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.