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        "author": "Winn Y Cui",
        "author_email": "winn.yc@berkeley.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "If you have hundreds of csvs you need to quickly digest and understand,\nyou can use crystal-ball to help with the onboarding and data\nexploration phase of your project. You will be able to immediately dive\ninto an expansive data set without getting lost.\n\nCrystalBall Features\n====================\n\n-   find specific columns and tables you may be interested in, but may\n    have otherwise not known where to look.\n\n-   walk through connections between different csvs,\n\n-   compare and establish foreign key and primary key relationships by\n    using simple boxplots\n\n-   dynamically create a master table of useful information while\n    simultaneously recording your step-by-step process for future\n    reference.\n\nInstallation and Usage\n======================\n\npip install crystal-ball</programlisting>\nYou can start using CrystalBall right away by importing it and\ninitializing it with a relative directory containing the CSVs.\n\n    import crystalball as cb\n\n    ball = cb.run(\"insert relative directory here\")\n\nMethods\n=======\n\nNote that all methods that involve searching via keynames are case\nsensitive.\n\ncb.contains(keywords: list, all\\_colnames: list=None) \u2192 list\n------------------------------------------------------------\n\nCheck if keywords exist in all\\_colnames.\n\ncb.featureSearch(keywords: list, all\\_colnames: list=None) \u2192 list\n-----------------------------------------------------------------\n\nFind the columns that contain the keywords.\n\ncb.tableSearch(keywords: list, csvname\\_to\\_colnames\\_list=None, mode: str=*UNION*) \u2192 list\n------------------------------------------------------------------------------------------\n\nFind the tables that contain the keywords.\n\ncb.openTable(rel\\_dir: str, indices: list=\\[0\\]) \u2192 DataFrame\n------------------------------------------------------------\n\nOpen the csv that is referenced by the given relative directory.\n\ncb.subTable(supertable: DataFrame, chosen\\_index: list, chosen\\_columns: list) \u2192 DataFrame\n------------------------------------------------------------------------------------------\n\nCreate a subtable from a supertable.\n\ncb.mergeTables(tables: list) \u2192 DataFrame\n----------------------------------------\n\nSequentially merge a list of tables that all share a common index.\n\ncb.analyzeRelationships(to\\_analyze: list, visualize: bool=True) \u2192 DataFrame\n----------------------------------------------------------------------------\n\nAnalyze basic stats of one or more different Series.\n\ncompareRelationship(to\\_analyze1: Series, to\\_analyze2: Series, visualize: bool=False) \u2192 DataFrame\n--------------------------------------------------------------------------------------------------\n\nCompare and contrast the difference between two Series.\n\ncb.export(df\\_to\\_export: DataFrame, write\\_to: str, export\\_type: str=*CSV*) \u2192 None\n------------------------------------------------------------------------------------\n\nExports contents of dataframe to relative location specified by\nwrite\\_to arg.\n\n\n",
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