{"name":"py38-dask","portdir":"python/py-dask","version":"2023.6.1","license":"BSD","platforms":"{darwin any}","epoch":0,"replaced_by":null,"homepage":"https://github.com/dask/dask/","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.","active":false,"categories":["devel","python"],"maintainers":[{"name":"stromnov","github":"stromnov","ports_count":2891}],"variants":[],"dependencies":[{"type":"build","ports":["py38-setuptools","py38-wheel","py38-build","py38-installer","py38-versioneer","clang-16"]},{"type":"lib","ports":["py38-partd","python38","py38-yaml","py38-toolz","py38-click","py38-cloudpickle","py38-fsspec","py38-importlib-metadata","py38-packaging"]}],"depends_on":[{"type":"lib","ports":["py38-distributed"]},{"type":"test","ports":["py38-sparse"]}]}