Aggregation functions (sum, mean, count) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sum aggregation on DataFrame column

What is the output of the following Python code using pandas?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'values': [10, 20, 30, 40]})
result = df['values'].sum()
print(result)

A 40

B TypeError

C 25

D 100

💡 Hint

Sum adds all numbers in the column.

The sum of 10 + 20 + 30 + 40 is 100.

❓ data_output

intermediate

Count of non-null entries in DataFrame column

Given this DataFrame, what is the output of the count aggregation?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'scores': [5, None, 7, None, 9]})
result = df['scores'].count()
print(result)

A 5

B 3

C 2

D None

💡 Hint

Count counts only non-null values.

There are 3 non-null values: 5, 7, and 9.

❓ Predict Output

advanced

Mean aggregation with missing values

What is the output of this code calculating the mean?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'temps': [20, 25, None, 30]})
result = df['temps'].mean()
print(round(result, 2))

A None

B 18.75

C 25.0

D TypeError

💡 Hint

Mean ignores missing values by default.

The mean is (20 + 25 + 30) / 3 = 25.0.

❓ visualization

advanced

Bar chart of sum aggregation by group

Which option shows the correct bar chart output for sum of sales by region?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

data = {'region': ['East', 'West', 'East', 'West'], 'sales': [100, 200, 150, 300]}
df = pd.DataFrame(data)
sum_sales = df.groupby('region')['sales'].sum()
sum_sales.plot(kind='bar')
plt.show()

A Bar chart with East=250 and West=500

B Bar chart with East=100 and West=200

C Bar chart with East=150 and West=300

D Line chart with East=250 and West=500

💡 Hint

Group sums add sales per region.

East sales: 100 + 150 = 250; West sales: 200 + 300 = 500.

🧠 Conceptual

expert

Understanding count vs size in groupby

In pandas, what is the difference between using count() and size() after a groupby operation?

A <code>count()</code> counts non-null values per group; <code>size()</code> counts all rows per group including nulls.

B <code>count()</code> counts all rows including nulls; <code>size()</code> counts only non-null values.

C <code>count()</code> returns the sum of values; <code>size()</code> returns the mean.

D <code>count()</code> and <code>size()</code> are identical and interchangeable.

💡 Hint

Think about how missing data affects counts.

count() ignores nulls and counts only non-null entries per group, while size() counts all rows regardless of nulls.