This is because Python must constantly convert back and forth between its own object types and the machine’s raw numerical types. Now consider the Cython version of the same code: import cython ...

dtype: the data type of the elements ... This short introduction should get you started in thinking of Python as a viable possibility in "real" numerical computations. The NumPY module provides a very ...

For work bound by Python’s native object types, the speedups won’t be large. But for numerical operations, or any operations not involving Python’s own internals, the gains can be massive.

Python 3.13 ... GIL makes many types of parallelism difficult, such as neural networks and reinforcement learning or scientific and numerical calculations where parallelism using CPU and GPU ...