[[11 22] [13 24]] because b is broadcasted to each row of a

[[10 20] [10 20]] because b replaces a

[[1 2] [3 4]] because addition is not possible

Solution:Step 1: Analyze shapes of arraysa is shape (2,2), b is shape (2,). Numpy broadcasts b across rows of a.Step 2: Perform element-wise addition with broadcastingAdd b to each row of a: first row [1+10, 2+20], second row [3+10, 4+20].Final Answer:[[11 22] [13 24]] -> Option AQuick Check:Broadc

[Solved] Given two arrays:a = np.array([[1, 2], [3, 4]]) b = np.array([10, 20])What is the result of a + b and why? | NumPy

NumPy - Array Operations

Given two arrays:

a = np.array([[1, 2], [3, 4]])
b = np.array([10, 20])

What is the result of a + b and why?

A

<code>[[11 22]
 [13 24]]</code> because <code>b</code> is broadcasted to each row of <code>a</code>

B

<code>[[10 20]
 [10 20]]</code> because <code>b</code> replaces <code>a</code>

C

<code>[[1 2]
 [3 4]]</code> because addition is not possible

DError due to shape mismatch

Step-by-Step Solution

Solution:

Step 1: Analyze shapes of arrays
a is shape (2,2), b is shape (2,). Numpy broadcasts b across rows of a.
Step 2: Perform element-wise addition with broadcasting
Add b to each row of a: first row [1+10, 2+20], second row [3+10, 4+20].
Final Answer:
[[11 22] [13 24]] -> Option A
Quick Check:
Broadcasting adds b to each row of a [OK]

Quick Trick: Broadcast smaller array to match larger shape for element-wise ops [OK]

Common Mistakes:

Expecting error due to different shapes
Thinking b replaces a instead of broadcasting
Ignoring numpy broadcasting rules

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Given two arrays:

Step 1: Analyze shapes of arrays

Step 2: Perform element-wise addition with broadcasting

Final Answer:

Quick Check:

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