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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def euclidean_dissim(a, b, **_):
 """Euclidean distance dissimilarity function"""

    if np.isnan(a).any() or np.isnan(b).any():
        raise ValueError("Missing values detected in numerical columns.")
    return np.sum((a - b) ** 2, axis=1)