np.take() and np.put() for advanced selection in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.take() with repeated indices

What is the output of the following code snippet?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
indices = [1, 3, 1, 4]
result = np.take(arr, indices)
print(result)

A [20 40 20 50]

B [10 30 10 50]

C [20 40 40 50]

D [10 20 30 40]

💡 Hint

np.take selects elements from the array at the given indices, including repeats.

np.take(arr, indices) returns elements at positions 1, 3, 1, and 4 from arr, which are 20, 40, 20, and 50 respectively.

❓ data_output

intermediate

Result of np.put() modifying array elements

Given the code below, what is the final state of the array 'arr' after np.put() is called?

NumPy

import numpy as np
arr = np.array([5, 10, 15, 20, 25])
indices = [0, 2, 4]
values = [50, 150, 250]
np.put(arr, indices, values)
print(arr)

A [50 10 150 20 250]

B [5 10 15 20 25]

C [50 150 250 20 25]

D [250 10 150 20 50]

💡 Hint

np.put replaces elements at specified indices with given values.

np.put replaces arr[0] with 50, arr[2] with 150, and arr[4] with 250, resulting in [50, 10, 150, 20, 250].

🔧 Debug

advanced

Identify the error in np.put usage

What error will the following code produce when executed?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
indices = [0, 1, 2]
values = [10, 20]
np.put(arr, indices, values)

A No error, array modified successfully

B IndexError: index 3 is out of bounds for axis 0 with size 4

C TypeError: 'list' object is not callable

D ValueError: could not broadcast input array from shape (2,) into shape (3,)

💡 Hint

Check if the length of values matches the length of indices.

np.put expects the values array to be broadcastable to the shape of indices. Here, values has length 2 but indices length 3, causing a ValueError.

🚀 Application

advanced

Using np.take() and np.put() to swap elements

You want to swap the elements at positions 1 and 3 in the array below using np.take() and np.put(). Which code snippet correctly performs this swap?

NumPy

import numpy as np
arr = np.array([100, 200, 300, 400, 500])

A

temp = np.take(arr, [1, 3])
np.put(arr, [3, 1], temp)

B

temp = np.take(arr, [1, 3])
np.put(arr, [1, 3], temp[::-1])

C np.put(arr, [1, 3], [arr[3], arr[1]])

D arr[[1, 3]] = arr[[3, 1]]

💡 Hint

Use np.take to get values, then np.put with reversed values to swap.

Option B correctly takes elements at indices 1 and 3, reverses them, and puts them back at the same indices, swapping the values.

🧠 Conceptual

expert

Effect of mode parameter in np.put()

Consider the array and code below. What will be the final array after executing np.put() with mode='wrap'?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
indices = [0, 5, 7]
values = [10, 20, 30]
np.put(arr, indices, values, mode='wrap')
print(arr)

A [10 20 30 4 5]

B IndexError: index 5 is out of bounds for axis 0 with size 5

C [20 2 30 4 5]

D [10 2 3 4 5]

💡 Hint

mode='wrap' wraps indices around the array length.

Indices 5 and 7 wrap to 0 and 2 respectively (5 % 5 = 0, 7 % 5 = 2). So values are put at positions 0, 0, and 2 in order, last write wins at position 0.