Multiple conditions with & and | in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the & operator do in pandas when filtering data?

The & operator is used to combine multiple conditions with a logical AND, meaning both conditions must be true for the row to be selected.

beginner

How do you combine multiple conditions with OR in pandas?

Use the | operator to combine conditions with a logical OR, so if any condition is true, the row is selected.

intermediate

Why do you need to put each condition in parentheses when using & or | in pandas?

Parentheses ensure the correct order of operations because & and | have lower precedence than comparison operators like > or ==.

beginner

Example: What does df[(df['age'] > 20) & (df['score'] < 50)] do?

It selects rows where the 'age' is greater than 20 AND the 'score' is less than 50.

beginner

Can you use 'and' or 'or' instead of & and | for filtering pandas DataFrames?

No, 'and' and 'or' do not work element-wise on pandas Series. You must use & for AND and | for OR with parentheses.

Which operator is used for element-wise AND in pandas filtering?

A |

B and

C &

D &&

In pandas, & is used for element-wise AND. 'and' is not valid for Series.

What is the correct way to filter rows where 'age' > 30 OR 'score' < 70?

A df[(df['age'] > 30) | (df['score'] < 70)]

B df[df['age'] > 30 | df['score'] < 70]

C df[df['age'] > 30 or df['score'] < 70]

D df[(df['age'] > 30) and (df['score'] < 70)]

Parentheses are needed around each condition when using |. 'or' and 'and' do not work element-wise.

Why must each condition be in parentheses when using & or | in pandas?

A Because & and | have lower precedence than comparison operators

B To improve code readability only

C It is optional

D To avoid syntax errors unrelated to precedence

Parentheses ensure the comparisons happen before & or |, avoiding logic errors.

What happens if you use 'and' instead of '&' in pandas filtering?

A It works the same

B It raises an error

C It filters rows incorrectly

D It converts to '&' automatically

'and' cannot be used with pandas Series and raises a ValueError.

Which of these filters rows where 'status' is 'active' AND 'score' is above 80?

A df[df['status'] == 'active' and df['score'] > 80]

B df[df['status'] == 'active' & df['score'] > 80]

C df[(df['status'] == 'active') and (df['score'] > 80)]

D df[(df['status'] == 'active') & (df['score'] > 80)]

Each condition must be in parentheses and combined with & for AND.

Explain how to filter a pandas DataFrame using multiple conditions with & and | operators.

Think about how to combine true/false checks for each row.

Describe why parentheses are important when using multiple conditions in pandas filtering.

Consider how Python reads expressions step-by-step.

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.