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Aggregating & Grouping

This quiz will test your understanding of these concepts with practical examples and code-based questions.

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Question 1

What does the following code return?

Python

import pandas as pd
data = {'A': [1, 2, 2], 'B': [3, 4, 5]}
df = pd.DataFrame(data)
result = df['A'].sum()
print(result)

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Question 2

What does this code output?

Python

import pandas as pd
data = {'Category': ['A', 'B', 'A', 'B'], 'Values': [10, 20, 30, 40]}
df = pd.DataFrame(data)
result = df.groupby('Category')['Values'].sum()
print(result)

A 40
B 60
Category
A 40
B 60
A 30
B 40
None of the above

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Question 3

Which of the following code snippets applies multiple aggregation functions?

Python

import pandas as pd 
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

df.agg(['sum', 'mean'])
df.groupby('A').sum()
df['A'].sum()
None of the above

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Question 4

What does this code return?

Python

import pandas as pd 
data = {'A': ['foo', 'bar', 'foo', 'bar'], 'B': [1, 2, 3, 4]}
df = pd.DataFrame(data)
result = df.groupby('A')['B'].transform('mean')
print(result)

[2.0, 3.0, 2.0, 3.0]
[3.0, 2.0, 3.0, 2.0]
[2.0, 3.0, 3.0, 2.0]
None of the above

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Question 5

How can you compute the range (max - min) of values for each group?

Python

import pandas as pd 
data = {'Group': ['X', 'X', 'Y', 'Y'], 'Values': [10, 20, 30, 40]}
df = pd.DataFrame(data)

df.groupby('Group')['Values'].apply(lambda x: x.max() - x.min())
df.groupby('Group').agg('range')
df.groupby('Group').apply(lambda x: x['Values'].range())
None of the above

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Question 6

What does the following code output?

Python

import pandas as pd 
data = {'A': ['foo', 'foo', 'bar', 'bar'], 
        'B': ['one', 'two', 'one', 'two'], 
        'C': [1, 2, 3, 4]}
df = pd.DataFrame(data)
result = df.groupby(['A', 'B']).sum()
print(result)

C
A B
foo one 1
two 2
bar one 3
two 4
A B
foo one 1
foo two 2
bar one 3
bar two 4
A B C
foo one 1
foo two 2
bar one 3
bar two 4
None of the above

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Question 7

What does this code return?

Python

import panadas as pd 
data = {'A': ['X', 'X', 'Y', 'Y'], 'B': ['one', 'two', 'one', 'two'], 'C': [10, 20, 30, 40]}
df = pd.DataFrame(data)
result = df.pivot_table(values='C', index='A', columns='B', aggfunc='sum')
print(result)

B one two
A
X 10 20
Y 30 40
B one two
X 15.0 25.0
Y 35.0 45.0
B one two
X 1 2
Y 3 4
None of the above

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Question 8

How can you find the sum of values greater than 20 for each group?

Python

import panads as pd
data = {'Group': ['A', 'A', 'B', 'B'], 'Values': [10, 30, 20, 40]}
df = pd.DataFrame(data)

df[df['Values'] > 20].groupby('Group')['Values'].sum()
df.groupby('Group')['Values'].sum().apply(lambda x: x > 20)
df.groupby('Group').filter(lambda x: x['Values'] > 20)['Values'].sum()
None of the above

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Question 9

What does this code output?

Python

import pandas as pd 
data = {'A': [1, 1, 2], 'B': [3, 4, 5]}
df = pd.DataFrame(data)
result = df.groupby('A').describe()
print(result)

B
count mean std min 25% 50% 75% max
A
1 2 3.5 0.5 3 3.25 3.5 3.75 4
2 1 5.0 NaN 5 5.00 5.0 5.00 5
B
count mean std min 25% 50% 75% max
A
1 2 3.5 0.707 3 3.25 3.5 3.75
2 1 5.0 NaN 5 5.00 5.0 5.00 5
A
1 2 3.5 0.707 3 3.25 3.5 3.75 4
2 1 5.0 NaN 5 5.00 5.0 5.00 5
None of the above

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Question 10

What does this code output?

Python

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
result = df[['A', 'B']].agg(['sum', 'mean'])
print(result)

A B
sum 6 15
mean 2 5
A B
sum 6 15
mean 2 5
A B
sum 6.0 15.0
mean 2.0 5.0
None of the above

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