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.

Practice

1. What does np.take() do in NumPy?

easy

A. Selects elements from an array using specified indices

B. Changes elements in an array at specified indices

C. Sorts the elements of an array

D. Creates a new array filled with zeros

Solution

1. Step 1: Understand the function purpose

   np.take() is used to pick elements from an array by their index positions.

2. Step 2: Compare with other options

   Only Selects elements from an array using specified indices describes selecting elements by indices, while others describe different functions.

3. Final Answer:

   Selects elements from an array using specified indices -> Option A

4. Quick Check:

   np.take() = select elements [OK]

Hint: np.take() picks by index, np.put() changes by index [OK]

Common Mistakes:

• Confusing np.take() with np.put()
• Thinking np.take() modifies the array
• Mixing np.take() with sorting functions

2. Which of the following is the correct syntax to replace elements at indices [1, 3] in array a with values [10, 20] using np.put()?

easy

A. np.put(a, [1, 3], [10, 20])

B. np.put([1, 3], a, [10, 20])

C. np.put(a, [10, 20], [1, 3])

D. np.put(a, 10, [1, 3, 20])

Solution

1. Step 1: Recall np.put() syntax

   The correct syntax is np.put(array, indices, values) to replace elements at given indices.

2. Step 2: Match syntax with options

   np.put(a, [1, 3], [10, 20]) matches the correct order: array, indices, values.

3. Final Answer:

   np.put(a, [1, 3], [10, 20]) -> Option A

4. Quick Check:

   np.put(array, indices, values) [OK]

Hint: np.put(array, indices, values) replaces elements [OK]

Common Mistakes:

• Swapping the order of arguments
• Passing values as indices
• Using wrong data types for indices or values

3. What is the output of the following code?

import numpy as np
a = np.array([5, 10, 15, 20, 25])
indices = [0, 2, 4]
result = np.take(a, indices)
print(result)

medium

A. [10 20 25]

B. [5 15 25]

C. [5 10 15]

D. [0 2 4]

Solution

1. Step 1: Understand np.take() with given indices

   np.take(a, [0, 2, 4]) selects elements at positions 0, 2, and 4 from array a.

2. Step 2: Extract elements from array

   Elements at these positions are 5 (index 0), 15 (index 2), and 25 (index 4).

3. Final Answer:

   [5 15 25] -> Option B

4. Quick Check:

   np.take() picks elements by index [OK]

Hint: np.take() returns elements at given indices [OK]

Common Mistakes:

• Confusing indices with values
• Expecting output as original array
• Mixing up index positions

4. Identify the error in this code snippet:

import numpy as np
a = np.array([1, 2, 3, 4])
np.put(a, [1, 4], [10, 20])
print(a)

medium

A. Syntax error in np.put() usage

B. Values list length does not match indices length

C. Index 4 is out of bounds for array of size 4

D. np.put() cannot modify arrays in-place

Solution

1. Step 1: Check array size and indices

   Array a has size 4, valid indices are 0 to 3. Index 4 is invalid.

2. Step 2: Understand np.put() behavior

   np.put() tries to assign values at given indices. Using index 4 causes an IndexError.

3. Final Answer:

   Index 4 is out of bounds for array of size 4 -> Option C

4. Quick Check:

   Index error due to invalid index [OK]

Hint: Check indices are within array size before np.put() [OK]

Common Mistakes:

• Ignoring array size limits
• Assuming np.put() auto-expands array
• Confusing index and value positions

5. Given a = np.array([1, 2, 3, 4, 5]), how can you use np.put() and np.take() together to replace elements at indices [0, 2] with [10, 20] and then retrieve these updated elements?

hard

A. np.take(a, [10, 20]); np.put(a, [0, 2])

B. np.take(a, [0, 2], [10, 20]); np.put(a, [0, 2])

C. np.put([0, 2], a, [10, 20]); np.take([0, 2], a)

D. np.put(a, [0, 2], [10, 20]); np.take(a, [0, 2])

Solution

1. Step 1: Use np.put() to update elements

   np.put(a, [0, 2], [10, 20]) replaces elements at indices 0 and 2 with 10 and 20.

2. Step 2: Use np.take() to retrieve updated elements

   np.take(a, [0, 2]) returns the updated elements at those indices.

3. Final Answer:

   np.put(a, [0, 2], [10, 20]); np.take(a, [0, 2]) -> Option D

4. Quick Check:

   Put updates, take retrieves updated values [OK]

Hint: Put first to update, then take to get new values [OK]

Common Mistakes:

• Mixing argument order in np.put()
• Using np.take() to update values
• Confusing arrays and indices in functions