Boolean indexing in Pandas

Introduction

Boolean indexing helps you pick rows or columns from data based on true or false conditions. It makes finding specific data easy and fast.

You want to find all sales above a certain amount in a sales report.

You need to select students who scored more than 80 in a test.

You want to filter out products that are out of stock from a list.

You want to see only the rows where temperature is below freezing.

You want to select customers from a specific city in your data.

Syntax

Pandas

filtered_data = data[condition]

data is your DataFrame.

condition is a boolean expression that returns True or False for each row.

Examples

Selects rows where the 'age' column is greater than 30.

Pandas

filtered = df[df['age'] > 30]

Selects rows where 'age' is 18 or less.

Pandas

young = df[df['age'] <= 18]

Selects rows where salary is above 50000 and department is 'Sales'.

Pandas

high_salary = df[(df['salary'] > 50000) & (df['department'] == 'Sales')]

Selects rows where 'in_stock' is False.

Pandas

not_in_stock = df[~(df['in_stock'])]

Sample Program

This code creates a small table of employees. Then it picks only those who are older than 30 years and prints them.

Pandas

import pandas as pd

data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David'],
    'age': [25, 17, 35, 45],
    'salary': [50000, 30000, 70000, 90000],
    'department': ['HR', 'IT', 'Sales', 'Sales']
}

df = pd.DataFrame(data)

# Select employees older than 30
older_than_30 = df[df['age'] > 30]
print(older_than_30)

Important Notes

Boolean indexing returns a new DataFrame with only the rows where the condition is True.

Use parentheses when combining multiple conditions with & (and) or | (or).

The ~ symbol means 'not' and flips True to False and vice versa.

Summary

Boolean indexing helps filter data easily using True/False conditions.

Use it to select rows that meet one or more conditions.

Remember to use parentheses when combining conditions.

Practice

1. What does boolean indexing in pandas do?

easy

A. Changes the data type of columns

B. Sorts the DataFrame by column values

C. Selects rows based on True/False conditions

D. Merges two DataFrames together

Solution

1. Step 1: Understand boolean indexing

   Boolean indexing uses True/False values to filter rows in a DataFrame.

2. Step 2: Identify its purpose

   It selects only rows where the condition is True, filtering data easily.

3. Final Answer:

   Selects rows based on True/False conditions -> Option C

4. Quick Check:

   Boolean indexing = row filtering [OK]

Hint: Boolean indexing filters rows with True conditions [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Mixing it up with DataFrame merging

2. Which of the following is the correct syntax to select rows where column 'age' is greater than 30 in a DataFrame df?

easy

A. df['age' > 30]

B. df[df['age'] > 30]

C. df[df.age = 30]

D. df[df['age'] => 30]

Solution

1. Step 1: Check correct boolean indexing syntax

   Use df[condition], where condition is df['age'] > 30.

2. Step 2: Identify syntax errors in other options

   df['age' > 30] misplaces brackets, C uses assignment (=) instead of comparison, D uses invalid operator =>.

3. Final Answer:

   df[df['age'] > 30] -> Option B

4. Quick Check:

   Correct syntax uses df[condition] with comparison [OK]

Hint: Use df[condition] with correct comparison operators [OK]

Common Mistakes:

• Using = instead of > for comparison
• Wrong bracket placement
• Using invalid operators like =>

3. Given the DataFrame df below:

   name  age  score
0  Anna   25     88
1  Bob    32     75
2  Cara   29     95
3  Dave   35     60
4  Ella   28     82

What will df[(df['age'] > 28) & (df['score'] > 70)] return?

medium

A. Rows with age > 28 and score > 70: Anna and Ella

B. Rows with age > 28 and score > 70: Bob and Dave

C. Rows with age > 28 and score > 70: Dave only

D. Rows with age > 28 and score > 70: Bob and Cara

Solution

1. Step 1: Identify rows where age > 28

   Rows: Bob (32), Cara (29), Dave (35) satisfy age > 28.

2. Step 2: From those, select rows with score > 70

   Bob (75), Cara (95), Dave (60). Dave fails score > 70, so only Bob and Cara remain.

3. Final Answer:

   Rows with age > 28 and score > 70: Bob and Cara -> Option D

4. Quick Check:

   Filter by both conditions = Bob and Cara [OK]

Hint: Check each condition separately, then combine results [OK]

Common Mistakes:

• Including Dave who has score 60
• Mixing up & and | operators
• Ignoring parentheses around conditions

4. What is wrong with this code snippet for filtering rows where score is above 80?

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

medium

A. Missing parentheses around conditions causing operator precedence error

B. Using & instead of | for combining conditions

C. Incorrect column name 'score'

D. DataFrame df is not defined

Solution

1. Step 1: Understand operator precedence in pandas boolean indexing

   Bitwise operators (&, |) require parentheses around each condition to avoid errors.

2. Step 2: Identify missing parentheses

   Code lacks parentheses around df['score'] > 80 and df['age'] < 30, causing wrong evaluation.

3. Final Answer:

   Missing parentheses around conditions causing operator precedence error -> Option A

4. Quick Check:

   Use parentheses with & and | [OK]

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

Common Mistakes:

• Forgetting parentheses around each condition
• Confusing & with logical and (and)
• Assuming no error without parentheses

5. You have a DataFrame df with columns 'city', 'temperature', and 'humidity'. You want to select rows where city is either 'Paris' or 'London' and temperature is above 20. Which code correctly applies boolean indexing?

hard

A. df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)]

B. df[df['city'] == 'Paris' | df['city'] == 'London' & (df['temperature'] > 20)]

C. df[df['city'] == ('Paris' or 'London') & df['temperature'] > 20]

D. df[(df['city'] == 'Paris') | (df['city'] == 'London') & (df['temperature'] > 20)]

Solution

1. Step 1: Understand condition grouping with parentheses

   Conditions combined with | must be wrapped in parentheses to group city checks correctly.

2. Step 2: Check each option for correct syntax and logic

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] correctly groups city conditions and combines with temperature condition using &.

3. Final Answer:

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] -> Option A

4. Quick Check:

   Group OR conditions with parentheses before AND [OK]

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

Common Mistakes:

• Missing parentheses causing wrong condition order
• Using Python 'or' instead of |
• Trying to compare city to ('Paris' or 'London') directly