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.

Practice

1. What does the numpy expression arr == 5 return when arr is a numpy array?

easy

A. The sum of all elements equal to 5

B. An array of True/False values showing where elements equal 5

C. The count of elements equal to 5

D. A single True or False value

Solution

1. Step 1: Understand element-wise comparison

   The expression arr == 5 compares each element of the array to 5 individually.

2. Step 2: Result type of comparison

   This returns a new array of the same shape with True where the element equals 5, and False otherwise.

3. Final Answer:

   An array of True/False values showing where elements equal 5 -> Option B

4. Quick Check:

   Element-wise comparison = Boolean array [OK]

Hint: Remember == returns True/False array element-wise [OK]

Common Mistakes:

• Thinking it returns a count or sum
• Expecting a single True/False value
• Confusing == with assignment =

2. Which of the following is the correct syntax to check if elements in numpy array arr are greater than 10?

easy

A. arr >> 10

B. arr => 10

C. arr > 10

D. arr >= 10

Solution

1. Step 1: Identify correct comparison operator

   The operator for 'greater than' is >, so arr > 10 checks elements greater than 10.

2. Step 2: Check other options

   arr => 10 is invalid syntax, arr >> 10 is a bitwise shift, and arr >= 10 checks greater or equal, not strictly greater.

3. Final Answer:

   arr > 10 -> Option C

4. Quick Check:

   Greater than operator is > [OK]

Hint: Use > for greater than, >= for greater or equal [OK]

Common Mistakes:

• Using => instead of >
• Confusing bitwise >> with comparison
• Using >= when > is asked

3. What is the output of the following code?

import numpy as np
arr = np.array([3, 7, 2, 9])
result = arr >= 5
print(result)

medium

A. [False False False False]

B. [True False True False]

C. [True True True True]

D. [False True False True]

Solution

1. Step 1: Compare each element with 5

   Elements: 3, 7, 2, 9. Check if each is >= 5: 3>=5 is False, 7>=5 is True, 2>=5 is False, 9>=5 is True.

2. Step 2: Form boolean array

   The result is [False, True, False, True].

3. Final Answer:

   [False True False True] -> Option D

4. Quick Check:

   Element-wise >= 5 = [False True False True] [OK]

Hint: Check each element against 5 carefully [OK]

Common Mistakes:

• Mixing up >= with >
• Misreading element values
• Expecting integer output instead of boolean

4. Identify the error in this numpy comparison code:

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

medium

A. Using single = instead of == for comparison

B. Array must be converted to list before comparison

C. print() cannot display numpy arrays

D. Missing parentheses in print statement

Solution

1. Step 1: Check comparison syntax

   The code uses arr = 2 which assigns 2 to arr, not compares. Comparison needs ==.

2. Step 2: Understand effect of assignment

   Assignment overwrites arr with 2, so print(result) prints 2, not a boolean array.

3. Final Answer:

   Using single = instead of == for comparison -> Option A

4. Quick Check:

   Comparison needs ==, not = [OK]

Hint: Use == for comparison, = is assignment [OK]

Common Mistakes:

• Confusing = and ==
• Thinking print can't show arrays
• Believing arrays must convert to lists

5. Given two numpy arrays:

import numpy as np
arr1 = np.array([1, 4, 6, 8])
arr2 = np.array([2, 4, 5, 10])

Which expression returns a boolean array showing where elements in arr1 are less than or equal to elements in arr2?

hard

A. arr1 <= arr2

B. arr1 < arr2

C. arr1 == arr2

D. arr1 >= arr2

Solution

1. Step 1: Understand element-wise comparison between arrays

   Comparing two arrays with operators like <= compares each element pair at the same index.

2. Step 2: Identify operator for 'less than or equal'

   The operator <= checks if elements in arr1 are less than or equal to corresponding elements in arr2.

3. Final Answer:

   arr1 <= arr2 -> Option A

4. Quick Check:

   Use <= for less or equal element-wise [OK]

Hint: Use <= to compare arrays element-wise for less or equal [OK]

Common Mistakes:

• Using < instead of <= when equal counts
• Using == which only checks equality
• Using >= which is opposite