Garbage collection and array references in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Effect of modifying a view on the original array

Consider the following code snippet using NumPy arrays:

import numpy as np
arr = np.array([1, 2, 3, 4])
view_arr = arr[1:3]
view_arr[0] = 10
print(arr)

What will be the output of the print statement?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
view_arr = arr[1:3]
view_arr[0] = 10
print(arr)

A [1 10 3 4]

B [1 2 3 4]

C [10 2 3 4]

D [1 10 10 4]

💡 Hint

Think about whether view_arr is a copy or a view of arr.

The slice arr[1:3] creates a view, not a copy. Modifying view_arr[0] changes arr[1]. So the array becomes [1, 10, 3, 4].

❓ query_result

intermediate

Effect of modifying a copy on the original array

What will be the output of this code?

import numpy as np
arr = np.array([5, 6, 7, 8])
copy_arr = arr.copy()
copy_arr[1] = 20
print(arr)

NumPy

import numpy as np
arr = np.array([5, 6, 7, 8])
copy_arr = arr.copy()
copy_arr[1] = 20
print(arr)

A [20 6 7 8]

B [5 6 7 8]

C [5 20 7 8]

D [5 6 20 8]

💡 Hint

Does modifying a copy affect the original array?

The copy() method creates a new array independent of the original. Changing copy_arr does not affect arr.

📝 Syntax

advanced

Identifying reference count changes with array assignment

Given the following code, which statement correctly describes the reference count behavior?

import numpy as np
arr = np.array([1, 2, 3])
ref1 = arr
ref2 = arr
ref1[0] = 10
print(arr)

NumPy

import numpy as np
arr = np.array([1, 2, 3])
ref1 = arr
ref2 = arr
ref1[0] = 10
print(arr)

A ref1 is a copy; modifying it does not affect arr or ref2.

B ref2 is a copy; modifying ref1 affects arr but not ref2.

C arr is copied when assigned to ref1 and ref2; changes are independent.

D All variables point to the same array; modifying ref1 changes arr and ref2.

💡 Hint

Think about what happens when you assign arrays to new variables without copying.

Assigning arr to ref1 and ref2 creates references to the same array. Changing ref1[0] changes the original array, so arr and ref2 see the change.

🔧 Debug

advanced

Why does the original array change unexpectedly?

Consider this code:

import numpy as np
arr = np.array([1, 2, 3, 4])
new_arr = arr[::2]
new_arr[0] = 100
print(arr)

Why does arr change even though slicing looks like it creates a new array?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
new_arr = arr[::2]
new_arr[0] = 100
print(arr)

A The assignment to new_arr creates a new array independent of arr.

B Slicing always creates a copy, so arr should not change.

C Slicing with steps creates a view, so changes affect the original array.

D The print statement is incorrect and does not show the real arr.

💡 Hint

Check if slicing with steps returns a view or a copy.

In NumPy, slicing with steps returns a view, not a copy. So modifying new_arr changes the original arr.

🧠 Conceptual

expert

Garbage collection and array references in NumPy

Which statement best describes how Python's garbage collector handles NumPy arrays with multiple references?

A The array memory is freed only when all references to it are deleted or go out of scope.

B The array memory is freed immediately when a new reference is created.

C NumPy arrays are never garbage collected and always stay in memory.

D Garbage collection frees arrays as soon as any reference is deleted, regardless of others.

💡 Hint

Think about how reference counting works in Python.

Python uses reference counting for garbage collection. The memory for a NumPy array is freed only when no references remain.