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    "info": {
        "author": "Guillaume Calmettes",
        "author_email": "gcalmettes@g.ucla.edu",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# `gcGroupbyExtension`: chain `.apply` methods on Pandas groupby object\n\n## Install\nThe extension is available on [PyPi](https://pypi.org/project/gcGroupbyExtension-gcalmettes/)\n```\npip install gcGroupbyExtension-gcalmettes\n```\n\n(Or if you do not want to install the package in your python distribution, just download this repo and place the `gcGroupbyExtension` folder in the folder you're running your python script/notebook in.)\n\n## Import\nOnce installed, the extension can be imported via:\n```\nimport gcGroupbyExtension\n```\n\n## What problems does this extension try to solve?\n[Pandas](https://pandas.pydata.org) provides both the [`.pipe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.pipe.html) and [`.apply`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.apply.html) methods to work on its [groupby](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) object.\nThe main difference between `.pipe` and `.apply` in the groupby context is that you have access to the entire scope of the groupby object (each group) with `.pipe`, while you only have access to the subcomponents scope (in the context of a groupby the subcomponents are slices of the dataframe that called groupby where each slice is a dataframe itself. This is analogous for a series groupby.)\n1) The `.pipe` method can be chained, while the `.apply` method can't.\n2) You can use the [`.agg`](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.core.groupby.DataFrameGroupBy.agg.html) method to limit the application of the functions on particular columns of the groups, but it is cumbersome to apply specific functions independantly on only a selection of the groups.\n3) There is no easy way to construct independant pipelines of functions for each group.\n\nThis extension provides this capability.\n\n## What does this extension actually do?\nThis extension allows to construct a pipeline of functions to be applied independently on the groups of a groupby object. The functions/transformations to be applied can be the same for all the groups or scoped to (a) specific group(s).\n\n**Details:**\nThis library registers a [custom accessor](https://pandas.pydata.org/pandas-docs/stable/development/extending.html) on pandas DataFrame and Series objects.\nThe methods of this extension are registered under the `gc` namespace.\n\nSee the [DEMO notebook](https://github.com/gcalmettes/pandas-groupby-apply-chaining-extension/blob/master/demo.ipynb) for details. \n\n## Care to show the syntax?\nSure! See the [DEMO notebook](https://github.com/gcalmettes/pandas-groupby-apply-chaining-extension/blob/master/demo.ipynb) for more details, but basically, you can do things like this:\n\n```\n(df.gc.groupby(\"nameOfColumn\")\n    .resetIndex() # this is a special method baked in\n    .apply(lambda x: x * 5, lambda x: x + x.iloc[3]) # accepts multiple functions\n    .apply(mySpecialFunction, onlyGroups=['group1']) # limit the function to specific group(s)\n    .apply(lambda x: x - x.mean(), ignoreGroups=['group4', 'group6']) # limit the function to specific group(s)\n    .apply(lambda x: x.std(axis=1))\n    .concat(axis=0, multiIndex=None).plot()\n)\n```\n\n\n",
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