child': 0}, 'All': 3}, 'B': {'Female': {'Adult': 0, 'Child': 0}, 'Male': {'Adult': 1, 'Child': 1}, 'All': 2}, 'All': {'Female': {'Adult': 2, 'Child': 1}, 'Male': {'Adult': 1, 'Child': 1}, 'All': 5}}

B

Preference       A  B  All
Gender AgeGroup           
Female Adult      2  0    2
       Child      1  0    1
Male   Adult      1  0    1
       Child      0  1    1
All              4  1    5

C

Preference       A  B  All
Gender AgeGroup           
Female Adult      2  0    2
       Child      1  0    1
Male   Adult      0  1    1
       Child      0  1    1
All              3  2    5

D

Preference       A  B  All
Gender AgeGroup           
Female Male       0  0    0
       Adult      2  0    2
       Child      1  0    1
Male   Adult      0  1    1
       Child      0  1    1
All              3  2    5

💡 Hint

Look carefully at the counts for each Gender and AgeGroup combination and the Preference columns.

The crosstab groups by Gender and AgeGroup on rows and Preference on columns. Margins add totals. The counts match the data exactly: Female Adults prefer A twice, Female Child prefers A once, Male Adults prefer B once, Male Child prefers B once.

❓ data_output

intermediate

Number of unique values in crosstab result

After running this code, how many unique values are in the resulting crosstab DataFrame?

Pandas

import pandas as pd

records = {'City': ['NY', 'LA', 'NY', 'LA', 'NY', 'LA'],
           'Product': ['X', 'X', 'Y', 'Y', 'X', 'Y'],
           'Sales': [10, 20, 10, 30, 20, 30]}
df = pd.DataFrame(records)

ct = pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum', dropna=False)
unique_values = ct.nunique().sum()

A 4

B 5

C 3

D 6

💡 Hint

Check the sums of sales for each City and Product combination.

The crosstab sums sales by City and Product. The sums are: NY-X=30, NY-Y=10, LA-X=20, LA-Y=60. Unique values are 4.

❓ visualization

advanced

Visualizing crosstab with normalization

Which option shows the correct heatmap visualization code for a normalized crosstab of 'Department' vs 'Satisfaction'?

Pandas

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = {'Department': ['HR', 'HR', 'IT', 'IT', 'Sales', 'Sales', 'Sales'],
        'Satisfaction': ['High', 'Low', 'High', 'Low', 'High', 'Low', 'Low']}
df = pd.DataFrame(data)

ct = pd.crosstab(df['Department'], df['Satisfaction'], normalize='index')

A

sns.heatmap(ct, annot=True, cmap='coolwarm')
plt.show()

B

sns.heatmap(ct.T, annot=True, cmap='viridis')
plt.show()

C

sns.heatmap(ct, annot=False, cmap='coolwarm')
plt.show()

D

sns.heatmap(ct, annot=True, cmap='Blues', cbar=False)
plt.show()

💡 Hint

Normalization is by index, so rows sum to 1. Annotated heatmap helps see values.

Option A correctly plots the normalized crosstab with annotations and a suitable color map. Option A transposes, which changes axes meaning. Option A lacks annotations. Option A removes color bar, which is less informative.

🔧 Debug

advanced

Identify the error in crosstab with missing values

What error will this code raise when running the crosstab with missing values in the data?

Pandas

import pandas as pd

data = {'Team': ['A', 'B', 'A', None, 'B'],
        'Result': ['Win', 'Lose', None, 'Win', 'Lose']}
df = pd.DataFrame(data)

ct = pd.crosstab(df['Team'], df['Result'])
print(ct)

A KeyError

B No error, prints crosstab with NaN rows/columns dropped

C TypeError

D ValueError

💡 Hint

Check how pandas crosstab handles missing values by default.

Pandas crosstab automatically excludes missing values in the grouping columns, so no error occurs. The output excludes rows or columns with NaN.

🚀 Application

expert

Calculate weighted crosstab with custom aggregation

Given this DataFrame, which option correctly computes a weighted crosstab of 'Category' vs 'Type' using the sum of 'Weight' as aggregation?

Pandas

import pandas as pd

data = {'Category': ['X', 'X', 'Y', 'Y', 'Z', 'Z'],
        'Type': ['A', 'B', 'A', 'B', 'A', 'B'],
        'Weight': [1.5, 2.0, 3.0, 1.0, 2.5, 0.5]}
df = pd.DataFrame(data)

A pd.crosstab(index=df['Category'], columns=df['Type'], values=df['Weight'], aggfunc='mean')

B pd.crosstab(df['Category'], df['Type'], aggfunc='sum')

C pd.crosstab(df['Category'], df['Type'], values='Weight', aggfunc='sum')

D pd.crosstab(df['Category'], df['Type'], values=df['Weight'], aggfunc='sum')

💡 Hint

Check the correct parameter names for values and aggregation function in pandas crosstab.

Option D uses correct parameter names: values=df['Weight'] and aggfunc='sum'. Option D misses values parameter. Option D uses values='Weight' (string) which is invalid. Option D uses aggfunc='mean' instead of sum.