{"name":"py37-dask","portdir":"python/py-dask","version":"2022.2.0","license":"BSD","platforms":"{darwin any}","epoch":1,"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":["py37-setuptools","py37-wheel","py37-build","py37-installer","clang-16"]},{"type":"lib","ports":["py37-toolz","python37","py37-yaml","py37-cloudpickle","py37-fsspec","py37-packaging","py37-partd"]}],"depends_on":[{"type":"lib","ports":["py37-distributed"]}]}