Series arithmetic and alignment in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Series Addition with Different Indexes

What is the output of the following code?

import pandas as pd
s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5, 6], index=['b', 'c', 'd'])
result = s1 + s2
print(result)

Data Analysis Python

import pandas as pd
s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5, 6], index=['b', 'c', 'd'])
result = s1 + s2
print(result)

A

a    NaN
b    6.0
c    8.0
d    NaN
dtype: float64

B

a    5.0
b    6.0
c    8.0
d    6.0
dtype: int64

C

a    NaN
b    6
c    8
d    NaN
dtype: int64

D

a    1
b    6
c    8
d    6
dtype: int64

💡 Hint

Think about how pandas aligns indexes and what happens when indexes don't match.

When adding two Series, pandas aligns them by their index labels. If an index label is missing in one Series, the result is NaN for that label. The result is float64 because NaN is a float.

❓ data_output

intermediate

Number of Non-NaN Elements After Series Subtraction

Given two Series with different indexes, how many non-NaN values are in the result of their subtraction?

import pandas as pd
s1 = pd.Series([10, 20, 30], index=['x', 'y', 'z'])
s2 = pd.Series([5, 15], index=['y', 'z'])
result = s1 - s2
print(result.dropna().count())

Data Analysis Python

import pandas as pd
s1 = pd.Series([10, 20, 30], index=['x', 'y', 'z'])
s2 = pd.Series([5, 15], index=['y', 'z'])
result = s1 - s2
print(result.dropna().count())

A 2

B 0

C 1

D 3

💡 Hint

Check which indexes exist in both Series.

Only indexes 'y' and 'z' exist in both Series, so subtraction results in valid numbers for these two. Index 'x' has no match in s2, so result is NaN there.

🔧 Debug

advanced

Identify the Error in Series Multiplication

What error does the following code raise?

import pandas as pd
s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5], index=['b', 'c'])
result = s1 * s2
print(result['a'])

Data Analysis Python

import pandas as pd
s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
s2 = pd.Series([4, 5], index=['b', 'c'])
result = s1 * s2
print(result['a'])

A KeyError

B TypeError

C No error, prints NaN

D AttributeError

💡 Hint

Check what happens when you access a missing index in a Series.

The multiplication aligns indexes, so 'a' is missing in s2, resulting in NaN in result. Accessing result['a'] returns NaN, which prints fine. So no error here. But the question asks what error is raised. Actually, no error is raised when printing NaN. So option C is correct.

❓ visualization

advanced

Visualizing Series Addition with Missing Data

You have two Series with some overlapping and some unique indexes. Which plot best shows the result of their addition, highlighting NaN values?

import pandas as pd
import matplotlib.pyplot as plt
s1 = pd.Series([2, 4, 6], index=['p', 'q', 'r'])
s2 = pd.Series([1, 3, 5], index=['q', 'r', 's'])
result = s1 + s2
result.plot(kind='bar')
plt.show()

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
s1 = pd.Series([2, 4, 6], index=['p', 'q', 'r'])
s2 = pd.Series([1, 3, 5], index=['q', 'r', 's'])
result = s1 + s2
result.plot(kind='bar')
plt.show()

A Bar chart with bars for 'p', 'q', 'r', 's' where 'p' and 's' bars are missing or zero height

B Bar chart with bars for 'p', 'q', 'r', 's' where 'p' and 's' bars are NaN and not shown

C Line plot showing values for 'p', 'q', 'r', 's' with gaps at 'p' and 's'

D Scatter plot with points only for 'q' and 'r'

💡 Hint

Think about how matplotlib handles NaN values in bar charts.

When plotting a Series with NaN values using bar chart, bars for NaN indexes are not shown (missing). So 'p' and 's' bars will be missing because their values are NaN after addition.

🚀 Application

expert

Combining Multiple Series with Different Indexes

You have three Series representing sales in different regions with overlapping and unique dates as indexes. You want to create a single Series showing total sales per date, treating missing data as zero. Which code snippet achieves this correctly?

import pandas as pd
s1 = pd.Series([100, 200], index=['2024-01-01', '2024-01-02'])
s2 = pd.Series([150, 250], index=['2024-01-02', '2024-01-03'])
s3 = pd.Series([50, 100], index=['2024-01-01', '2024-01-03'])

# Which option correctly sums these Series treating missing values as zero?

Data Analysis Python

import pandas as pd
s1 = pd.Series([100, 200], index=['2024-01-01', '2024-01-02'])
s2 = pd.Series([150, 250], index=['2024-01-02', '2024-01-03'])
s3 = pd.Series([50, 100], index=['2024-01-01', '2024-01-03'])

# Which option correctly sums these Series treating missing values as zero?

A

total = s1 + s2 + s3
print(total.fillna(0))

B

total = pd.concat([s1, s2, s3]).sum()
print(total)

C

total = s1.combine(s2, lambda x, y: x + y).combine(s3, lambda x, y: x + y)
print(total)

D

total = s1.add(s2, fill_value=0).add(s3, fill_value=0)
print(total)

💡 Hint

Look for a method that allows adding Series with missing indexes treated as zero.

Using add() with fill_value=0 correctly sums Series treating missing indexes as zero. Option D sums first then fills NaN, which can cause wrong sums. Option D does not handle missing values properly. Option D concatenates Series vertically and sums all values, losing index alignment.