Boolean indexing for filtering in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Boolean Indexing on NumPy Array

What is the output of the following code snippet using NumPy boolean indexing?

NumPy

import numpy as np
arr = np.array([10, 15, 20, 25, 30])
filtered = arr[arr > 20]
print(filtered)

A [20 25 30]

B [15 20 25 30]

C [25 30]

D [10 15 20]

💡 Hint

Remember that boolean indexing selects elements where the condition is True.

The condition arr > 20 selects elements strictly greater than 20, which are 25 and 30.

❓ data_output

intermediate

Number of Elements After Boolean Filtering

How many elements remain after applying the boolean filter in the code below?

NumPy

import numpy as np
arr = np.array([5, 12, 17, 9, 3, 21, 14])
filtered = arr[(arr >= 10) & (arr <= 20)]
print(len(filtered))

A 3

B 5

C 4

D 2

💡 Hint

Count elements between 10 and 20 inclusive.

Elements >=10 and <=20 are 12, 17, 14, so 3 elements.

🔧 Debug

advanced

Identify the Error in Boolean Indexing

What error will the following code produce?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
filtered = arr[arr > 2 and arr < 5]
print(filtered)

A SyntaxError

B TypeError

C ValueError

D No error, outputs [3 4]

💡 Hint

Check how logical operators work with NumPy arrays.

The 'and' operator cannot be used with NumPy arrays for element-wise comparison. It raises a TypeError.

🚀 Application

advanced

Filter Rows in 2D NumPy Array Using Boolean Indexing

Given a 2D NumPy array representing data, which option correctly filters rows where the second column is greater than 50?

NumPy

import numpy as np
data = np.array([[10, 45], [20, 55], [30, 65], [40, 35]])
filtered = ???
print(filtered)

A data[data[:, 1] > 50]

B data[data[1] > 50]

C data[:, data[1] > 50]

D data[:, 1] > 50

💡 Hint

Use boolean indexing on rows by selecting the column first.

data[:, 1] selects the second column. Then data[data[:, 1] > 50] selects rows where that condition is True.

🧠 Conceptual

expert

Understanding Boolean Indexing Behavior with NaN Values

Consider the following NumPy array with NaN values. What will be the output of the boolean indexing operation?

NumPy

import numpy as np
arr = np.array([1.0, np.nan, 3.0, np.nan, 5.0])
filtered = arr[arr > 2]
print(filtered)

A Raises ValueError

B [nan 3. nan 5.]

C [3. nan 5.]

D [3. 5.]

💡 Hint

NaN compared with any number returns False in boolean indexing.

NaN compared with > 2 returns False, so NaNs are excluded. Only 3.0 and 5.0 remain.

Practice

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

easy

A. Select elements from an array based on True/False conditions

B. Sort an array in ascending order

C. Change the data type of an array

D. Calculate the sum of all elements in an array

Solution

1. Step 1: Understand boolean indexing concept

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

2. Step 2: Compare with other options

   Sorting, changing data type, and summing are different numpy operations, not boolean indexing.

3. Final Answer:

   Select elements from an array based on True/False conditions -> Option A

4. Quick Check:

   Boolean indexing = filtering by True/False [OK]

Hint: Boolean indexing picks elements where condition is True [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it calculates sums

2. Which of the following is the correct syntax to filter array arr for values greater than 5 using boolean indexing?

easy

A. arr > 5[arr]

B. arr[arr > 5]

C. arr.filter(arr > 5)

D. arr[arr < 5]

Solution

1. Step 1: Identify correct boolean indexing syntax

   In numpy, filtering uses arr[condition] where condition is a boolean array.

2. Step 2: Check each option

   arr[arr > 5] uses correct syntax. arr > 5[arr] is invalid syntax. arr.filter(arr > 5) is not a numpy method. arr[arr < 5] filters for less than 5, not greater.

3. Final Answer:

   arr[arr > 5] -> Option B

4. Quick Check:

   Correct syntax is arr[condition] [OK]

Hint: Use arr[condition] to filter arrays in numpy [OK]

Common Mistakes:

• Placing condition outside brackets
• Using non-existent filter method
• Mixing up greater than and less than

3. What is the output of the following code?

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

medium

A. [7 4 9]

B. [2 4]

C. [7 9 1]

D. [2 7 4 9 1]

Solution

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

   This condition selects odd numbers because odd numbers have remainder 1 when divided by 2.

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 C

4. Quick Check:

   Filter odd numbers = [7 9 1] [OK]

Hint: Use modulo (%) to filter odd/even numbers [OK]

Common Mistakes:

• Selecting even numbers instead of odd
• Including all elements without filtering
• Misunderstanding modulo operator

4. The following code throws an error. What is the mistake?

import numpy as np
arr = np.array([10, 15, 20, 25])
filtered = arr[arr > 15 and arr < 25]

medium

A. Using 'and' instead of '&' for element-wise condition

B. Missing parentheses around conditions

C. Using 'or' instead of 'and'

D. Array is not defined properly

Solution

1. Step 1: Identify boolean operator error

   In numpy, element-wise logical operations require '&' instead of Python's 'and'.

2. Step 2: Understand why 'and' causes error

   'and' expects single boolean, but arr > 15 and arr < 25 returns arrays, causing TypeError.

3. Final Answer:

   Using 'and' instead of '&' for element-wise condition -> Option A

4. Quick Check:

   Use '&' for element-wise logical AND [OK]

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

Common Mistakes:

• Using 'and' instead of '&' in numpy conditions
• Forgetting parentheses around each condition
• Assuming 'or' works like '|'

5. Given a numpy array data = np.array([3, 6, 9, 12, 15, 18]), how would you filter values that are divisible by 3 but not by 6 using boolean indexing?

hard

A. data[(data % 3 == 0) & (data % 6 == 0)]

B. data[(data % 3 == 0) | (data % 6 != 0)]

C. data[(data % 3 != 0) & (data % 6 == 0)]

D. data[(data % 3 == 0) & (data % 6 != 0)]

Solution

1. Step 1: Define conditions for filtering

   We want numbers divisible by 3 (data % 3 == 0) but not divisible by 6 (data % 6 != 0).

2. Step 2: Combine conditions with element-wise AND

   Use '&' to combine both conditions inside parentheses for correct boolean indexing.

3. Step 3: Apply combined condition to data array

   data[(data % 3 == 0) & (data % 6 != 0)] correctly applies both conditions with '&'. Others use wrong operators or conditions.

4. Final Answer:

   data[(data % 3 == 0) & (data % 6 != 0)] -> Option D

5. Quick Check:

   Use & and parentheses for combined conditions [OK]

Hint: Combine conditions with & and parentheses for filtering [OK]

Common Mistakes:

• Using | instead of & for AND condition
• Mixing up divisibility conditions
• Forgetting parentheses around each condition