Mistake in Your Approach
Your approach:
python corr = np.corrcoef(a.T, b.T).diagonal(a.shape[1])

What Went Wrong?

1. Incorrect Use of 'np.corrcoef' -'np.corrcoef(a.T, b.T)' computes the correlation matrix for column-wise comparisons, not row-wise.
   -Since '.T' transposes the matrix, it correlates columns rather than rows.

2. Wrong diagonal() Usage
   -diagonal(a.shape[1]) is incorrect.
   -diagonal() extracts elements from the main diagonal, but in this case, the relevant values are not located where expected.

Code With Correct approach: python

import numpy as np from scipy.stats import pearsonr

-Define the arrays a = np.array([[1, 2, 4], [3, 6, 2], [3, 4, 7], [9, 7, 7], [6, 3, 1], [3, 5, 9]]) b = np.array([[4, 5, 2], [9, 2, 5], [1, 5, 6], [4, 5, 6], [1, 2, 6], [6, 4, 3]])

-Compute correlation for each row correlation_values = np.array([pearsonr(row_a, row_b)[0] for row_a, row_b in zip(a, b)])

-Display results print(correlation_values)

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