Broadcasting - use Technical Quotient

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Broadcasting - use Technical Quotient

NumPy

Broadcasting

2. Understand

How does broadcasting improve the efficiency of NumPy operations compared to operations involving only arrays of identical shapes?

By reduction in memory and computation by avoiding manual data replication.
By increasing the size of arrays to match each other.
By converting all operations to scalar operations.
By creating copies of the smaller array to match the larger array's shape.

3. Apply

Consider arrays a = np.array([[0, 1, 2], [3, 4, 5]]) and scalar b = 4. You want to multiply each element of a by b using broadcasting. Which of the following code snippets correctly applies broadcasting?

import numpy as np\na = np.array([[0, 1, 2], [3, 4, 5]])\nb = 4\nresult = a * b

import numpy as np\na = np.array([[0, 1, 2], [3, 4, 5]])\nb = np.array([4, 4, 4])\nresult = a * b

Incorrect; while it technically works, this method does not utilize broadcasting since b is manually made into an array of the same shape as a, which isn't the most efficient method.

import numpy as np\na = np.array([[0, 1, 2], [3, 4, 5]])\nb = 4\nb = np.tile(b, (2, 3))\nresult = a * b

Incorrect; uses np.tile to manually replicate b to match the shape of a, which is less efficient than broadcasting.

import numpy as np\na = np.array([[0, 1, 2], [3, 4, 5]])\nb = np.array([4])\nresult = a * b

Incorrect; demonstrates that wrapping the scalar in an array doesn't leverage broadcasting's full efficiency, as broadcasting with a scalar (b = 4) is more straightforward and memory-efficient.

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1. Remember

2. Understand

3. Apply

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