Indexing on ndarrays - use Technical Quotient

2. Remember

What does an empty tuple index x[()] return when applied to a zero-dimensional array?

An error
A scalar

Correct; an empty tuple index returns a scalar if x is zero-dimensional.

A view
A copy of the array

5. Apply

How should you index a NumPy array a to ensure you receive a scalar when a is zero-dimensional?

import numpy as np\na = np.array(42)\nprint(a[...])

Incorrect; a[...] always returns a view, not necessarily a scalar.

import numpy as np\na = np.array(42)\nprint(a[np.intp])

Incorrect; a[np.intp] is not a valid way to index an array for this purpose.

import numpy as np\na = np.array(42)\nprint(a[()])

Correct; a[()] returns a scalar if a is zero-dimensional.

import numpy as np\na = np.array(42)\nprint(a[0])

Incorrect; a[0] implies a is not zero-dimensional, otherwise, it would raise an error.

6. Apply

In a situation where you have a NumPy array a and you need to ensure that updating it with a sequence of strings does not leave it in a partially updated state, what should you be cautious of?

Avoiding casting errors during assignment.

Incorrect; even if all elements are valid numerical strings, casting errors can still occur if the array's dtype does not match.

Using advanced indexing techniques.
Performing the update in a single operation.
Ensuring all elements in the sequence are valid numerical strings.

7. Analyze

Given the unpredictable nature of the iteration order in advanced indexing assignments, how can the final state of an array be influenced?

It's entirely predictable and not influenced by the iteration order.
Only by the data type of the array elements.
By using a for-loop to iterate over elements.
By the sequence in which elements are updated.

1. Remember

2. Remember

3. Understand

4. Understand

5. Apply

6. Apply

7. Analyze

Leave a ReplyCancel Reply