Iterating over groups in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of group iteration with sum aggregation

What is the output of this code snippet that groups data by 'Category' and sums the 'Value' column?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'B', 'C'],
    'Value': [10, 20, 30, 40, 50]
})
result = {}
for name, group in data.groupby('Category'):
    result[name] = group['Value'].sum()
print(result)

A {'A': 40, 'B': 60, 'C': 50}

B {'A': 30, 'B': 60, 'C': 50}

C {'A': 40, 'B': 20, 'C': 50}

D {'A': 40, 'B': 60, 'C': 0}

💡 Hint

Sum the 'Value' for each group named by 'Category'.

The code groups rows by 'Category'. For each group, it sums the 'Value' column. 'A' has 10+30=40, 'B' has 20+40=60, 'C' has 50.

❓ data_output

intermediate

Number of groups created by groupby

How many groups will be created by grouping this DataFrame by 'Type'?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Type': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'],
    'Score': [5, 10, 15, 20, 25, 30, 35]
})
groups = data.groupby('Type')
print(len(groups))

A 7

B 4

C 3

D 1

💡 Hint

Count unique values in 'Type'.

The unique values in 'Type' are 'X', 'Y', and 'Z', so 3 groups are created.

🔧 Debug

advanced

Identify the error in group iteration code

What error will this code raise when trying to iterate over groups?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Group': ['G1', 'G2', 'G1'],
    'Value': [1, 2, 3]
})
for group in data.groupby('Group'):
    print(group['Value'].sum())

A TypeError: list indices must be integers or slices, not str

B AttributeError: 'tuple' object has no attribute 'sum'

C KeyError: 'Value'

D No error, prints sums correctly

💡 Hint

Remember what groupby returns when iterated.

data.groupby returns tuples (group_name, group_df). The code tries to index the tuple with a string, causing TypeError.

❓ visualization

advanced

Plotting mean values per group

Which option produces a bar plot showing the mean 'Score' for each 'Category'?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'B', 'C'],
    'Score': [10, 20, 30, 40, 50]
})

A data.groupby('Category')['Score'].sum().plot(kind='line'); plt.show()

B data.groupby('Category').plot(kind='bar', y='Score'); plt.show()

C data.plot.bar(x='Category', y='Score'); plt.show()

D data.groupby('Category')['Score'].mean().plot(kind='bar'); plt.show()

💡 Hint

Group by 'Category', calculate mean, then plot bar chart.

Option D groups by 'Category', calculates mean 'Score', then plots a bar chart. Others either plot wrong data or wrong chart type.

🚀 Application

expert

Extract groups with max average value

Given a DataFrame grouped by 'Team', which code extracts the group(s) with the highest average 'Points'?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Team': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'],
    'Points': [10, 20, 30, 40, 50, 60, 70]
})

A

result = data.groupby('Team').apply(lambda g: g[g['Points'] == g['Points'].max()])
print(result)

B

means = data.groupby('Team')['Points'].mean()
max_mean = means.max()
result = data[data['Team'].isin(means[means == max_mean].index)]
print(result)

C

means = data.groupby('Team')['Points'].sum()
max_sum = means.max()
result = data[data['Team'] == means.idxmax()]
print(result)

D

max_mean = data['Points'].max()
result = data[data['Points'] == max_mean]
print(result)

💡 Hint

Find group means, then filter original data by groups with max mean.

Option B correctly computes mean Points per Team, finds max mean, then filters data for those Teams. Others either use max Points per row or sum instead of mean.