That is how to effectively create arrays in Python. However, there are other options for arrays too. One example is to create a CSV file, which you can learn to do in our quick guide .

The best parallel processing libraries for Python. Ray: Parallelizes and distributes AI and machine learning workloads across CPUs, machines, and GPUs.; Dask: Parallelizes Python data science ...

It is possible to use generic Python objects as the dtype for a NumPy array, but if you do this, you’ll get no better performance with NumPy than you would with Python generally.

Pierre Glaser from INRIA gave this talk at EuroPython 2019. "Modern hardware is multi-core. It is crucial for Python to provide high-performance parallelism. This talk will expose to both ...