Logical operations (and, or, not) in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of combined logical operations on NumPy arrays

What is the output of this code snippet using NumPy logical operations?

NumPy

import numpy as np
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, False, True, True])
result = np.logical_and(np.logical_not(arr1), arr2)
print(result)

A [False False True False]

B [False True False False]

C [True False False False]

D [False False False True]

💡 Hint

Remember that logical_not flips True to False and vice versa, then logical_and returns True only if both are True.

np.logical_not(arr1) flips True to False and False to True. So it becomes [False, True, False, True]. Then logical_and with arr2 ([False, False, True, True]) results in True only where both are True, which is only at the last position.

❓ data_output

intermediate

Count of True values after logical OR operation

Given two boolean NumPy arrays, what is the count of True values after applying logical OR?

NumPy

import numpy as np
arr1 = np.array([True, False, False, True, False])
arr2 = np.array([False, True, False, False, True])
result = np.logical_or(arr1, arr2)
count_true = np.sum(result)
print(count_true)

A 4

B 3

C 5

D 2

💡 Hint

logical_or returns True if either element is True. Count how many positions have True after OR.

logical_or combines elements: [True or False, False or True, False or False, True or False, False or True] = [True, True, False, True, True]. Counting True values gives 4.

🔧 Debug

advanced

Identify the error in logical operation code

What error does this code raise when run?

NumPy

import numpy as np
arr1 = np.array([True, False, True])
arr2 = np.array([False, True])
result = np.logical_and(arr1, arr2)
print(result)

A IndexError: index out of bounds

B TypeError: unsupported operand type(s) for &: 'bool' and 'bool'

C ValueError: operands could not be broadcast together with shapes (3,) (2,)

D No error, outputs [False False False]

💡 Hint

Check if the arrays have the same shape for element-wise operations.

NumPy logical operations require arrays to have compatible shapes. Here, arr1 has shape (3,) and arr2 has shape (2,), so they cannot be broadcast together, causing a ValueError.

🚀 Application

advanced

Filter rows in a NumPy array using logical conditions

Which option correctly filters rows where the first column is greater than 5 AND the second column is less than 10?

NumPy

import numpy as np
data = np.array([[6, 9], [4, 11], [7, 8], [5, 10]])

A data[np.logical_and(data[:,0] > 5, data[:,1] < 10)]

B data[(data[:,0] > 5) & (data[:,1] < 10)]

C data[np.logical_or(data[:,0] > 5, data[:,1] < 10)]

D data[data[:,0] > 5 and data[:,1] < 10]

💡 Hint

Use element-wise logical AND with & and parentheses for conditions.

Option B uses element-wise & with parentheses, which is correct for NumPy arrays. Option B is also valid but less common; however, option B uses Python 'and' which does not work element-wise, and option B uses logical_or which is incorrect for the AND condition.

🧠 Conceptual

expert

Understanding short-circuit behavior in NumPy logical operations

Which statement about NumPy logical operations is TRUE?

A NumPy logical operations always evaluate all elements in the arrays without short-circuiting.

B NumPy logical operations only work on 1D arrays and raise errors on higher dimensions.

C NumPy logical operations raise an error if any element is False in the input arrays.

D NumPy logical operations like np.logical_and short-circuit and stop evaluating as soon as the result is known.

💡 Hint

Think about how NumPy processes arrays element-wise.

NumPy logical operations evaluate all elements element-wise and do not short-circuit like Python's built-in 'and' or 'or'. They return an array of results for all elements.