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{
"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": 2544
}
],
"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"
]
}
]
}