Data aggregation reporting in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby aggregation with multiple functions

What is the output of this code snippet that groups data by 'Category' and applies multiple aggregation functions?

Pandas

import pandas as pd

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

result = data.groupby('Category').agg({'Value': ['sum', 'mean']})
print(result)

A

          Value     
           sum  mean
Category            
A           30  15.0
B           40  20.0
C           40  40.0

B

          Value     
           mean  sum
Category            
A           15.0  30
B           20.0  40
C           40.0  40

C

          Value     
           sum  mean
Category            
A           30  20.0
B           40  10.0
C           40  40.0

D

          Value     
           sum  mean
Category            
A           20  15.0
B           40  20.0
C           40  40.0

💡 Hint

Remember that sum adds all values per group and mean calculates the average.

The groupby groups rows by 'Category'. For each group, sum adds values and mean calculates average. For 'A', sum is 10+20=30 and mean is 15.0. For 'B', sum is 10+30=40 and mean is 20.0. For 'C', only one value 40, so sum and mean are 40.

❓ data_output

intermediate

Number of groups after filtering and aggregation

Given this DataFrame, how many groups remain after grouping by 'Type' and filtering groups with sum of 'Score' > 50?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Type': ['X', 'X', 'Y', 'Y', 'Z', 'Z'],
    'Score': [30, 25, 10, 15, 40, 20]
})

filtered = data.groupby('Type').filter(lambda x: x['Score'].sum() > 50)
groups = filtered.groupby('Type').ngroups
print(groups)

A 1

B 0

C 3

D 2

💡 Hint

Calculate sum of 'Score' per 'Type' and check which sums are greater than 50.

Sum per 'Type': X=55, Y=25, Z=60. Only groups X and Z have sum > 50. So after filtering, 2 groups remain.

🔧 Debug

advanced

Identify the error in aggregation code

What error does this code raise when trying to aggregate data?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Group': ['A', 'A', 'B'],
    'Value': [1, 2, 3]
})

result = data.groupby('Group').agg({'Value': 'sum', 'NonExistent': 'mean'})
print(result)

A AttributeError: 'DataFrameGroupBy' object has no attribute 'agg'

B KeyError: 'NonExistent'

C ValueError: No numeric types to aggregate

D TypeError: unsupported operand type(s) for +: 'int' and 'str'

💡 Hint

Check if all columns in aggregation dictionary exist in the DataFrame.

The aggregation dictionary includes 'NonExistent' column which is not in the DataFrame, causing a KeyError.

❓ visualization

advanced

Correct plot for aggregated data

Which option shows the correct bar plot code to visualize the sum of 'Sales' per 'Region' from this DataFrame?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Region': ['North', 'South', 'East', 'West', 'North', 'South'],
    'Sales': [100, 150, 200, 130, 120, 170]
})

agg_data = data.groupby('Region')['Sales'].sum()

A

plt.bar(agg_data, agg_data.index)
plt.show()

B

agg_data.plot(kind='bar')
plt.show()

C

plt.bar(agg_data.index, agg_data)
plt.show()

D

data.plot.bar(x='Region', y='Sales')
plt.show()

💡 Hint

Remember bar plot x-axis is categories, y-axis is values.

agg_data is a Series with index as 'Region' and values as sum of 'Sales'. plt.bar needs x as categories (index) and height as values.

🚀 Application

expert

Calculate weighted average after aggregation

Given this DataFrame, which code correctly calculates the weighted average 'Score' per 'Class' using 'Weight' as weights?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Class': ['X', 'X', 'Y', 'Y', 'Y'],
    'Score': [80, 90, 70, 60, 75],
    'Weight': [1, 3, 2, 1, 2]
})

A data.groupby('Class').apply(lambda x: (x['Score'] * x['Weight']).sum() / x['Weight'].sum())

B data.groupby('Class').agg({'Score': lambda x: (x * data['Weight']).mean()})

C data.groupby('Class').agg({'Score': 'mean'})

D data.groupby('Class').apply(lambda x: x['Score'].mean() * x['Weight'].mean())

💡 Hint

Weighted average is sum of (value * weight) divided by sum of weights.

Option A correctly computes weighted average per group by multiplying 'Score' and 'Weight' per row, summing, then dividing by sum of weights in that group.