Comparison operations in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of element-wise comparison with numpy arrays

What is the output of this code snippet using numpy arrays for comparison?

NumPy

import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([2, 2, 2, 2])
result = arr1 > arr2
print(result)

A [False False True True]

B [True False False False]

C [True True True True]

D [False False False False]

💡 Hint

Compare each element of arr1 with the corresponding element in arr2.

The comparison arr1 > arr2 checks each element: 1>2 is False, 2>2 is False, 3>2 is True, 4>2 is True, resulting in [False False True True].

❓ data_output

intermediate

Count of elements equal to a value in numpy array

Given a numpy array, how many elements are equal to 5?

NumPy

import numpy as np
arr = np.array([5, 3, 5, 7, 5, 2])
count = np.sum(arr == 5)
print(count)

A 3

B 2

C 1

D 4

💡 Hint

Use element-wise comparison and sum the True values.

The expression arr == 5 creates a boolean array [True, False, True, False, True, False]. Summing True values (counted as 1) gives 3.

🔧 Debug

advanced

Identify the error in numpy comparison code

What error does this code raise?

NumPy

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

A No error, outputs [False True True]

B TypeError: '>' not supported between instances of 'numpy.ndarray' and 'int'

C SyntaxError: invalid syntax

D ValueError: The truth value of an array is ambiguous

💡 Hint

Check how chained comparisons work with numpy arrays.

The expression arr > 2 > 1 is evaluated as (arr > 2) > 1. The first part returns a boolean array, then Python tries to compare this array to 1, which is invalid and raises a ValueError.

🚀 Application

advanced

Filter numpy array elements using comparison

Which option correctly filters elements greater than 10 from the numpy array?

NumPy

import numpy as np
arr = np.array([5, 12, 7, 18, 3])
filtered = ???
print(filtered)

A arr.filter(arr > 10)

B arr > 10

C arr[arr > 10]

D np.filter(arr, arr > 10)

💡 Hint

Use boolean indexing to select elements.

Boolean indexing with arr[arr > 10] returns elements where the condition is True. Other options are invalid or return boolean arrays instead of filtered elements.

🧠 Conceptual

expert

Understanding broadcasting in numpy comparison

Given arr1 = np.array([1, 2, 3]) and arr2 = np.array([[2], [3], [4]]), what is the shape of the result of arr1 < arr2?

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([[2], [3], [4]])
result = arr1 < arr2
print(result.shape)

A (1, 3)

B (3, 3)

C (3,)

D (3, 1)

💡 Hint

Consider how numpy broadcasts shapes (3,) and (3,1) for element-wise comparison.

arr1 shape is (3,), arr2 shape is (3,1). Broadcasting expands arr1 to (1,3) and arr2 to (3,1), resulting in a (3,3) boolean array after comparison.