# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4 PortSystem 1.0 PortGroup python 1.0 name py-dask version 2025.9.1 revision 0 categories-append devel license BSD supported_archs noarch platforms {darwin any} python.versions 39 310 311 312 313 maintainers {stromnov @stromnov} openmaintainer description Minimal task scheduling abstraction. long_description 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. homepage https://github.com/dask/dask/ checksums rmd160 252a7664bcd768be0241f999906dda2b3120a1b0 \ sha256 718df73e1fd3d7e2b8546e0f04ce08e1ed7f9aa3da1eecd0c1f44c8b6d52f7e0 \ size 10973663 if {${name} ne ${subport}} { if {${python.version} == 39} { version 2024.8.0 revision 0 checksums rmd160 e34d1bee80f224d40f154576d20abb795a7cff0c \ sha256 f1fec39373d2f101bc045529ad4e9b30e34e6eb33b7aa0fa7073aec7b1bf9eee \ size 9895684 } depends_build-append \ port:py${python.version}-versioneer depends_lib-append port:py${python.version}-click \ port:py${python.version}-cloudpickle \ port:py${python.version}-fsspec \ port:py${python.version}-packaging \ port:py${python.version}-partd \ port:py${python.version}-toolz \ port:py${python.version}-yaml if {${python.version} < 312} { depends_lib-append port:py${python.version}-importlib-metadata } livecheck.type none }