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{
    "name": "py38-dask",
    "portdir": "python/py-dask",
    "version": "2022.12.0",
    "license": "BSD",
    "platforms": "darwin",
    "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": true,
    "categories": [
        "devel",
        "python"
    ],
    "maintainers": [
        {
            "name": "stromnov",
            "github": "stromnov",
            "ports_count": 2076
        }
    ],
    "variants": [],
    "dependencies": [
        {
            "type": "build",
            "ports": [
                "py38-setuptools",
                "py38-wheel",
                "py38-build",
                "clang-14",
                "py38-installer"
            ]
        },
        {
            "type": "lib",
            "ports": [
                "py38-toolz",
                "python38",
                "py38-yaml",
                "py38-cloudpickle",
                "py38-fsspec",
                "py38-packaging",
                "py38-partd"
            ]
        }
    ],
    "depends_on": [
        {
            "type": "lib",
            "ports": [
                "py38-distributed"
            ]
        },
        {
            "type": "test",
            "ports": [
                "py38-sparse"
            ]
        }
    ]
}