Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), how can you use fancy indexing to select elements at positions (0,2), (1,0), and (2,1) and create a new 1D array with these values?

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NumPy - Indexing and Slicing

Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), how can you use fancy indexing to select elements at positions (0,2), (1,0), and (2,1) and create a new 1D array with these values?

Aarr[[0,2,1], [2,1,0]]

Barr[[0,1,2], [2,0,1]]

Carr[[2,1,0], [0,1,2]]

Darr[[0,1,2]][[2,0,1]]

Step-by-Step Solution

Solution:

Step 1: Understand 2D fancy indexing
To select elements at (row, col) positions, use two index arrays: one for rows, one for columns.
Step 2: Match given positions to index arrays
Positions are (0,2), (1,0), (2,1), so rows = [0,1,2], cols = [2,0,1].
Step 3: Use fancy indexing syntax
Use arr[[0,1,2], [2,0,1]] to get elements [3,4,8].
Final Answer:
arr[[0,1,2], [2,0,1]] -> Option B
Quick Check:
Rows and cols arrays match positions [OK]

Quick Trick: Use two index arrays: rows and columns [OK]

Common Mistakes:

Mixing row and column indices order
Using chained indexing instead of fancy indexing
Using wrong index arrays for positions

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Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), how can you use fancy indexing to select elements at positions (0,2), (1,0), and (2,1) and create a new 1D array with these values?

Step 1: Understand 2D fancy indexing

Step 2: Match given positions to index arrays

Step 3: Use fancy indexing syntax

Final Answer:

Quick Check:

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