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    "info": {
        "author": "Nicholas Lawrence",
        "author_email": "nickclawrence@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "\n==================================================================\n``pandas-refract``: Convenient dataframe fragmentation functions\n==================================================================\n\n**pandas-refract** is an MIT licensed Python package with a simple function that allows users to divide their \ndataframes by the 'Truthy' and 'Falseyness' of a provided array.\n\nBecause this library deals with splitting dataframes by its values, it uses imagery of light - refract and disperse, like light entering a prism.\nEventually, the goal of this package is additional features to the Pandas library that allows users to .pop rows\nfrom a dataframe where a condition is met, etc. As far as I can tell this is not possible like the below example.\n\nIdeal case would be::\n\n    target_df = df.pop(df['target_column'] == 'target_value')\n    non_target_df = df\n\nWhat is required now is::\n\n    target_df = df[df['target_column'] == 'target_value'] \n    non_target_df = df[df['target_column'] != 'target_value']\n\nWith pandas-refract this becomes::\n\n    target_df, non_target_df = refract(df, df['target_column'] == 'target_value']]\n\n\nObviously, this package is not providing anything not currently possible in the current Pandas library. It does,\nhowever, add a layer of convenience for more complex slicing where you need to separate, not remove, rows by conditions.\n\n\nExamples\n========\n\nSimplest example of current Pandas requires::\n\n    df1 = df[df.column.notnull()].reset_index(drop=True)\n    df2 = df[df.column.isnull()].reset_index(drop=True)\n\nor::\n\n    df1 = df[df.column == 'test_string'].reset_index(drop=True)\n    df2 = df[df.column != 'test_string'].reset_index(drop=True)\n\n\nWith pandas-refract this becomes::\n\n    df1, df2 = refract(df, df.column.notnull(), True]\n\nand::\n\n    df1, df2 = refract(df, df.column == test_string', True]   \n\n\nBut you don't have to pass it explicit boolean arrays::\n\n    data = {'a': ['', 'truthy', '', 'truthy'],\n            'b': [0, 1, 2, 3]\n            }\n\n    df = pd.DataFrame(data)\n\n    truthy_df, falsey_df = refract(df, df.a)\n\n\nMore complex examples:\n*(where 'a' is Falsey and 'b' is an odd number)*\n::\n\n    df1, df2 = refract(df, ((~df.a) & (df.b % 2 == 1)))\n\n\n\n",
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        "license": "MIT",
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        "name": "pandas-refract",
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