{
    "info": {
        "author": "David Yu (KPMG)",
        "author_email": "davidyjun@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "======\nwoe_iv\n======\n\n\n\n\n\n\ncaculate woe(weight of evidence) of each feature and then iv(information value).\n\n\n\nFeatures\n--------\n\n* 1 Calculation of WOE  and IV\n\n    def WOE(cls, data, varList, type0='Con', target_id='y', resfile='result.xlsx'):\n        \"\"\"\n        \u5bf9\u5206\u7c7b\u53d8\u91cf\u76f4\u63a5\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u5e76\u8fdb\u884cWOE\u3001IV\u503c \u8ba1\u7b97\n        \u5bf9\u8fde\u7eed\u578b\u53d8\u91cf\u8fdb\u884c\u5206\u7ec4\uff08default:10\uff09\u540e\u8fdb\u884cWOE\u3001IV\u503c \u8ba1\u7b97\n        :param data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table)\n        :param varList: variable list\n        :param type0: Continuous or Discontinuous(Category), 'con' is the required input for Continuous\n        :param target_id: y flag when gen the train data\n        :param resfile: download path of the result file of WOE and IV\n        :return: pandas DataFrame, result of woe and iv value according y flag\n        \"\"\"\n        pass\n\n* 2 Apply of WOE repalcement of ABT\n\n    def applyWOE(cls, X_data, X_map, var_list, id_cols_list=None, flag_y=None):\n        \"\"\"\n        \u5c06\u6700\u4f18\u5206\u7bb1\u7684\u7ed3\u679cWOE\u503c\u5bf9\u539f\u59cb\u6570\u636e\u8fdb\u884c\u7f16\u7801\n        :param X_data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table)\n        :param X_map: pandas dataframe, map table, result of applying WOE, refer the func woe_iv.WOE\n        :param var_list: variable list\n        :param id_cols_list: some other features not been analysed but wanted like id, adress, etc.\n        :param flag_y: y flag when gen the train data\n        :return: pandas DataFrame, result of bining with y flag\n        \"\"\"\n        pass\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-12-19)\n0.2.0 (2018-12-2l)\n------------------\n\n* First release on PyPI.\n\n\n",
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        "docs_url": null,
        "download_url": "",
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        "home_page": "https://github.com/helloourworld/woe_iv",
        "keywords": "woe_iv",
        "license": "",
        "maintainer": "",
        "maintainer_email": "",
        "name": "woe-iv",
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        "summary": "caculate woe(weight of evidence) of each feature and then iv(information value).",
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