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        "author_email": "jasc@gmx.net",
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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
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        "description": "=====================\nPython data pipelines\n=====================\n\n\n.. image:: https://img.shields.io/pypi/v/pydatapipes.svg\n        :target: https://pypi.python.org/pypi/pydatapipes\n\n.. image:: https://img.shields.io/travis/janschulz/pydatapipes.svg\n        :target: https://travis-ci.org/janschulz/pydatapipes\n\n.. image:: https://readthedocs.org/projects/pydatapipes/badge/?version=latest\n        :target: https://pydatapipes.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://pyup.io/repos/github/janschulz/pydatapipes/shield.svg\n     :target: https://pyup.io/repos/github/janschulz/pydatapipes/\n     :alt: Updates\n\n\nFeatures\n--------\n\nThis package implements the basics for building pipelines similar to magrittr in R. Pipelines are\ncreated using ``>>``. Internally it uses singledispatch_ to provide a way for a unified API\nfor different kinds of inputs (SQL databases, HDF, simple dicts, ...).\n\nBasic example what can be build with this package:\n\n.. code-block:: python\n\n    >>> from my_library import append_col\n    >>> import pandas as pd\n\n    >>> pd.DataFrame({\"a\" : [1,2,3]}) >> append_col(x=3)\n       a  X\n    0  1  3\n    1  2  3\n    2  3  3\n\nIn the future, this package might also implement the verbs from the R packages dplyr_ and\ntidyr_ for ``pandas.DataFrame`` and  or I will fold this into one of the other available\nimplementation of dplyr_ style pipelines/verbs for pandas.\n\n\nDocumentation\n-------------\n\nThe documentaiton can be found on ReadTheDocs_: https://pydatapipes.readthedocs.io\n\nLicense\n-------\n\nFree software: MIT license\n\nCredits\n---------\n\n* magrittr_ and it's usage in dplyr_ / tidyr_ for the idea of using pipelines in that ways\n* lots of python implementations of dplyr style pipelines: dplython_, pandas_ply_, dfply_\n\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _ReadTheDocs: https://readthedocs.org/\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n.. _magrittr: https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html\n.. _dplyr: https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html\n.. _tidyr: https://cran.r-project.org/web/packages/tidyr/index.html\n.. _singledispatch: https://docs.python.org/3/library/functools.html#functools.singledispatch\n.. _dplython: https://github.com/dodger487/dplython\n.. _pandas_ply: https://github.com/coursera/pandas-ply\n.. _dfply: https://github.com/kieferk/dfply\n\n\n=======\nHistory\n=======\n\n0.1.0 (2016-10-22)\n------------------\n\n* First release on PyPI.\n\n\n",
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