Boolean indexing in NumPy

Introduction

Boolean indexing helps you pick data from arrays by using True or False values. It makes finding and selecting data easy and fast.

You want to find all numbers greater than 10 in a list of numbers.

You need to select rows in a table where a condition is met, like ages over 18.

You want to filter out bad or missing data from your dataset.

You want to quickly find all items that match a rule without looping.

You want to change only certain values in an array based on a condition.

Syntax

NumPy

filtered_array = array[condition]

# where 'condition' is a boolean array of the same shape as 'array'

The condition inside the brackets must be a boolean array (True or False values).

The result is a new array with only the elements where the condition is True.

Examples

Selects all numbers greater than 5 from the array.

NumPy

import numpy as np
arr = np.array([1, 5, 10, 15])
result = arr[arr > 5]

Selects numbers divisible by 20 using a boolean mask.

NumPy

arr = np.array([10, 20, 30, 40])
mask = arr % 20 == 0
result = arr[mask]

Selects numbers greater than 2 and less than 5 using multiple conditions.

NumPy

arr = np.array([1, 2, 3, 4, 5])
result = arr[(arr > 2) & (arr < 5)]

Sample Program

This program filters the ages array to find all people who are 18 or older using boolean indexing.

NumPy

import numpy as np

# Create an array of ages
ages = np.array([12, 17, 19, 24, 15, 30])

# Select ages 18 or older
adults = ages[ages >= 18]

print("Original ages:", ages)
print("Adults (18 or older):", adults)

Important Notes

Boolean indexing does not change the original array unless you assign back to it.

You can combine multiple conditions using & (and), | (or), and ~ (not) with parentheses.

Boolean indexing works with multi-dimensional arrays too, selecting elements where the condition is True.

Summary

Boolean indexing uses True/False arrays to pick data from numpy arrays.

It is a fast and easy way to filter or select data based on conditions.

You can combine multiple conditions to select exactly what you want.

Practice

1. What does boolean indexing in numpy allow you to do?

easy

A. Create a new array filled with zeros

B. Change the data type of an array

C. Sort the array elements in ascending order

D. Select elements from an array using a True/False mask

Solution

1. Step 1: Understand boolean indexing concept

   Boolean indexing uses a True/False array to pick elements from another array.

2. Step 2: Identify the correct operation

   Only Select elements from an array using a True/False mask describes selecting elements based on True/False values.

3. Final Answer:

   Select elements from an array using a True/False mask -> Option D

4. Quick Check:

   Boolean indexing = Select elements [OK]

Hint: Boolean mask picks elements where True appears [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it creates new arrays with zeros

2. Which of the following is the correct syntax to select elements greater than 5 from a numpy array arr using boolean indexing?

easy

A. arr[arr < 5]

B. arr > 5[arr]

C. arr[arr > 5]

D. arr.select(arr > 5)

Solution

1. Step 1: Understand boolean indexing syntax

   The correct syntax is to use the array with a boolean mask inside square brackets: arr[condition].

2. Step 2: Check each option

   arr[arr > 5] uses arr[arr > 5], which is correct. Others have wrong order or invalid methods.

3. Final Answer:

   arr[arr > 5] -> Option C

4. Quick Check:

   Correct syntax = arr[condition] [OK]

Hint: Put condition inside brackets after array name [OK]

Common Mistakes:

• Placing condition before array name
• Using invalid methods like select()
• Using wrong comparison operators

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
result = arr[arr % 2 == 1]

medium

A. [7 9 1]

B. [2 4]

C. [7 4 9]

D. [2 7 4 9 1]

Solution

1. Step 1: Understand the condition arr % 2 == 1

   This condition selects elements where the remainder when divided by 2 is 1, i.e., odd numbers.

2. Step 2: Apply condition to array elements

   Elements 7, 9, and 1 are odd, so they are selected.

3. Final Answer:

   [7 9 1] -> Option A

4. Quick Check:

   Odd elements = [7 9 1] [OK]

Hint: Filter with modulo to get odd numbers [OK]

Common Mistakes:

• Selecting even numbers instead
• Confusing modulo operator
• Returning full array instead of filtered

4. The following code has a problem. What is the problem?

import numpy as np
arr = np.array([1, 2, 3, 4])
mask = arr > 2
print(arr[mask == True])

medium

A. mask == True is unnecessary and redundant

B. arr should be converted to list first

C. Comparison operator > is invalid for numpy arrays

D. Square brackets cannot be used for indexing

Solution

1. Step 1: Analyze the boolean mask creation

   mask = arr > 2 creates a boolean array correctly.

2. Step 2: Check the indexing expression

   Using mask == True is redundant since mask is already boolean. It's unnecessary and bad practice.

3. Step 3: Correct usage

   Use arr[mask] directly without comparing mask to True.

4. Final Answer:

   mask == True is unnecessary and redundant -> Option A

5. Quick Check:

   Use boolean mask directly [OK]

Hint: Use mask directly, no need for '== True' [OK]

Common Mistakes:

• Adding '== True' to boolean mask
• Trying to convert numpy array to list for indexing
• Misunderstanding comparison operators

5. Given a numpy array data = np.array([10, 15, 20, 25, 30]), how can you select elements greater than 12 and less than 28 using boolean indexing?

hard

A. data[data > 12 and data < 28]

B. data[(data > 12) & (data < 28)]

C. data[data > 12 | data < 28]

D. data[(data > 12) or (data < 28)]

Solution

1. Step 1: Understand combining conditions with & and parentheses

   In numpy, use & for element-wise AND and wrap each condition in parentheses.

2. Step 2: Check each option

   data[(data > 12) & (data < 28)] correctly uses & (element-wise AND) with parentheses around each condition. Using Python keywords and/or cause errors because they don't work element-wise on arrays. Using | (bitwise OR) selects elements greater than 12 or less than 28, which is wrong here.

3. Final Answer:

   data[(data > 12) & (data < 28)] -> Option B

4. Quick Check:

   Use & with parentheses for AND conditions [OK]

Hint: Use & with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' or 'or' instead of & or |
• Not using parentheses around conditions
• Using | (OR) instead of & (AND) for filtering