Given a numpy array arr = np.arange(24), which reshape command correctly reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation?

hard📝 Application Q15 of 15

NumPy - Array Manipulation

Given a numpy array arr = np.arange(24), which reshape command correctly reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation?

Aarr.reshape(2, 3, 4)

Barr.reshape(-1, 3, 4)

Carr.reshape(3, -1, 4)

Darr.reshape(2, 4, -1)

Step-by-Step Solution

Solution:

Step 1: Understand array size and target shape
arr has 24 elements. Target shape is (2, 3, 4) which multiplies to 24.
Step 2: Use -1 to let numpy calculate dimension
Using -1 lets numpy infer that dimension. arr.reshape(-1, 3, 4) uses -1 in first dimension, so shape becomes (2,3,4) automatically.
Step 3: Check other options
arr.reshape(2, 3, 4) works but does not use automatic calculation. arr.reshape(3, -1, 4) results in (3, 2, 4). arr.reshape(2, 4, -1) results in (2, 4, 3).
Final Answer:
arr.reshape(-1, 3, 4) -> Option B
Quick Check:
Use -1 for auto dimension: reshape(-1, 3, 4) [OK]

Quick Trick: Use -1 where you want numpy to infer dimension [OK]

Common Mistakes:

Placing -1 in wrong dimension causing errors
Not matching total elements after reshape
Assuming multiple -1s allowed in reshape

Master "Array Manipulation" in NumPy

9 interactive learning modes - each teaches the same concept differently

Learn Why Deep Visual Try Challenge Project Recall Time

Want More Practice?

15+ quiz questions · All difficulty levels · Free

Free Signup - Practice All Questions

More NumPy Quizzes

Given a numpy array arr = np.arange(24), which reshape command correctly reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation?

Step 1: Understand array size and target shape

Step 2: Use -1 to let numpy calculate dimension

Step 3: Check other options

Final Answer:

Quick Check:

Want More Practice?