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.

Practice

1. What happens when you add two pandas Series with different labels using + operator?

easy

A. The result is a Series with all zeros

B. Values are added ignoring labels, just by position

C. An error is raised due to label mismatch

D. Values are added only where labels match; others become NaN

Solution

1. Step 1: Understand pandas Series label alignment

   When adding two Series, pandas aligns data by their labels (index) before performing arithmetic.

2. Step 2: Effect of label mismatch on addition

   If a label is missing in one Series, the result for that label is NaN because pandas cannot add missing data.

3. Final Answer:

   Values are added only where labels match; others become NaN -> Option D

4. Quick Check:

   Series addition aligns labels = Values are added only where labels match; others become NaN [OK]

Hint: Remember: pandas adds by matching labels, not positions [OK]

Common Mistakes:

• Assuming addition ignores labels and adds by position
• Expecting an error on label mismatch
• Thinking missing labels fill with zero automatically

2. Which of the following is the correct syntax to add two pandas Series s1 and s2 with label alignment, allowing specification of fill_value for missing labels?

easy

A. s1 + s2

B. s1 + s2.values

C. s1.add(s2)

D. s1.append(s2)

Solution

1. Step 1: Check syntax for adding Series with alignment and fill_value

   Using s1.add(s2) adds Series with label alignment and allows fill_value for missing labels.

2. Step 2: Compare other options

   s1 + s2 adds with alignment but no fill_value option. s1 + s2.values ignores labels (positional). s1.append(s2) concatenates.

3. Final Answer:

   s1.add(s2) -> Option C

4. Quick Check:

   Use .add() for aligned addition with fill_value [OK]

Hint: Use .add() method for safe label-aligned addition [OK]

Common Mistakes:

• Using + with raw values ignoring labels
• Confusing append with addition
• Not knowing .add() exists for Series

3. Given the code:

import pandas as pd
s1 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
s2 = pd.Series([1, 3, 3], index=['b', 'c', 'd'])
result = s1 + s2
print(result)

What is the output?

medium

A. a NaN
b 21.0
c 33.0
d NaN

B. a 10
b 22
c 33
d 3

C. a 10
b 21
c 33
d 3

D. a NaN
b 22.0
c 33.0
d NaN

Solution

1. Step 1: Align Series by labels

   s1 has 'a':10, 'b':20, 'c':30; s2 has 'b':1, 'c':3, 'd':3. Union index: 'a','b','c','d'.

2. Step 2: Add values where labels match, NaN otherwise

   'a': 10 + NaN = NaN, 'b':20+1=21.0, 'c':30+3=33.0, 'd': NaN + 3 = NaN. Floats due to NaN.

3. Final Answer:

   a NaN b 21.0 c 33.0 d NaN -> Option A

4. Quick Check:

   Series addition aligns labels, unmatched get NaN [OK]

Hint: Add Series and check labels; unmatched labels become NaN [OK]

Common Mistakes:

• Adding values by position ignoring labels
• Expecting no NaN for unmatched labels
• Misreading index labels and values

4. Consider the code:

import pandas as pd
s1 = pd.Series([5, 10], index=['x', 'y'])
s2 = pd.Series([2, 4, 6], index=['x', 'y', 'z'])
result = s1 + s2
print(result)

The result contains NaN. What is the problem and how to fix it to treat missing values as zero?

medium

A. No error; code runs fine

B. NaN due to different lengths; fix by using s1.add(s2, fill_value=0)

C. Error because + cannot add Series; use concat instead

D. Error due to missing index in s1; fix by reindexing s1 to s2's index

Solution

1. Step 1: Check the code execution

   The code runs but produces NaN for 'z' (missing in s1). Alignment by labels causes NaN for unmatched.

2. Step 2: Fix with fill_value

   s1.add(s2, fill_value=0) fills missing labels with 0: 'x':7, 'y':14, 'z':6.

3. Final Answer:

   NaN due to different lengths; fix by using s1.add(s2, fill_value=0) -> Option B

4. Quick Check:

   Use .add() with fill_value to avoid NaN [OK]

Hint: Use .add(fill_value=0) to handle missing labels safely [OK]

Common Mistakes:

• Thinking + raises error on length mismatch
• Using concat instead of arithmetic
• Ignoring missing labels causing NaN

5. You have two Series representing sales in two stores:

store1 = pd.Series([100, 200, 300], index=['apple', 'banana', 'orange'])
store2 = pd.Series([50, 150, 100], index=['banana', 'orange', 'grape'])

How do you create a Series showing total sales per fruit, treating missing fruits as zero?

hard

A. store1.add(store2, fill_value=0)

B. store1 + store2

C. store1.append(store2).sum()

D. store1.combine(store2, lambda x, y: x + y)

Solution

1. Step 1: Understand the problem

   We want to add sales per fruit, but some fruits are missing in one store's Series.

2. Step 2: Use addition with fill_value=0

   Using store1.add(store2, fill_value=0) adds values label-wise, treating missing labels as zero, so no NaN appears.

3. Final Answer:

   store1.add(store2, fill_value=0) -> Option A

4. Quick Check:

   Add Series with fill_value=0 to handle missing labels [OK]

Hint: Use .add(fill_value=0) to sum with missing labels as zero [OK]

Common Mistakes:

• Using + causing NaN for missing labels
• Using append and sum incorrectly
• Using combine without fill_value causing NaN