Selecting rows by condition in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of filtering rows with multiple conditions

What is the output of this code snippet that filters rows where age is greater than 30 and score is at least 80?

Pandas

import pandas as pd

data = {'name': ['Anna', 'Bob', 'Cara', 'Dan'], 'age': [25, 35, 40, 28], 'score': [88, 75, 90, 85]}
df = pd.DataFrame(data)

result = df[(df['age'] > 30) & (df['score'] >= 80)]
print(result)

A

Empty DataFrame
Columns: [name, age, score]
Index: []

B

   name  age  score
2  Cara   40     90

C

   name  age  score
0  Anna   25     88
3  Dan    28     85

D

   name  age  score
1  Bob    35     75
2  Cara   40     90

💡 Hint

Remember to use & for 'and' conditions and parentheses around each condition.

The code filters rows where age > 30 and score >= 80. Only Cara (age 40, score 90) meets both conditions.

❓ data_output

intermediate

Number of rows after filtering by string condition

Given this DataFrame, how many rows remain after selecting rows where the 'city' column contains the substring 'York'?

Pandas

import pandas as pd

data = {'name': ['Alice', 'Brian', 'Clara', 'David'], 'city': ['New York', 'Yorktown', 'Boston', 'Newark']}
df = pd.DataFrame(data)

filtered = df[df['city'].str.contains('York')]
print(len(filtered))

A 2

B 3

C 0

D 1

💡 Hint

Check which city names include the substring 'York'.

Both 'New York' and 'Yorktown' contain 'York', so 2 rows remain.

🔧 Debug

advanced

Identify the error in filtering rows with OR condition

What error does this code produce when trying to filter rows where 'score' is less than 50 or 'age' is greater than 60?

Pandas

import pandas as pd

data = {'name': ['Eve', 'Frank', 'Grace'], 'age': [45, 65, 70], 'score': [55, 40, 30]}
df = pd.DataFrame(data)

result = df[df['score'] < 50 or df['age'] > 60]
print(result)

A SyntaxError

B Empty DataFrame

C ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

D Correct output with filtered rows

💡 Hint

Check how to combine multiple conditions on pandas Series.

Using 'or' between Series causes a ValueError. Use bitwise operator '|' with parentheses instead.

❓ visualization

advanced

Visualizing filtered data distribution

Which plot correctly shows the count of rows where 'category' is 'A' or 'B' from this DataFrame?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'category': ['A', 'B', 'C', 'A', 'B', 'C', 'A'], 'value': [10, 20, 30, 40, 50, 60, 70]}
df = pd.DataFrame(data)

filtered = df[df['category'].isin(['A', 'B'])]
counts = filtered['category'].value_counts()

counts.plot(kind='bar')
plt.show()

A Bar chart with two bars labeled 'A' and 'B' showing counts 3 and 2 respectively

B Pie chart showing categories 'A', 'B', and 'C' with counts 3, 2, and 2

C Line plot showing values over index for all categories

D Scatter plot of 'value' vs 'category' for all rows

💡 Hint

Check which categories are included after filtering and what value_counts returns.

Filtering keeps only 'A' and 'B'. value_counts counts 3 for 'A' and 2 for 'B'. The bar chart shows these counts.

🚀 Application

expert

Selecting rows with complex condition and calculating mean

Given this DataFrame, what is the mean 'score' of rows where 'age' is between 20 and 40 (inclusive) and 'passed' is True?

Pandas

import pandas as pd

data = {'name': ['Ian', 'Jane', 'Kyle', 'Lara', 'Mona'],
        'age': [22, 35, 41, 28, 39],
        'score': [88, 92, 85, 79, 95],
        'passed': [True, False, True, True, False]}
df = pd.DataFrame(data)

filtered = df[(df['age'] >= 20) & (df['age'] <= 40) & (df['passed'])]
mean_score = filtered['score'].mean()
print(round(mean_score, 2))

A 88.5

B 81.0

C 85.67

D 83.5

💡 Hint

Filter rows carefully, then calculate the average of the 'score' column.

Rows matching conditions: Ian (22, True, 88), Lara (28, True, 79). Mean score = (88 + 79) / 2 = 83.5.