In Python data visualization, how do I parallelize workloads?

In Python data visualization, you can parallelize workloads to improve performance and efficiency by utilizing libraries like `multiprocessing`, `joblib`, or `concurrent.futures`. This allows you to take advantage of multi-core processors for executing multiple data visualization tasks simultaneously.


        from multiprocessing import Pool
        import matplotlib.pyplot as plt

        def plot_data(data):
            plt.figure()
            plt.plot(data)
            plt.title(f'Plot for {data}')
            plt.show()

        if __name__ == "__main__":
            data_sets = [range(10), range(10, 20), range(20, 30)]
            with Pool(processes=3) as pool:
                pool.map(plot_data, data_sets)