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        "author": "Ethan Robbins",
        "author_email": "ethan.d.robbins@gmail.com",
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        "description": "# easystyle\n\neasystyle is a python package created to allow simple and quick data analysis without having to export to excel. easystyle reads in the data as a dataframe as highlights certain cells depending on what the user calls. The package is heavily dependent on the styling in pandas and pulls some basic code from the Stying pandas documentation. Functions such as identifying, by highlighting the cell or text, the maximum, the minimum, outliers, and NaN values are used in easystyle. These functions allow the user to take a quick look at their data in Jupyter Notebook. It also serves the purpose of showing the data cleaner than with a standard dataframe so that you can present the information to others without having to go to excel.\n\neasystyle works great in Jupyter Notebook, but I am unsure of how well, or if it will work at all, in other environments\n\n\n## Installing easystyle\n\n    pip install easystyle\n\n## Importing easystyle\n\nThe best way to import easystyle is:\n\n    from easystyle import Style as es\n\nThis is since it makes it simpler to call functions when using the package. For the following examples we will assume this importing method.\n\n## Highlight Negative Values\n\n    def highlight_negative(df, col = 'red'):\n\nIn order to use highlight_negative, the user must provide the dataframe. The user does not need to provide a color, but may do so if they want a color other than red. To run the function:\n\n    df = es.highlight_negative(df, color)\n\n## Highlight Positive Values\n\n    def highlight_positive(df, col = 'green'):\n\nThis function follows similarly to the function above. To run the function:\n\n    df = es.highlight_positive(df, color)\n\n## Highlight Negative and Positive Values\n\n    def highlight_neg_pos(df, colNeg = 'red', colPos = 'green'):\n\nFollows from above. To run the function:\n\n    df = es.highlight_neg_pos(df, color_neg, color_pos)\n\n## Highlight Maximum Value\n\n    def highlight_max(df, column_names, col='yellow'):\n\nThe function is fed an additional variable, the column names. `column_names` is an array of the column names that you wish to find the maximum in. For example:\n\n    column_names = ['A', 'B', 'C']\n    df = es.highlight_max(df, column_names, color)\n\n## Highlight Minimum Value\n\n    def highlight_min(df, column_names, col='orange'):\n\nFollows similarly to highlighting a maximum value. For example:\n\n    column_names = ['A', 'B', 'C']\n    df = es.highlight_min(df, column_names, color)\n\n## Highlight Outliers\n\n    def highlight_outlier(df, column_names, colLow='red', colUp='red'):\n\nThis function uses the IQR method in order to find outliers. Again, `column_names` is a array of column names. For example:\n\n    df = es.highlight_outlier(df, column_names, colorLower, colorUpper)\n\n## Highlight NaN Values\n\n    def highlight_NaN(df, color = 'red'):\n\nIf any NaN values are detected, they are highlighted. For example:\n\n    df = es.highlight_NaN(df, color)\n\n## Give a Gradient\n\n    def highlight_gradient(df, color = 'green'):\n\nThis function uses the gradients provided by the Seaborn package. For example:\n\n    df = es.highlight_gradient(df, color)",
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