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gnu: Add python-modin.
* gnu/packages/python-science.scm (python-modin): New variable.
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@ -916,3 +916,68 @@ (define-public python-distributed
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@code{dask} APIs to moderate sized clusters.")
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(license license:bsd-3)))
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(define-public python-modin
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(package
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(name "python-modin")
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(version "0.10.1")
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(source
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(origin
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;; The archive on pypi does not include all required files.
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(method git-fetch)
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(uri (git-reference
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(url "https://github.com/modin-project/modin")
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(commit version)))
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(file-name (git-file-name name version))
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(sha256
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(base32
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"128ghfb9ncmnn8km409xjcdppvn9nr9jqw8rkvsfavh7wnwlk509"))))
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(build-system python-build-system)
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(arguments
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`(#:phases
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(modify-phases %standard-phases
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(add-after 'unpack 'make-files-writable
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(lambda _
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(for-each make-file-writable (find-files "."))))
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(replace 'check
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(lambda* (#:key tests? #:allow-other-keys)
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(when tests?
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(setenv "MODIN_ENGINE" "dask")
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(invoke "python" "-m" "pytest"
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"modin/pandas/test/test_concat.py")
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(setenv "MODIN_ENGINE" "python")
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(invoke "python" "-m" "pytest"
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"modin/pandas/test/test_concat.py")))))))
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(propagated-inputs
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`(("python-cloudpickle" ,python-cloudpickle)
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("python-dask" ,python-dask)
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("python-distributed" ,python-distributed)
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("python-numpy" ,python-numpy)
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("python-packaging" ,python-packaging)
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("python-pandas" ,python-pandas)))
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(native-inputs
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`(("python-coverage" ,python-coverage)
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("python-jinja2" ,python-jinja2)
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("python-lxml" ,python-lxml)
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("python-matplotlib" ,python-matplotlib)
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("python-msgpack" ,python-msgpack)
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("python-openpyxl" ,python-openpyxl)
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("python-psutil" ,python-psutil)
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("python-pyarrow" ,python-pyarrow)
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("python-pytest" ,python-pytest)
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("python-pytest-benchmark" ,python-pytest-benchmark)
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("python-pytest-cov" ,python-pytest-cov)
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("python-pytest-xdist" ,python-pytest-xdist)
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("python-scipy" ,python-scipy)
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("python-sqlalchemy" ,python-sqlalchemy)
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("python-tables" ,python-tables)
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("python-tqdm" ,python-tqdm)
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("python-xarray" ,python-xarray)
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("python-xlrd" ,python-xlrd)))
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(home-page "https://github.com/modin-project/modin")
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(synopsis "Make your pandas code run faster")
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(description
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"Modin uses Ray or Dask to provide an effortless way to speed up your
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pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame
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libraries, Modin provides seamless integration and compatibility with existing
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pandas code.")
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(license license:asl2.0)))
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