Eta

def eta(*measurements):
    """General safe implementation to calculate correlation coefficient disregarding NaNs"""
    nanless_measurements = [
        measurement[measurement == measurement]
        for measurement in measurements
    ]

    category_means = np.array([
        np.mean(measurement)
        for measurement in nanless_measurements
    ])

    population_mean = sum(
        np.sum(measurement)
        for measurement in nanless_measurements
    ) / sum(
        np.size(measurement)
        for measurement in nanless_measurements
    )

    summed_local_delta_squares = sum(
        np.sum((measurement - respective_mean) ** 2)
        for measurement, respective_mean in zip(nanless_measurements, category_means)
    )

    summed_global_delta_squares = sum(
        np.sum((measurement - population_mean) ** 2)
        for measurement in nanless_measurements
    )

    return abs(summed_global_delta_squares - summed_local_delta_squares) / summed_global_delta_squares