Structured arrays - use Technical Quotient

2. Understand

How can you access the age field of a structured array?

x['age']
x.age
x.get('age')

Incorrect; get is a dictionary method, not applicable for structured arrays in NumPy.

x['age']()

Incorrect; using () suggests a method call, which is not correct for accessing array fields.

3. Apply

You want to modify the age field of all elements in a structured array to 5. Which code snippet correctly achieves this?

 x.set('age', 5)\nprint(x)

Incorrect; set is not a method used in NumPy for modifying structured array fields.

 x.age = 5\nprint(x)

 x['age'] = 5\nprint(x)

Correct; assigns the value 5 to all elements in the age field of the structured array.

 x['age']().update(5)\nprint(x)

4. Analyze

Given a structured array x used to store pet information, you notice accessing individual fields like x['age'] for filtering pets over 5 years is slower than expected. Considering structured arrays' C-struct-like memory layout, which could explain the poor performance?

Access speed is independent of the data structure's memory layout; slowness is likely due to Python's global interpreter lock.
NumPy structured arrays inherently speed up all operations, including field access and filtering, due to direct memory access.
The C-struct-like layout of structured arrays can lead to non-optimal cache utilization, affecting access speed for operations like filtering.
Structured arrays are optimized for tabular data access in Python, ensuring the fastest possible access speed.

1. Remember

2. Understand

3. Apply

4. Analyze

Leave a ReplyCancel Reply