Dask provides multi-core execution on larger-than-memory datasets using blocked algorithms and task scheduling. It maps high-level NumPy, Pandas, and list operations on large datasets on to many operations on small in-memory datasets. It then executes these graphs in parallel on a single machine. Dask lets us use traditional NumPy, Pandas, and list programming while operating on inconveniently large data in a small amount of space.
Dask provides multi-core execution on larger-than-memory datasets using blocked algorithms and task scheduling. It maps high-level NumPy, Pandas, and list operations on large datasets on to many operations on small in-memory datasets. It then executes these graphs in parallel on a single machine. Dask lets us use traditional NumPy, Pandas, and list programming while operating on inconveniently large data in a small amount of space.
To install py39-dask, run the following command in macOS terminal (Applications->Utilities->Terminal)
sudo port install py39-dask
To see what files were installed by py39-dask, run:
port contents py39-dask
To later upgrade py39-dask, run:
sudo port selfupdate && sudo port upgrade py39-dask
Reporting an issue on MacPorts Trac
The MacPorts Project uses a system called Trac to file tickets to report bugs and enhancement requests.
Though anyone may search Trac for tickets, you must have a GitHub account in order to login to Trac to create tickets.