Solution:Step 1: Understand default type promotionAdding int16 and float32 promotes to float64 by default, increasing memory.Step 2: Convert int16 to float32 before additionBy converting arr_int to float32, adding float32 arrays keeps result as float32, saving memory.Step 3: Check other optionsresul

[Solved] You have two arrays:arr_int = np.array([1000, 2000], dtype=np.int16) arr_float = np.array([0.1, 0.2], dtype=np.... Which code correctly achieves this with type promotion? | NumPy

NumPy - Array Operations

You have two arrays:

arr_int = np.array([1000, 2000], dtype=np.int16)
arr_float = np.array([0.1, 0.2], dtype=np.float32)

You want to add them and keep the result as float32 to save memory. Which code correctly achieves this with type promotion?

Aresult = arr_int.astype(np.float32) + arr_float

Bresult = arr_int + arr_float.astype(np.float64)

Cresult = arr_int + arr_float

Dresult = arr_int.astype(np.int32) + arr_float

Step-by-Step Solution

Solution:

Step 1: Understand default type promotion
Adding int16 and float32 promotes to float64 by default, increasing memory.
Step 2: Convert int16 to float32 before addition
By converting arr_int to float32, adding float32 arrays keeps result as float32, saving memory.
Step 3: Check other options
result = arr_int + arr_float.astype(np.float64) promotes to float64, increasing memory. result = arr_int + arr_float promotes to float64 by default. result = arr_int.astype(np.int32) + arr_float promotes int16 to int32, then adds float32, promoting to float64.
Final Answer:
result = arr_int.astype(np.float32) + arr_float -> Option A
Quick Check:
Cast int to float32 first to keep float32 result [OK]

Quick Trick: Cast int to float32 before adding float32 to keep dtype [OK]

Common Mistakes:

Ignoring default promotion to float64
Casting float to float64 increasing memory
Not casting int before addition

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More NumPy Quizzes

You have two arrays:

Step 1: Understand default type promotion

Step 2: Convert int16 to float32 before addition

Step 3: Check other options

Final Answer:

Quick Check:

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