Multiple conditions with & and | in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of filtering DataFrame with multiple conditions

What is the output DataFrame after applying the filter with multiple conditions using & and | operators?

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Charlie', 'David', 'Eva'],
        'Age': [23, 35, 45, 35, 23],
        'Score': [88, 92, 85, 70, 88]}
df = pd.DataFrame(data)

filtered = df[(df['Age'] > 30) & ((df['Score'] > 80) | (df['Name'] == 'David'))]
print(filtered)

A

    Name  Age  Score
3  David   35     70

B

    Name  Age  Score
1    Bob   35     92
2 Charlie   45     85

C

    Name  Age  Score
1    Bob   35     92
2 Charlie   45     85
3  David   35     70

D

Empty DataFrame
Columns: [Name, Age, Score]
Index: []

💡 Hint

Remember to use parentheses around each condition when combining with & and | in pandas.

The filter selects rows where Age is greater than 30 AND (Score is greater than 80 OR Name is David). Bob and Charlie have Age > 30 and Score > 80. David has Age > 30 and Name == David, so he is included despite Score being 70.

❓ data_output

intermediate

Count rows matching complex conditions

How many rows in the DataFrame satisfy the condition: (Age < 40) | (Score >= 90) and Name starts with 'A' or 'E'?

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Charlie', 'David', 'Eva'],
        'Age': [23, 35, 45, 35, 23],
        'Score': [88, 92, 85, 70, 88]}
df = pd.DataFrame(data)

condition = ((df['Age'] < 40) | (df['Score'] >= 90)) & (df['Name'].str.startswith(('A','E')))
count = df[condition].shape[0]
print(count)

A 3

B 2

C 1

D 4

💡 Hint

Check which names start with A or E first, then apply the combined numeric conditions.

Anna and Eva start with A or E. Both have Age < 40 or Score >= 90. So count is 2.

🔧 Debug

advanced

Identify the error in filtering with multiple conditions

What error does the following code raise when filtering a DataFrame with multiple conditions using & and | without parentheses?

Pandas

import pandas as pd

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

filtered = df[df['A'] > 1 & df['B'] < 6]
print(filtered)

A TypeError: unsupported operand type(s) for &: 'int' and 'Series'

B SyntaxError: invalid syntax

C ValueError: The truth value of a Series is ambiguous

D No error, prints filtered DataFrame

💡 Hint

Operator precedence matters when combining conditions in pandas.

Without parentheses, Python evaluates df['A'] > 1 & df['B'] < 6 as df['A'] > (1 & df['B']) < 6, causing a TypeError because 1 & df['B'] tries to bitwise AND int and Series.

❓ visualization

advanced

Visualize filtered data with multiple conditions

Which plot correctly shows the filtered data points where (Age >= 30) & (Score < 90) from the DataFrame?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Name': ['Anna', 'Bob', 'Charlie', 'David', 'Eva'],
        'Age': [23, 35, 45, 35, 23],
        'Score': [88, 92, 85, 70, 88]}
df = pd.DataFrame(data)

filtered = df[(df['Age'] >= 30) & (df['Score'] < 90)]

plt.scatter(filtered['Age'], filtered['Score'])
plt.xlabel('Age')
plt.ylabel('Score')
plt.title('Filtered Data Points')
plt.show()

A Scatter plot with points at (23,88), (35,70), (45,85)

B Scatter plot with points at (23,88), (35,92), (23,88)

C Scatter plot with points at (35,92), (45,85), (23,88)

D Scatter plot with points at (35,85), (45,85), (35,70)

💡 Hint

Check which rows satisfy Age >= 30 and Score < 90 before plotting.

Rows with Age 35 and Score 85 (Bob), Age 45 and Score 85 (Charlie), and Age 35 and Score 70 (David) satisfy the condition and appear in the scatter plot.

🚀 Application

expert

Apply multiple conditions to categorize data

Given a DataFrame with columns 'Temperature' and 'Humidity', which code correctly creates a new column 'Weather' with values 'Hot & Dry', 'Hot & Humid', 'Cold' based on these conditions? - 'Hot & Dry' if Temperature > 30 and Humidity < 50 - 'Hot & Humid' if Temperature > 30 and Humidity >= 50 - 'Cold' otherwise

Pandas

import pandas as pd

data = {'Temperature': [35, 40, 25, 32, 28], 'Humidity': [45, 55, 60, 40, 70]}
df = pd.DataFrame(data)

# Fill in the code below

A

df['Weather'] = 'Cold'
df.loc[(df['Temperature'] > 30) & (df['Humidity'] < 50), 'Weather'] = 'Hot & Dry'
df.loc[(df['Temperature'] > 30) & (df['Humidity'] >= 50), 'Weather'] = 'Hot & Humid'
print(df)

B

df['Weather'] = df.apply(lambda row: 'Hot & Dry' if row['Temperature'] > 30 and row['Humidity'] < 50 else 'Hot & Humid' if row['Temperature'] > 30 and row['Humidity'] >= 50 else 'Cold', axis=1)
print(df)

C

df['Weather'] = pd.cut(df['Temperature'], bins=[-float('inf'),30,float('inf')], labels=['Cold','Hot'])
df.loc[df['Humidity'] < 50, 'Weather'] = 'Hot & Dry'
df.loc[df['Humidity'] >= 50, 'Weather'] = 'Hot & Humid'
print(df)

D

df['Weather'] = ['Hot & Dry' if (t > 30 and h < 50) else 'Hot & Humid' if (t > 30 and h >= 50) else 'Cold' for t, h in zip(df['Temperature'], df['Humidity'])]
print(df)

💡 Hint

Use vectorized conditions with loc for better performance and clarity.

Option A initializes all as 'Cold' then updates rows matching conditions using loc with & and | operators correctly. Option A uses list comprehension but is less efficient. Option A uses apply which is slower. Option A misassigns labels ignoring humidity properly.