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        "author": "Urev Oleg",
        "author_email": "urevolegg@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "Description\n===========\n\nRequirements\n------------\n\n* python >= 3.6\n* numpy >= 1.16\n* pandas >= 0.23\n* matplotlib >= 3.02\n* IPython >= 7.2.0\n\n------------\n\nModule, provides the function _view_, which displays general information on the data:\n  - Result of method info()\n  - Result of method describe()\n  - for numeric / categorical variables - The number of nulls in the data (amount and percentage for each column)\n  - grid of histograms for numeric variables\n  - Top-5 of the most frequent categorical variables (for each)\n\nParameters (function view):\n * d - table with data\n * only_numeric - True / False, default: True. True - information output only by numerical variables, False - information output by numerical and categorical variables.\n * full_stats - True / False, default: False. False - output information on numerical variables without interquartile range, data boundaries without outliers, True - complete output with IQR, min and max by IQR and amount of outliers.\n * histograms - True / False, default: True. True - output with building histograms for numerical variables, False - without building histograms\n\nTop-5 elements of categorical variables\n\n The postfix (_name / _count) is assigned to the name of the data column to create top-5 elements table. :\n  - __name_ - category name\n  - __count_ - number of unique elements in this category. If there are less than 5 unique elements, then the values \u200b\u200bin the _count field are filled -1\n\nInstall\n-------\n\t$ pip install data_view\n\nUsage\n-----\n\n    $ python3\n\n    from data_view import *\n\n    d = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])\n\n    view(d, only_numeric=True, histograms=False)\n\n\n\n",
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