Arithmetic operations on columns in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding two DataFrame columns

What is the output of this code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})
df['C'] = df['A'] + df['B']
print(df['C'].tolist())

A [4, 7, 9]

B [6, 7, 8]

C [1, 2, 3]

D [5, 7, 9]

💡 Hint

Adding two columns sums their values row-wise.

The new column 'C' is created by adding corresponding elements of columns 'A' and 'B'. So, 1+4=5, 2+5=7, 3+6=9.

❓ data_output

intermediate

Result of subtracting columns with missing values

Given this DataFrame, what is the output of subtracting column 'B' from 'A'?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [10, np.nan, 30],
    'B': [1, 2, np.nan]
})
df['D'] = df['A'] - df['B']
print(df['D'].tolist())

A [9, 0, 30]

B [9.0, -2.0, 30.0]

C [9.0, nan, nan]

D [nan, nan, nan]

💡 Hint

Subtracting with NaN results in NaN for that row.

When subtracting, if either value is NaN, the result is NaN. Only the first row has both numbers, so 10-1=9.0; others are NaN.

❓ visualization

advanced

Visualizing product of two columns

Which plot correctly shows the product of columns 'X' and 'Y' as a new column 'Z'?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'X': [2, 4, 6],
    'Y': [1, 3, 5]
})
df['Z'] = df['X'] * df['Y']
plt.bar(df.index, df['Z'])
plt.xlabel('Index')
plt.ylabel('Product Z')
plt.title('Bar plot of product of X and Y')
plt.show()

A Bar plot with heights [2, 12, 30]

B Histogram of values [1, 3, 5]

C Scatter plot with points (2,1), (4,3), (6,5)

D Line plot with points [2, 7, 11]

💡 Hint

The product of X and Y is calculated element-wise.

The product column Z is [2*1=2, 4*3=12, 6*5=30]. The bar plot shows these values as heights.

🔧 Debug

advanced

Identify error in column division code

What error does this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [10, 20, 30], 'B': [2, 0, 5]})
df['C'] = df['A'] / df['B']
print(df['C'].tolist())

A ZeroDivisionError

B [5.0, inf, 6.0]

C TypeError

D [5.0, nan, 6.0]

💡 Hint

Division by zero in pandas results in inf, not an error.

Pandas returns inf for division by zero, so the second element is inf, not an error or NaN.

🧠 Conceptual

expert

Effect of broadcasting in arithmetic operations on columns

Given a DataFrame df with columns 'A' and 'B', what happens when you run df['C'] = df['A'] + 5?

A Adds 5 to each element in column 'A' and stores in 'C'

B Adds 5 to the entire column 'A' as a single value and stores in 'C'

C Raises a TypeError because 5 is not a column

D Creates column 'C' with all values equal to 5

💡 Hint

Pandas broadcasts scalar values across all rows in a column.

Adding a scalar to a column adds that scalar to each element individually.