Python, the versatile and programmer-friendly language, has long been hamstrung by its single-threaded implementation, CPython. This limitation has hindered its ability to fully utilize multiple CPUs or compute clusters, making it less than ideal for heavy workloads. However, a slew of innovative frameworks has emerged to distribute Python workloads across multiple cores, machines, or both, unlocking unparalleled processing power.

From machine learning to data science, these seven frameworks – Ray, Dask, Dispy, Pandaral·lel, Ipyparallel, Joblib, and Parsl – offer a range of solutions to parallelize and distribute Python tasks. Each framework boasts unique strengths, whether it's Ray's minimal syntax, Dask's centralized scheduling, or Parsl's multi-step workflow capabilities.

With these frameworks, developers can now:

• Scale machine learning and data science workloads with ease

• * Distribute tasks across multiple machines or cores

• * Achieve unparalleled processing power and efficiency

• * Overcome Python's single-threaded limitations

In a nutshell, these frameworks are revolutionizing the way Python is used, empowering developers to tackle complex tasks with unprecedented speed and agility.