The best Python libraries for parallel processing

The post introduces seven Python libraries that help distribute a heavy workload across multiple CPUs or compute clusters, addressing Python's single-threaded limitations. Libraries discussed include Ray, Dask, Dispy, Pandar·lel, Ipyparallel, Joblib, and Parsl, each catering to different needs such as machine learning, data science, and general parallel processing tasks. Highlights include Ray's minimal syntax and cluster management, Dask's centralized scheduler and actor model, and Joblib's efficient disk caching and parallelization capabilities.