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.

Practice

1. Which operator should you use in pandas to select rows where age is greater than 30 and salary is above 50000?

easy

A. Use and to combine the two conditions without parentheses.

B. Use | to combine the two conditions without parentheses.

C. Use & to combine the two conditions with parentheses around each.

D. Use or to combine the two conditions with parentheses.

Solution

1. Step 1: Understand the condition type

   We want rows where both conditions are true, so this is an AND condition.

2. Step 2: Choose the correct pandas operator

   In pandas, & is used for AND between conditions, and each condition must be in parentheses.

3. Final Answer:

   Use & to combine the two conditions with parentheses around each. -> Option C

4. Quick Check:

   AND condition = & with parentheses [OK]

Hint: Use & with parentheses for AND conditions in pandas [OK]

Common Mistakes:

• Using 'and' instead of & causes errors.
• Not wrapping each condition in parentheses.
• Using | instead of & for AND conditions.

2. Which of the following is the correct syntax to filter a DataFrame df for rows where df['A'] > 10 or df['B'] < 5?

easy

A. df[(df['A'] > 10) | (df['B'] < 5)]

B. df[(df['A'] > 10) & (df['B'] < 5)]

C. df[df['A'] > 10 | df['B'] < 5]

D. df[df['A'] > 10 or df['B'] < 5]

Solution

1. Step 1: Identify the condition type

   The question asks for rows where either condition is true, so this is an OR condition.

2. Step 2: Use correct pandas syntax for OR

   In pandas, | is used for OR, and each condition must be wrapped in parentheses inside the DataFrame filter.

3. Final Answer:

   df[(df['A'] > 10) | (df['B'] < 5)] -> Option A

4. Quick Check:

   OR condition = | with parentheses [OK]

Hint: Use | with parentheses for OR conditions in pandas [OK]

Common Mistakes:

• Using & instead of | for OR conditions.
• Not wrapping conditions in parentheses.
• Using Python 'or' instead of | causes errors.

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'age': [25, 40, 35, 28], 'salary': [40000, 60000, 50000, 45000]})
filtered = df[(df['age'] > 30) & (df['salary'] >= 50000)]
print(filtered)

What will be the output?

medium

A. Rows with age > 30 or salary >= 50000: rows 1, 2, and 3

B. Rows with age > 30 and salary >= 50000: rows 1 and 2

C. Rows with age > 30 and salary >= 50000: row 1 only

D. Empty DataFrame

Solution

1. Step 1: Check each row against conditions

   Row 0: age=25 (no), salary=40000 (no)
   Row 1: age=40 (yes), salary=60000 (yes)
   Row 2: age=35 (yes), salary=50000 (yes)
   Row 3: age=28 (no), salary=45000 (no)

2. Step 2: Select rows where both conditions are true

   Rows 1 and 2 satisfy both conditions.

3. Final Answer:

   Rows with age > 30 and salary >= 50000: rows 1 and 2 -> Option B

4. Quick Check:

   Both conditions true for rows 1 and 2 [OK]

Hint: Check each condition row-wise, then combine with & [OK]

Common Mistakes:

• Confusing & with | and selecting wrong rows.
• Ignoring parentheses causing syntax errors.
• Misreading >= as > or vice versa.

4. What is wrong with this pandas filter code?

df[(df['score'] > 80) & df['age'] < 30]

Choose the best fix.

medium

A. Use 'and' instead of &.

B. Replace & with | to fix the syntax.

C. Remove parentheses around the first condition.

D. Add parentheses around the second condition: df[(df['score'] > 80) & (df['age'] < 30)]

Solution

1. Step 1: Identify the syntax error

   The second condition df['age'] < 30 is not wrapped in parentheses, which is required when using &.

2. Step 2: Correct the syntax by adding parentheses

   Wrap the second condition in parentheses to fix the error: df[(df['score'] > 80) & (df['age'] < 30)]

3. Final Answer:

   Add parentheses around the second condition: df[(df['score'] > 80) & (df['age'] < 30)] -> Option D

4. Quick Check:

   Each condition must be in parentheses with & [OK]

Hint: Wrap each condition in parentheses when using & or | [OK]

Common Mistakes:

• Not using parentheses around all conditions.
• Using 'and' instead of &.
• Changing & to | without reason.

5. You have a DataFrame df with columns temperature and humidity. You want to select rows where temperature is between 20 and 30 inclusive, or humidity is below 40. Which of the following code snippets correctly applies these conditions?

hard

A. df[((df['temperature'] >= 20) & (df['temperature'] <= 30)) | (df['humidity'] < 40)]

B. df[(df['temperature'] >= 20) & df['temperature'] <= 30 | (df['humidity'] < 40)]

C. df[(df['temperature'] >= 20) | (df['temperature'] <= 30) & (df['humidity'] < 40)]

D. df[(df['temperature'] >= 20) & (df['humidity'] < 40) | (df['temperature'] <= 30)]

Solution

1. Step 1: Understand the condition logic

   We want rows where temperature is between 20 and 30 (both conditions combined with AND), OR humidity is below 40.

2. Step 2: Apply correct operator precedence with parentheses

   Use parentheses to group the temperature range conditions with & and then combine with | for humidity condition.

3. Final Answer:

   df[((df['temperature'] >= 20) & (df['temperature'] <= 30)) | (df['humidity'] < 40)] -> Option A

4. Quick Check:

   Group AND conditions, then OR with other condition [OK]

Hint: Group AND conditions in parentheses before OR [OK]

Common Mistakes:

• Missing parentheses causing wrong logic.
• Mixing & and | without grouping.
• Using | inside AND group incorrectly.